A Practical Interference-Aware Power ManagementProtocol for Dense Wireless Networks

Paper 21, 14 pages

ABSTRACTThe availability of spectrum resources has not kept pace withwireless network popularity. As a result, data transfer perfor-mance is often limited by the number of devices interferingon the same frequency channel within an area. In this pa-per, we introduce a protocol that manages the transmissionpower and CCA threshold of 802.11 devices to maximizenetwork performance. The protocol is based on the obser-vation that for a pair of interfering wireless links, it is pos-sible to calculate the ratio of the transmit power of the twosenders that maximizes overall network capacity. We firstpresent an algorithm that extends this result for dense clus-ters of nodes and then describe a distributed protocol that im-plements the transmission power setting algorithm and CCAtuning mechanism. Finally, we describe an implementationof the project in Linux. It uses commercial 802.11 cards andaddressed several practical challenges such as protocol sta-bility and calibration. Our experimental evaluation using an8 node testbed shows that our protocol works well in prac-tice and can improve the performance over default configu-rations by more than 200%. We also use OPNET simulationsto evaluate larger topologies with different node densities.

1. INTRODUCTIONThe popularity of wireless is fueling a rapid growth

in the number of devices using the unlicensed frequencybands. Resulting dense wireless deployments in hotspots,residential neighborhoods, and campuses increasinglysuffer from poor performance due to interference be-tween the large number of devices sharing the samefrequency band. This problem is exacerbated by thefact that the default transmit power and carrier sensethreshold (aka Clear Channel Assesment or CCA thresh-old) in 802.11 devices results in inefficient use of thespectrum. In this paper, we describe the design andimplementation of a distributed protocol for selectingthe transmit power and CCA threshold so as to reduceinterference and increase network capacity, while main-taining fairness.

Several projects [21][17][1][15][7] have explored ad-justing transmit power and/or the CCA treshold to im-prove performance. For example, [1] proposed to use

the minimum power level necessary to reach a receiver.However, this approach can increase unfairness sincenodes closer to their destination may experience higherinterference. In fact, several authors [19][17][1] havepointed out that systems that do not tune the CCAthreshold along with the transmit power often reducefairness since low power stations are less likely to beheard. More recent work [17] performs joint transmis-sion power and CCA threshold tuning, but the proposedprotocol uses the same transmit power level and CCAthreshold for all nodes in a cell, thus limiting opportu-nities for spatial reuse. Also, practical challenges suchas calibration are not addressed in earlier work.

In this paper, we first present an iterative greedy al-gorithm that uses perfect knowledge about the RF envi-ronment to determine the transmit power for all trans-missions. The basic idea of the algorithm is to itera-tively adjust the transmit power of individual links sothat the number of edges in the transmission conflictgraph [18] is reduced. We then describe a practical,distributed version of the power control algorithm for802.11 nodes. In the protocol, each node builds a localtopology graph by overhearing packets from its neigh-bors. It uses the RSSI of the incoming packets andtransmission information stored in the packets. Eachnode then uses its local topology graph to optimizeboth the transmit power to each neighbor and to ad-just its CCA threshold using an altruistic version of theEchos [21] algorithm.

Next, we describe an implementation of the proto-col that addresses several practical challenges, includinghow to stablize the protocol, how to calibrate both thetransmit power and RSSI readings of the cards, etc. Wepresent an evaluation of the protocol using both testbedmeasurements and simulation, focusing on infrastruc-ture 802.11 networks. Measurements in an eight nodetestbed show throughput gains of more than 200%. Us-ing OPNET simulation, we study larger topologies andthe impact of node density on performance. While thealgorithm optimizes capacity for any type of wirelesslink and the implementation address many practical is-sues, we should note that we do not consider possible

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interactions with transmit rate selection, AP selectionin infrastructure networks, or multi-hop routing and op-portunistic relaying in mesh networks in this paper.

The rest of the paper is organized as follows. We de-scribe our interference model in Section 2. Section 3discusses our greedy centralized algorithm for powercontrol, extensions for CCA tuning, and power reallo-cation for further increasing spatial reuse. In Section 4,we present the distributed power management proto-col. We discuss our implementation and an evaluationusing a testbed and OPNET in Sections 5 through 7.We review related work in Section 8 and summarize ourconclusions in Section 9.

2. MODELING INTERFERENCEIn this section, we first review two models that are

widely used in the wireless community to identify whenconcurrent transmissions can occur. We then introduceour interference model and the conflict graph.

2.1 Concurrent Transmission ModelThe physical SINR model [9][18] predicts that a

packet can be successfully decoded if the signal to in-terference plus noise ratio exceeds some threshold. Ittakes into account received signal strength from thesource and all sources of interference and noise. TheSINR model predicts that uniformly increasing powerlevels increases system throughput by reducing the ef-fects of thermal noise, though such gains are marginalin interference-dominated networks, e.g. dense 802.11networks.

The circle model is a much simpler model that isused implicitly in many papers [14][1]. In this model,every source is associated with a transmission and aninterference range. Nodes within the transmission rangeof a source can decode frames from the source, andnodes within interference range will be prevented fromtransmitting due to carrier sense [3]. The ranges de-pend on the power level each source uses. The circlemodel predicts that using the minimum possible powerlevel minimizes interference – exactly the opposite con-clusion of the SINR model!

The right choice of model depends on whether theinterface hardware exhibits the capture effect [22], i.e.the ability to decode one transmission when multipletransmissions collide. In particular, it depends on howan incoming transmission is affected by a later framewith a higher signal strength. If the interface capturesthe stronger signal, then the SINR model is more ac-curate – otherwise the circle model is a better predic-tor. To determine the behavior of modern 802.11 hard-ware, we performed an experiment in a wireless emu-lator [12]. The experiment uses four laptops equippedwith Atheros 5212 cards. We use netperf [11] to cre-ate two interfering non-rate controlled UDP flows, a

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Received Signal Strength for Ft (dBm)

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Figure 1: Packet Capture With Interfering Sig-nals for Atheros 5212 cards

targer flow Ft and an interfering flow Fi, with transmitpower levels Pt and Pi at the target receiver, respec-tively. Also, we use the emulator to prevent any inter-ference at both sources, thus eliminating the effects ofcarrier sense and collisions with link-layer ACKs.

Figure 1 shows the throughput achieved by Ft as afunction of Pt. Each curve is for a constant interfer-ence level Pi and we changed Pi from -100dBm (lowerthan the noise level) to -60dBm (high enough to bedecoded) in increments of 4dB. If the hardware doesnot exhibit the capture effect, the throughput of Ft forhigh values of Pi should be half of maximum through-put. However, Figure 1 shows that except for signalstrengths close to the noise floor, all curves have thesame shape and are equally spaced. Also, the interfer-ing signal (equally spaced curves) has a similar effectas noise (leftmost curve). These features are consistentwith a strong capture effect, showing that the circlemodel is not representative for this hardware. Similarobservations were made in [16], which presents a timinganalysis of the capture effects. For Intel cards, captureeffect is supported by setting the CCA threshold to arelatively high value [23], i.e. ignore the inteference sig-nal that is below the CCA threshold.

Unfortunately, the SINR model is challenging to workwith because each transmission interacts with all othertransmissions. Thus, for deriving our protocol, we sim-plify the SINR model by making two assumptions: 1)interference is dominated by the strongest source (pair-wise interference assumption), and 2) we can ignorethermal noise. Recent work [6] has shown that thepair-wise assumption usually holds and we can ensurethe second assumption by keeping power levels highenough. Note that this model is similar to the pro-tocol model [18][9], except that we do not assume thatall nodes use the same power level.

2.2 Conflict GraphTo concisely represent the interference that is present

in a network, researchers have used a conflict graph [18].Each vertex in the conflict graph represents a link inthe wireless network and there is an undirected edge

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(b) (d)

(a) (c) (e)

Figure 2: Topologies for two interfering flows: (a) in general, (b)(c)(d)(e) some common scenarios.

between two links if the two links interfere, i.e. theycannot be active at the same time. Clearly the conflictgraph depends on the transmit power used by the nodes.We construct the conflict graph based only on the pair-wise SINR model described above, independent of anyMAC protocol. This is the reason why it is sufficient touse undirected edges: concurrent transmissions shouldbe avoided both when two links interfere with each otherand when interference happens in only one direction.

The goal of our work is to increase spatial reuse,which is beneficial for almost all practical network de-ployments. Since the lack of an edge in conflict graphis equivalent to enabling concurrent transmissions, ourgoal is equivalent to minimizing the number of edges inthe conflict graph. Note that earlier work has generallyused more specific optimization criteria, e.g. maximizemulti-hop network capacity [18], achieve maxmin fair-ness [2], or minimize mean delay [17].

3. POWER CONTROLIn this section, we develop a centralized algorithm

for per-link power control on a collection of nodes. Wefirst analyze how tranmission power should be config-ured in a simple two flow scenario. We use the insightsgained from this simple setting to design an iterative,greedy algorithm for a cluster of nodes, considering onlyphysical layer effects. We then consider the impact ofthe MAC layer, specifically carrier sense, and we de-scribe candidate CCA tuning mechanisms. The reasonwe separate the selection of transmit power and CCAthreshold is that the interactions between the two pa-rameters are complex, so concurrent optimization is dif-ficult, if not impractical. Since the transmit powers arethe primary factor to optimize, selecting transmit pow-ers first, followed by CCA thresholds, is both practicaland effective. While we show in our evaluation that thisapproach is effective, it does occasionally lead to lostconcurrent transmission opportunities. Thus we alsointrodcue a post-CCA power reallocation algorithm toto further improve spatial reuse.

3.1 Simple Two-Flow Scenario

We begin by considering a simple scenario (Figure 2(a))where S1 transmits to R1 and S2 transmits to R2. TheSINR at receivers R1 and R2 are SINR1 = P1 −L11 −P2+L21 and SINR2 = P2−L22−P1+L12, respectively,where Pi is the transmit power level from Si to Ri, andLij is the path loss from Si to Rj (i, j ∈ {1, 2}).1 Notethat independent of the transmit power levels, we haveSINR1+SINR2 = L12+L21−L11−L22. Power controlessentially allocates this sum between the two transmis-sions, i.e. increasing SINR1 will decrease SINR2. Inorder to enable concurrent transmission, we need bothSINR1 ≥ SINRthrsh and SINR2 ≥ SINRthrsh. Notethat it may not be possible to satisfy both constraints,so concurrent transmission may be impossible.

We now consider what happens when all nodes usethe same power (i.e., equal power) or when they use theminimum power level to reach the receiver (i.e., min-imum power). With equal power, we have SINR1 =L21 − L11, and SINR2 = L12 − L22. Thus, in Fig-ure 2, equal power performs well in scenario (c) & (d)because SINR1 ≈ SINR2 but poorly in (b) & (e) be-cause SINR1 >> SINR2. With minimum power, wehave SINR1 = L21 − L22, and SINR1 = L12 − L11.Thus, minimum power performs well in scenario (b) &(e) but poorly in (c) & (d). Intuitively, if the senderis far away from the receiver, the transmission is morelikely to be penalized using equal power because its re-ceived signal strength is relatively weak. Using mini-mum power has the opposite problem. If the sender isclose to its receiver, it is more likely to be penalizedbecause the interference level is likely to be high.

We constructed the scenarios shown in Figure 2(b)&(d)on a 4-node testbed and used it to compare the perfor-mance of different transmission power and CCA thresh-old settings. We first obtained a baseline throughput byhaving only one of the sources transmitting. We thenran experiments with both sources active, using theequal and minimum transmit power settings describedabove, combined with the default 802.11 CCA threshold

1We do not assume pathloss obeys the triangle inequality inour algorithms or protocols. However, simulations use thisassumption for simplicity.

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Table 1: Evaluation result on a 4-node testbed.Scenario PC/CCA LT1(%) LT2(%) LT1+LT2

Equal/High 81.4 96.6 178.0Figure Equal/Def 54.8 60.8 115.62(d) Min/Def 11.2 102.0 113.2

Protocol 94.3 90.0 184.3Equal/High 20.8 98.8 119.6

Figure Equal/Def 36.4 81.5 117.92(b) Min/Def 98.8 85.9 184.7

Protocol 94.1 93.3 187.4

and a high CCA threshold that prevents transmittingnodes from deferring to each other. We also used ourproposed protocol for power and CCA threshold selec-tion. The details of our protocol and the experimentalsetup are described in Sections 4 and 5.

The results, as a percentage of baseline, are shownin Table 1. We see that our protocol enables concur-rent transmissions in both scenarios, while neither equalpower nor minimum power work well in both scenarios.Specifically, our power control mechanism alleviates thehidden terminal problem caused by minimum power inscenario (d), by equal power in scenario (b), and alsosolves the exposed terminal problem caused by defaultCCA threshold in scenario (d).

3.2 Centralized AlgorithmWe now present the greedy, iterative power control

algorithm that forms the basis of our distributed pro-tocol. It assumes that every node has complete knowl-edge of the network topology (i.e. path loss betweennodes) and current configuration (i.e., power levels usedby sources). The basic idea of the algorithm is to greed-ily decrease the number of edges in the conflict graph.We do per-link power management since it offers moreopportunities for special reuse than per cell configura-tion used in earllier work [17]. Thus, for example, inan infrastructure network, each client contributes twolinks to the network, one from itself to the AP, and theother from the AP to itself.

In the previous section, we showed how to set theratios of transmit power between two links to allow si-multaneous transmission, if possible. However, if thereare multiple links, the choice made for one link may notbe compatible with the choices for other links. Thus,we use a greedy algorithm that iteratively allows moreconcurrent transmissions by removing edges from theconflict graph. We use the following notations in Al-gorithm 1. src(t), dst(t) are the source and destinationof link t while P (t) is the power level currently usedon that link. For any two nodes n, n′, L(n, n′) is thepath loss from n to n′. We assume that each wirelessdevice has a range of discrete tunable power levels, e.g.

0dBm to 20dBm; in practice, power range is typicallylimited by noise consideration (lowerbound) and powerrestrictions / hardware limitation (upperbound).

In each iteration, and for each link t, the algorithmexamines the power level used on all other links (line 6)and the topology (line 7-10) to determine what powerlevel would allow simultaneous transmission with theother links (line 11-12). It then picks the power levelthat can remove the most edges from the conflict graph(line 18). The new power level will be used if it allowsmore concurrent transmissions than that in the previ-ous iteration (line 19-21). In each iteration, the numberof edges in the conflict graph decreases, and the algo-rithm converges when no more edges can be removedfrom the conflict graph. Also, by using this algorithm,the source that needs maximum power level will hit thepower limit, and then all other sources will keep anappropriate ratio to that source. Simulation results forthis algorithm (omitted for space reasons) show that al-though the algorithm is greedy, its performance is veryclose to the upper bound.

Algorithm 1 Iterative Power Control Algorithm1. while not stable do2. /* For each link t, use v[i] to determine how often

concurrent transmission is possible with powerlevel i */

3. for all t do4. clear(v)5. for all t′ 6= t do6. P ′ ← P (t′)7. L11 ← L(src(t), dst(t))8. L12 ← L(src(t), dst(t′))9. L21 ← L(src(t′), dst(t))

10. L22 ← L(src(t′), dst(t′))11. Pmin ← L12 − L22 − SINRthrsh + P ′

12. Pmax ← L11 − L21 + SINRthrsh + P ′

13. /* [Pmin, Pmax] is the range of P (t) such thatt and t′ can transmit together */

14. for i = Pmin to Pmax do15. v[i]++16. end for17. end for18. Find the Pm such that v[Pm] is maximum.

/* last[t] is used to ensure convergence: changepower level only when more concurrent trans-missions are allowed */

19. if v[Pm] > last[t] then20. P (t)← Pm

21. last[t]← v[Pm]22. end if23. end for24. end while

3.3 Interactions with Carrier Sense

4

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Figure 3: Example of Power Reallocation.

Algorithm 1 did not consider MAC layer effects suchas carrier sense. Past work has however noted the im-portance of CCA tuning when doing power control [17,1]. We now describe candidate CCA tuning mecha-nisms, leveraging earlier work [7, 21].

In Alpha [7], every source uses a fixed product α(product in watts, or sum in dB) for power level andCCA threshold. Having every node uses a same alphaensures the symmetry property [17], where either bothnodes hear each other or neither can hear each other.In Echos [21], every source picks a CCA threshold thatallows it to hear all the transmissions that interfere withits current transmission. Simulations (Section 7) showthat Echos can lead to starvation for some transmis-sions. The reason is that each node in Echos greedilyoptimizes for its own transmissions. To address thisproblem, we introduce Altruistic Echos (AEchos). It issimilar to Echos, but sets the CCA threshold to hear allthe transmission that interfere with the current trans-mission or will be interfered with by the current trans-mission.

Both Echos and AEchos use localized decisions andcan easily be incorporated into the power managementprotocol. Alpha is more complex to integrate since it re-quires network-wide agreement on α. In our evaluation,we manually set the α.

3.4 Post-CCA Power ReallocationWhile selecting power levels first and then CCA thresh-

olds is both practical and effective, it does occasionallylead to lost concurrent transmission opportunities. Fig-ure 3 shows an example for a one-hop network. Re-ceiver R4 is in a bad location since flow F14 interfereswith F25, but otherwise all flows can transmit simul-taneously. Suppose that Algorithm 1 assigns the samepower level from S1 to all its receivers. S2 can now ei-ther use a high CCA threshold that causes R4 to starve,or it can use a low CCA level that wastes the spatialreuse opportunities with F11, F12, F13. A better solu-tion is to have S1 reallocate power levels to its receivers:by using a lower power on F11, F12, F13, S2 can set itsCCA threshold to defer to F14 but ignore F11, F12, F13,thus allowing spatial reuse with F25 without starvingR4. Power reallocation is possible because the itera-tive power control algorithm in fact produces a rangeof power levels that result in the same conflict graph.

The basic idea behind power reallocation is to assign

higher power levels to receivers that experience higherlevel of interference, e.g. R4 in the above example, whilekeeping the power levels within the ranges producedby the iterative algorithm. The algorithm is executedlocally on each sender, but only when the sender hasmultiple receivers, e.g. in infrastructure networks, onlyAPs need to execute it. To reallocate power levels, thesender first sorts all its receivers by the level of inter-ference they experience (high to low) and then tries toimprove spatial reuse by reallocate the transmit poweraccording to the sorted order. It always keeps the trans-mit power within the range produced by the iterativealgorithm, even this means that concurrent transmis-sion opportunities cannot be increased. Note that theeffect of power reallocation, i.e. improved spatial reuse,will take place in the next iteration when other sendersadjust their CCA thresholds accordingly.

4. DISTRIBUTED PROTOCOLIn this section, we present the distributed power man-

agement protocol based on the algorithm described inSection 3. We describe topology information collectionand packet reception and transmission processing.

4.1 Data CollectionIn order to create a local interference graph, nodes

need information about the path loss on nearby links.This is obtained by having each source insert transmitpower and path loss information in each packet, specif-ically, the power level used for the current packet, thepath loss measurement to the destination, the path lossmeasurement to a randomly chosen third node, and abit to indicate whether this is a high power frame (seebelow). The power level used for this packet will beused by the receiver to calculate the path loss. Thesender’s path loss measurement to the destination willbe used by the receiver to help calibrate the power lev-els of the cards (Section 5.5). Finally, by including thepath loss to a third node, we are effectively creating agossiping channel that allows a source to learn the pathloss of links they are not part of. This is used by thesource to estimate the interference at its destinations.The overhead of extra information is less than 1% for afull-sized data packet.

Let us illustrate the data collection phase using theexample in Figure 2(a), S1 needs the following infor-mation to determine power level: L11, L12, L21, L22, P2.Then, P2 can be extracted from packet when S2 is trans-mitting. L11 can be measured when R1 is transmitting,L12 can be measured when R2 is transmitting, and L21

can be measured by R2 when S2 is transmitting andsent to S1. Finally, L22 can be measured and insertedinto a packet by S2.

When a node uses a low power level, other transmit-ters may not be able decode its packets and collect the

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above information. To avoid this problem, nodes peri-odically (e.g. every 2 seconds) transmit announcementsat the maximum power level. These announcements canbe piggybacked on other packets, such as beacons orACKs. A similar problem happens when a source usesa high CCA threshold and has a lot of packets to send:it will transmit a significant fraction of the time withoutdeferring to other sources, thus preventing it from over-hearing packets from other sources. Though this doesnot affect performance after the protocol converges, itwill affect its ability to change its view of the topology,e.g. when a node moves or joins the network. In or-der to avoid this problem, each node periodically set itsCCA threshold to low level (e.g. 20ms every second).

Each node in the system operates in promiscuous /monitor mode and upon receiving a packet, the nodeupdates its topology information and configuration. Foreach of its active flows, a source node keeps several lists:Tsd to store current transmit power level, current CCAthreshold and measured path loss for the source to theflow’s destination; Tso to store path loss from the sourceto other nodes; Tdo to store path loss from the destina-tion to other nodes; and Too to store the power level,and path loss of other flows.

4.2 Packet Transmission/ReceptionWhen node S1 is about to transmit a data or con-

trol frame to destination R1, S1 will first search for thepower level, CCA threshold, and path loss for R1. Ifsuch information is unavailable, it will use the defaultpower level and CCA threshold, and it inserts an in-valid value for path loss in the packet. This shouldonly happen for the first frame between the sender andreceiver, e.g. an association request in infrastructurenetwork. After a few packet exchanges, S1 will haveinitial measurements for power level, CCA threshold,and path loss, and will use those for the transmission.In addition, S1 will also piggyback power level, pathloss, and the picked entry from the Tso list onto theframe. The frame is then added into the device queuefor transmission.

Upon receiving a packet from R1, node S1 measuresthe RSS of that packet and it extracts power level, pathloss from R1 to another node, and path loss from R1 toits destination from the frame. The path loss from R1

to S1 is calculated by subtracting RSS from power levelused for the packet. Then, S1 will determine which liststo update. If the packet is destined to S1, or the packetis from its sender to another receiver, then it will updatethe Tsd and Tdo lists. Otherwise, the node will updatethe Tso and Too lists. If there is an update to any ofthe lists, S1 will recalculate the configurations for all itsdestinations.

4.3 Absolute Transmit Power

Algorithm 1 presented in Section 3 determines theratios between the transmit powers for all the wire-less links, i.e. uniformly shifting the power levels upor down does not affect the results. We also notedthat, in practice, the range is limited by noise considera-tion (low end) and power limitations (high end). Whilethe optimization algorithm is not sensitive to absolutepower levels, the protocol generally benefits from usinghigh power levels. The reason is that this increases theamount of topology information that nodes can collectby overhearing packets. As a result, our protocol firstcalculates ratios and then assigns the maximum powerlevel (usually 20dBm) to the link requiring the highestpower. The power for the other links are then derivedusing the ratios.

5. IMPLEMENTATIONWe now present an implementation of the protocol

using commercial wireless hardware. We also discussseveral practical challenges including protocol stabilityand device calibration.

5.1 Ideal System RequirementsThe following is a list of ideal hardware/firmware fea-

tures needed to run our protocol. Since these featuresare not well supported in many current devices, we de-scribe design alternatives and “work-arounds” in thefollowing sections.

Per-packet Transmit Power and CCA Thresh-old Control. Control over transmit power and CCAthreshold is central to our design. To the best of ourknowledge, the only publicly available solution that sup-ports this is the Intel 2915/2200 card with AP firmwareand driver. Also, since our protocol works on a per-link basis, these controls need to operate on a packetgranularity, at least for senders with multiple receivers.Unfortunately, the Intel 2915/2200 AP firmware doesnot support these functions stably on a per-packet ba-sis. We believe that this weak support is a result of alack of demand and that future cards should be able tosupport per-packet reconfigurability.

Receiver Threshold Control. The receiver thresh-old is used by a radio to determine when to decode an in-coming signal. If a signal is below this threshold, the ra-dio is not activated. On Intel 2915/2200 hardware, thereceiver threshold is coupled with CCA threshold [23].As a result, when a node uses a high CCA threshold,it can no longer decode many low power transmissions,limiting its ability to gather topology information in ourprotocol. Even worse, the node can miss critical pack-ets, e.g. an AP may miss association requests from anew client even if it is sent using a high power level.We want the receiver threshold to be set independentlyfrom the CCA threshold. Again, this should be possiblein future cards.

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Accurate Signal Strength Measurements andTransmit Power Settings. Our protocol dependson accurate RSS measurements and accurate controlof the transmit power. Our experience shows that theRSSI readings from the Atheros cards and the trans-mit power setting of the Intel cards we used are fairlylinear. However, our experience, confirmed by earlierwork [8][12], shows that wireless cards are uncalibrated,both in terms of transmit power levels and RSSI read-ings. This is the case even for cards of the same modelfrom the same vendor. As a result, changes in thesevalues are measured accurately but the absolute valuesof the readings cannot be compared across cards. Sincenodes exchange both transmit power and RSSI values inour protocol, we need cards to be reasonably calibrated.Calibration by the manufacturers only solves part of theproblem, because cards can become uncalibrated overtime due of drift. Thus automated calibration is neces-sary and we describe our mechanism in Section 5.5.

5.2 Implementation SetupWe implemented our power control protocol with AE-

chos CCA tuning mechanism (the choice is based onsimulation results in Section 7) in Linux using nodesequipped with one Intel 2915 card and two Atheros 5212cards. This setup is needed so we can work around theshortcomings of the current commercial cards. The In-tel card supports transmit power and CCA tuning andit is used as the primary transmission interface. To dealwith the receiver threshold problem, one Atheros card,using a default receiver threshold, is used to monitorall ongoing traffic. This allows us to collect topologyinformation, which is then used to set the txpower andCCA threshold on the Intel card.

When measuring the performance for bidirectionaltraffic, e.g. TCP, both senders and receivers must beable to control their transmit power and CCA thresh-old. To achieve this, nodes use the Intel card (in APmode) for transmission (providing control over powerand CCA threshold) and the second Atheros card for re-ceiving (capture effect presents); we realize this set upby configuring the routing tables appropriately. Notehowever that this set up only allows a sender to talkto one receiver at a time since the Atheros card of thesender can only be associated with the Intel card (whichneeds to run in AP mode) of one receiver.

For experiments that use multiple receivers, we sim-plify the previous setup by having receivers not use theirIntel card. Instead, all receivers transmit and receivewith their Atheros cards. We use one-directional UDPin those experiments so that the lack of transmit powerand CCA control on receivers will not affect the results.Another problem with the multi-receiver set up is thatwe do not have per-packet control over the transmitpower and CCA threshold, so we cannot easily inter-

(a) Topology and association (b) Conflict graph

Figure 4: The topology and association of ourexperiments with 8 nodes

leave transmission to multiple receivers. To overcomethis problem, we switch between receivers at a coarsegranularity: the sender transmit for 10 seconds to eachof its receivers in turn. All possible sender-receiver com-binations are enumerated, and the throughput of eachflow is calculated as the mean throughput of that flowacross all combinations (i.e. the full duration of theexperiment).

Note that our protocol does not apply to link-layerACKs. There are two implications. First, since thewireless card sends the ACKs, we cannot control thetransmit power that is used (note that ACKs do notuse carrier sense so the CCA threshold is not relevant).Second, since link-level ACKs are not delivered to thedevice driver, they cannot be used to update the topol-ogy information. This loss of information is especiallycritical in unidiectional tests. We overcome this prob-lem by exchanging several packets between senders andreceivers at the start of each experiment so all nodeshave relevant topology information.

All our experiments use the setup in Figure 4.

5.3 Smoothing RSSI ReadingsRSSI readings will always vary over time due to noise,

interference and environmental changes. We found thatmany variation in RSSI readings tend to have a shortduration. In our protocol, stability of the RSSI read-ings is more important than the accuracy of individualreadings, so it is advantageous to filter out these varia-tions even if they reflects actual signal strength changesand are not an artifact of the measurement. The rea-son is that rapidly changing RSSI readings could leadto fluctuations of signal strengths throughout the net-works and possibly network instability.

A common approach to smoothing RSSI readings isto take a moving average [13]. However, moving av-erages of RSSI can still fluctuate greatly, even with alarge averaging window of 1 second. Since most RSSIvariation is on short time-scales, we take the approach

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Figure 6: Assessment of CCAthresholds

of removing short-lived changes from RSSI readings, sothey do not contribute to the moving average. We dothis by filtering out all RSSI values that (1) that devi-ates by more than 2dB from the current moving aver-age and (2) is short lived. The deviating RSSI readingis considered to be short-lived, if within the averagingwindow, there exists a time interval K, where all RSSIvalues in the interval fall within 2dB of the moving av-erage. We set the value of K to be 10% of the averagingwindow. Otherwise, the change is considered long-livedand it is incorporated into the average.

In order to determine the appropriate window sizefor filtering readings, we carry out the following experi-ment. Node S3 transmits and S1, S2, R1, and R2 recordthe RSSI readings. Figure 5(a) shows how many read-ings are discarded at the four receivers. Note that largerwindows limit the rate at which we adjust to long-livedchanges. As a result, we use a 500ms window in our pro-tocol since it is the smallest window that removes mostspurious data points. Figure 5(b) shows the result ofapplying our filtering algorithm to the RSSI readingsfrom two RSSI measurement streams. The first andthird curves result from our smoothing algorithm witha 500ms window, and the second and fourth curves arefrom a simple moving average with a 1 second windowfor comparison. The result shows that the our filteredreadings are both stable and reasonable. We will alsoshow later that the filtering does not hurt throughput.

5.4 CCA Offset and Transmit Power Granu-larity

Measurement noise, calibration accuracy and otherfactors mean that small changes in CCA threshold andtransmit power will have little impact on system behav-ior. We explore this granularity issue, focusing on iden-tifying what CCA thresholds must be used to reliablydefer or ignore competing signals and how power levelsmust be adapted to accomodate these CCA thresholds.

To determine how low S1 should set its CCA thresh-old to correctly defer to another transmission, we let F22

transmit with a fixed low CCA threshold, and set thetransmit power of F11 to interfere with F22 (Figure 4).

Figure 6(DEFER) shows F22’s throughput for differentCCA threshold settings on S1. The throughput is plot-ted as a ratio to the throughput of F22 when S1 usesa default low CCA threshold and the offset is plottedas an offset from the average RSS from S2. It showsthat with an offset -4, the ratio is close to one. Thisindicates that in order for S1 to defer to transmissionF22, the CCA threshold on S1 should be set 4dB lowerthan the interfering signal. To measure how high theCCA thresholds need to be to ignore a signal, we let F22

transmit with a fixed high CCA threshold, and F11 andF22 are configured so that they can transmit simultane-ously. The baseline is the throughput of F11 when S1

uses a very high CCA threshold. Figure 6(NODEFER)shows the ratio of F11’s throughput relative to this base-line for different CCA thresholds on S1. It shows thatthe CCA threshold need to be 6dB higher than averageRSS to achieve full spatial reuse.

A related issue is what the granularity should trans-mit power settings be. This is especially important forthe reallocation algorithm in Section 3.4 since it maybe necessary to distribute a number of values within alimited range. While the transmit power can be anyinteger, in practice, the useful transmit power spacingdepends on CCA granularity, which is essentially thedifference between the two CCA offsets, e.g. 10 dBin this case. For example, the CCA threshold on S1

should be P22 − L(S1, S2)− 4 to defer to transmissionF22, where L(S1, S2) is pathloss between S1 and S2.And at the same time, the CCA threshold should beP23 − L(S1, S2) + 6 to ignore transmission F23. ThusP22−L(S1, S2)−4 = P23−L(S1, S2)+6, or P22 shouldbe 10 dB higher than P23. We carried out similar ex-periments on other senders and all had a granulatity ofless than 10dB. Thus we use a spacing of 10dB in ourexperiments, so given the txpower range of our cards,each sender can support 3 receivers without losing spa-tial reuse.

5.5 CalibrationOur protocol relies on both transmit power settings

and RSSI readings on network cards to be accurate.

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Even if hardware manufactures calibrate network inter-faces, the devices can become uncalibrated over time [8].While manual calibration using a signal analyzer andgenerator is possible [8], this is not a practical solutionis most deployments. In this section, we describe anautomated calibration mechanism.

As observed in previous work, the relationship be-tween output power and transmit power value on eachcard is roughly linear but there is typically an offset; off-sets of up to 20 dB have been observed in [8]. The rela-tionship between actual RSS and RSSI readings is simi-lar and offsets of up to 10dB have been observed in [12].To formalize the calibration problem, let ∆txpowi and∆RSSIi denote the difference between actual and re-ported transmit power levels and RSSI readings at nodei, respectively. For our protocol, we need to calibratethe cards relative to one particular node i, i.e. solve∆txpowj − ∆txpowi and ∆RSSIj − ∆RSSIi for anynode j. Put it another way, after calibration, we onlyneed to know the differences in the offsets between allnodes, and share the same unknown ∆txpow and ∆RSSI.The two unknowns will not affect the result of our pro-tocol because: 1) every pathloss will have an offsetof ∆RSSI, and this does not affect the calculation ofSINR for either flow, 2) every txpower setting will havean offset of ∆txpower, which doesn’t change SINRs northe conflict graph, 3) ∆RSSI and ∆txpower does notneed to be equal because CCA thresholds are set basedon observed RSS, which already includes an offset of∆txpower.

We calibrate the RSSI readings based on the assump-tion that receiver sensitivity of all cards are the same.

Table 2: RSSI offset from R1 (in dB) with thedifferent cards

Experiment S1 R1 S2 R2

1 1 0 2 02 1 0 -1 23 1 0 3 0

We let one node send packets with different transmitpower levels and all other nodes record the RSSI of thepackets they received. For nodes that cannot receive thepackets at low transmit powers, the histogram of theRSSI readings of the first N received packets shouldbe independent of their location, but depend only onthe difference between their ∆RSSIi’s. In contrast,the histogram of the transmit power level of the firstN received packets is determined by its location, i.e.pathloss. We compare the RSS histograms with differ-ent offsets, and choose the offset with the closest dis-tance, i.e. argmink

∑i |Pi − Qi+k| for two histograms

P and Q. Figure 7 shows an example of transmit powerand RSS histograms for the same device at two differentlocations with N = 15000. It shows that even thoughthe device has a different txpower histograms at twolocations, i.e. different pathlosses to two locations, theRSS histograms are roughly the same. Note that if anode can receive packets from the lowest power levels,it should be excluded because the histogram does notreflect its sensitivity, but its location instead. Also, thismechanism is susceptible to external interferenc andsuch RSSI calibration should be carried out when ex-ternal interference is expected to be low.

In our experiments, we used S3 as the sender to cal-ibrate the RSSI readings of S1, S2, R1, and R2. Weran the experiments three times, with receivers at dif-ferent locations. Table 2 shows the offsets of differentcards from R1. Ideally, the offsets would all be con-sistent, and this is nearly true for experiment 1 and3. However, experiment 2 produces a slightly differentcallibration, which might be caused by external inter-ference during the experiment. Thus we use the RSSIoffsets from experiment 1 on each node. Also, comparedwith the calibration errors observed in previous work,the calibration errors in our testbed are small becauseall the wireless devices are newly purchased.

After the RSSI readings are calibrated, we calibratethe transmit power levels based on the law of chan-nel reciprocity, i.e. the pathloss from node A to B isthe same as that from B to A at each point in time.This means that nodes can detect the offset betweentheir transmit powers by exchanging their views of thepathloss between them (Section 4.1); the difference be-tween the two reported pathloss values is the offset.

6. EXPERIMENTAL EVALUATION

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Figure 8: UDP Experiments with 4 nodes, i.e. 2 flows F11 and F22.

In this section, we evaluate our protocol on a 8-nodeindoor testbed, as shown in Figure 4(a). The nodes arelocated in two rooms on opposite sides of a hallway.

6.1 UDP Throughput & Protocol BehaviorTo evaluate how well RSSI smoothing and calibration

work, we carry out an experiment involving four nodes,S1, R1, S2, R2, forming two UDP flows F11 and F22,and using the UDP setup. We compare our protocolwith several other mechanisms, 1) Manual: manuallytuned txpower with high CCA threshold, to obtain sim-ilar throughput for both flows, this shows the best thatour protocol can achieve, 2) High/Def: default transmitpower with a high CCA threshold, which shows whethertwo flows interfere or not, and 3) Default: default trans-mit power and CCA threshold. Transmit rates areset manually, and we show results from 36Mbps and48Mbps, since these rates result in different conflictgraph. All throughputs as shown are an average over 4runs.

Throughput. Figure 8(a) shows the UDP through-put for the data rate of 36Mbps. The horizontal linesshows the throughput when only one flow is active. Thesolid line is for a high CCA threshold, which measuresthe maximum possible throughput, and the dashed lineis for the default CCA threshold, which shows impactof other traffic in our test environment. When bothflows are active, they cannot achieve full throughputwhen using the same power level, since F11 interfereswith F22 (but not vice versa) as shown by the High/Defresults. Simultaneous transmission is possible if F11 usea lower power level, as shown in Manual. The through-put of our protocol is very close to the upper bound

Table 3: TCP PerformanceData Rate Pwr Ctl LT1(%) LT2(%)36 Bi 94.6 90.7

Uni 96.3 8.648 Bi 91.7 87.7

Uni 90.6 85.6

of Manual. For Default, note that even with the low,default CCA thresholds, when both transmissions canhear each other, throughput is not exactly fairly shared;this is probably caused by collisions that affect con-tention window sizes and thus fairness.

Figure 8(b) shows the UDP throughout for the datarate of 48Mbps. The solid and dashed lines are similarto those in Figure 8, but the throughputs are halvedbecause, at 48Mbps, both flows always interfere witheach other. From the High/Def bar, we see that F11

has a better SINR than F22 when nodes use the samepower. The throughput of our protocol is roughly thesame as the default configuration. Fairness is slightlyworse than Manual since our CCA threshold selectionis somewhat prone to noise in the RSSI values. Theseresults show that our protocol correctly identifies whenflows interfere and that the throughputs of our protocolare very close to the upper bound.

Stability. Figure 8(c) shows the view of the topol-ogy at nodes S1 and S2 over time, and Figure 8(d)shows the transmit powers and CCA thresholds theychoose. Compared with the results without filtering,Figure 8(e)&(f) respectively, our protocol is quite stableand the topology views at different nodes are roughlyconsistent.

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Table 4: Convergence Time after Sudden Move-mentMovement Without Low With Low

CCA (seconds) CCA (seconds)R1 away from S1 15.5 1.2R1 to S1 0.8 0.7

6.2 TCP Throughput & Bidirectional TrafficIn this experiment, we run TCP bulk transfers from

S1 to R1 and S2 to R2 at the same time. Table 3presents the TCP throughput for data rate of 36Mbpsand 48Mbps as a percentage of the throughput achievedwhen one transmission is active. “Bi” power protocolrefers to a setup where we perform power control andCCA tuning on both link between the senders and re-ceivers, while the “Uni” results are for a setup wherewe only perform power control on the sender. The factthat TCP performs well at 36Mbps in the Bi setup butnot in the Uni setup shows that although TCP ACKsare small, their transmissions can have a significant im-pact. At 48Mbps, the throughputs in the Bi and Unisetups are almost the same since all nodes use low CCAthresholds.

6.3 Mobility & ConvergenceWe evaluate how quickly our protocol can converge

after a sudden node movement. Since bidirectional traf-fic is necessary for the nodes to rebuild the topology, weuse the TCP setup in this experiment, but since TCPmay interact with packet losses due to movement, weuse bidirectional UDP traffic instead. Table 4 showsthe convergence time after moving R1 close to or awayfrom S1. It is calculated as the time from the earliestobserved change in topology to the last change plus anadditional 500ms delay from the smoothing algorithm.Note that the results exclude the duration between theactual movement and when the node receives a firstpacket after the movement.

The results show that periodically lowering the CCAthreshold, as described in Section 4, is necessary to dealwith such changes. Without this optimization, the con-vergence time for R1 moving away from S1 is very longbecause it requires a change from high CCA to low CCAand such change is difficult when the flows are satu-rating the network. Convergence time is much shorterwhen R1 is moving towards S1 in both cases because achange from low CCA to high CCA is easier.

6.4 More Complex ScenariosIn this experiment, we use all 8 nodes in our testbed.

A manually generated pair-wise conflict graph for the36Mbps data rate is shown in Figure 4(b). The through-put of each flow for data rate of 36Mbps is shown in Fig-ure 9. Given the conflict graph, our protocol achieves

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almost optimal throughput, which is 200% better thanthe default configuration.

First, let us look at F11, F22 and F23. In this exper-iment, F11 interferes with F23 but F22 does not. Thus,our protocol makes F22 use a lower power level, whichallows F23 to chose a CCA threshold that allows con-current transmission with F22 but defers to F11. Thetransmit power spacing of 10dB works in this case andthe throughput of F23 is only slightly lower than ex-pected. Second, F34 and F35 can both transmit simul-taneously with all other flows. At S3, F35 uses a powerlevel of 10dBm and F34 uses 0dBm. Though F35 uses ahigh power level, it gets slightly lower throughput thanexpected. This is probably because the pair-wise inter-ference assumption does not quite hold, since F35 canachieve full throughput when only one of the flows F11

and F23 is active. Also, the performance of the defaultconfiguration is better than expected. The reason isprobably that the nodes are in another room, and theRSSI readings can sometimes drop below the defaultCCA threshold due to changes in the environment.

7. OPNET SIMULATIONWe now use the OPNET simulator to study several

properties of our protocol: network capacity, fairness,convergence, and performance in large grids. We usetwo node placement models. 1) Clustered placement:APs (sources) are uniformly distributed, and the clients(recivers) are uniformly distributed in a disk of a chosenradius center at a randomly chosen AP, and 2) Randomplacement: APs and clients are uniformly distributed,and clients associate with the closest AP. Since the re-sults from the random placement model are similar tothose from the clustered model, we only present the re-sults for the clustered model. In our simulations, allAPs operate on the same channel and use the samedata rate. We simulate 10 APs and 10 clients withina 100m×100m grid, placed according to the clusteredmodel. We vary the cluster radii from 3m to 21m. Theresults for each radii setting are the average of threedifferent random topologies. The start time for trafficflows is uniformly distributed in the first 20 seconds andwe run each experiment for 20 minutes. Traffic is gen-erated using an exponential on-off process with a traffic

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Table 5: Network Capacity (Mbps) with differ-ent radius size (m)

CCA PC 3 9 15 21Iter 31.4 28.1 17.9 15.2

Echos Min 29.9 24.2 15.9 8.70Equal 29.0 19.3 13.4 9.46Iter 30.8 27.6 17.2 12.7

AEchos Min 30.2 19.8 14.1 8.37Equal 25.3 18.0 11.3 7.00Iter 31.4 23.5 14.0 8.15

Default Min 30.3 22.5 13.8 7.61Equal 6.40 6.31 6.12 5.96Iter 32.0 28.4 9.60 8.60

Alpha Min 30.3 24.1 12.5 6.48Equal 28.2 18.7 12.1 7.75Iter 16.7 11.8 6.51 4.32

No Min 15.6 11.7 5.37 3.09Equal 15.2 9.05 2.98 2.49

demand for each node of about 2Mbps.We simulate all combinations of CCA tuning and

power control. For power control, the choices are ouriterative algorithm, the minimum power to reach a re-ceiver, and all nodes using equal power. For CCA tun-ing, the choices are Alpha tuning, Echos, AEchos, de-fault CCA threshold and disabled carrier sense (sug-gested in [10]). Equal power combined with defaultCCA is essentially the default configuration in 802.11.For Alpha tuning, a relatively high α is aggressive andwould lead to more collisions and a relatively low α isconservative and would lead to more exposed terminalproblems. We pick the α manually such that the lowestlink throughput would be similar to what we observeusing iterative power control with AEchos.

Network Capacity. Table 5 presents the simulationresults for network capacity (total throughput across allnodes). For power control, iterative power control im-proves capacity by 2 to 75% over minimum power, andby 8% to 82% over equal power. This improvementalso increases as the average radius size increases, i.e.more diversity in client-AP distance. Minimum powerwith default CCA threshold works well when the ra-dius is short, but the performance degrades when theradius is long. For CCA tuning, Echos provides thebest capacity when the radius is longer than 12m, andAlpha has the best capacity when radius is short. Thereason that Alpha’s performace degrades with the ra-dius is that the iterative power control does not considerthe CCA tuning mechanism, and can create asymmetricphysical layer interference. For example, one link mayuse low power to enable concurrent transmission withanother link, while other links uses high power level,causing asymmetric interference. Thus, if the α is setaggressively, then a low power link can starve. As a re-

Table 6: Protocol Convergence Time (sec)# AP WO/ Low CCA W/ Low CCA Low Traffic10 39 25 1220 138 121 37

sult, we set α more conservatively to prevent starvation.However, this causes the network capacity to decreasedramatically. The results also show that AEchos per-forms similarly to Echos, with the difference rangingfrom 2 to 20% for iterative. When equal power anddefault CCA threshold are used, the simulated regionsis small enough that all sources defers to each other.Thus, the network throughput is about the same, in-dependent of the radius. Disabling carrier sense doesnot work very well in the simulation, since the trafficdemand is high and many collisions happen. However,consistent with the observation in [10], the throughputcan be higher than using the default configurations. Wealso changed the number of APs, while keeping the ra-tio of AP-AP distance to client-AP distance to about4.5. The results from this setup are consistent with theabove. This setup did show that the improvement ofiterative approach increases with the number of APs.

Fairness. Figure 10 shows the CDF of link through-put for a radius setting of 15m. The numbers in legendare the Jain fairness indices for each curve. In Echos (a)and AEchos (b), the curves for the iterative algorithmare roughly to the right of the other two curves, indi-cating that it provides better throughput for all links.Iterative also has a better fairness index. In general,Echos has better throughput than AEchos, but we ob-served that Echos results in starvation for some nodes.Starvation was especially a problem for Echos in largeradii. AEchos provides a reasonable balance betweenthroughput and avoiding starvation.

Convergence. Table 6 shows the convergence timesfor iterative power control with and without periodiclow CCA threshold for different numbers of APs. Thetime is measured from the start of the experiment untilthe configuration is stable. Remember that flows arestarted uniformly during the first 20 seconds, so con-vergence may be slow during that period. It shows thatthe convergence time of the protocol can be reduced byperiodically lowering the CCA threshold. However, theconvergence time can still be more than two minutes.This is because the duration of low CCA is only 20msin our protocol, and when the traffic demand is high,collisions can prevent nodes from successfully overhear-ing enough traffic to collect topology information. Atlower traffic rates, the convergence time is significantlylower (last column in Table 6).

Periodic Announcement & Large Grid. Fig-ure 11 shows the CDF of link throughput with andwithout periodic announcement. The simulation uses

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the iterative algorithm and AEchos. We see that linkthroughputs are much improved with periodic announce-ments; the network throughput is improved by about47%. We also ran the same experiment for a 300× 300grid. In that case, not all nodes can hear each other sotheir local topology graph is not complete. Figure 11shows that this only slightly reduces link throughput,showing that our protocol can also work in larger areas.

8. RELATED WORKA wide range of work has explored techniques for tun-

ing transmit power and/or CCA threshold to improveperformance.

Transmit Power Tuning. The authors in [1] pro-pose to increase spatial reuse in dense wireless deploy-ments by reducing transmit power levels. The idea isthat this will reduce interference, as suggested by thecircle model. Our approach is based on a more realis-tic model of radio behavior. The authors also observedthat minimizing transmit power can lead to starvation.

In [4], the authors measure the performance of powercontrol in wireless networks, and identify three cases:1) overlapping, where aggregate throughput cannot beincreased by power control, 2) hidden-terminal, wherepower control can help to ensure fairness, 3) poten-tially disjoint, where power control can allow concurrenttransmission. Their results are consistent with our ob-servations. Their work did not consider the interactionwith the CCA threshold selection.

CCA Threshold Tuning. Echos [21] tunes theCCA threshold while using fixed power levels. The ideais that sources delay transmission if an ongoing trans-mission will interfere. In our simulation, we found thatEchos leads to starvation in some scenarios and we pro-pose an altruistic variant that avoids starvation at thecost of slightly reduced network throughput.

Joint CCA/Transmit Power Tuning. A few ef-forts, have explored joint CCA/transmit power tuningto maximize performance. However, existing effortshave assumed all nodes [15] or all nodes in a cell [17] usethe same configuration. These approaches work poorlywhen there is diversity in client-AP distances. Our pro-

tocol operates on a per-link basis and can handle suchdiversity both within a cell and between cells.

Both [17] and [7] tune the CCA by keeping the prod-uct of power level and CCA threshold a constant. Wefound that this approach, which we called alpha, didnot work well in certain scenarios where alpha need tobe set conservatively to avoid startvation, while wastingmany spatial reuse opportunities. Also, in the experi-ments in [17], the txpower and CCA thresholds are cal-culated offline. We deployed the protocol in an onlinefashion, and addressed several practical issues.

Practicality of Power Control. [20] studies thefeasibility of fine-grained power control based on RSSIreadings. The paper shows that under certain senarios,fine-grained power control is not feasible, and suggestsusing a more coarse-grained power control in these sce-narios. Their work is complimentary to our work andcan be added to our protocol. The authors in [5] studythe interactions between channel selection, user associ-ation, and power control. They observed that channelselection is important for power control to work. Also,since in their experiments, APs are carefully deployedto maximize coverage, power control is not as critical.

9. CONCLUSIONWe presented a practical interference-aware power

management protocol. In the protocol, each node in-serts signal strength information into its packets. Thisallows nodes to measure the path loss on nearby linksby monitoring traffic. Based on this information, nodesthen execute a power control algorithm that iterativelyincreases the number of concurrent transmissions thatcan take place. We introduce an altruistic version ofthe Echos CCA tuning algorithm to avoid starvation.We implemented the protocol in Linux and addressedseveral practical challenges. The experimental resultsfrom a 8-node platform shows that our protocol workswell in practice. Our evaluation of the protocol usingthe OPNET simulator shows that it improves networkthroughput by 22% to 87% compared with only tuningthe CCA threshold and using default transmit power,and by 2% to 67% compared with using minimum power

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levels. The protocol also improves performance for low-throughput links.

Two areas that we did not explicitly address are theprotocol’s failure modes and its support for rate adap-tation. An example failure mode is that the protocolmay react to RSSI fluctuations and create asymmet-ric links, where one of the sources thinks concurrenttransmission is possible and the other thinks it is im-possible. We believe it is possible to have nodes monitorachieved and expected throughputs and trigger recov-ery processes when expectations are not met. To handledata rates, our protocol must be adapted to produceconflict graphs for all considered data rates and initiatedata rate changes at a coarser time scale than in currentsystems. We leave these design issues to future work.

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