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Digital Object Identifier 10.1109/MVT.2013.2295067

Date of publication: 6 February 2014

Wireless World 2020

Future pervasive communication system requirements for two to three orders of magnitude capaci-ty improvement, flexible, fast

deployment, and cost/energy efficiency are expected to revolutionize the way we design and use wireless networks. From a network infrastructure perspec-tive, the emphasis is placed on achiev-ing ubiquitous, real-time high data rate communications “anytime-anywhere,” including at cell-edge, through Small Cell Network architectures and Hetero-geneous Cellular Networks (HetNets). From a pervasive systems’ perspective, the vision of the Internet of Things

suggests the integration between ubiq-uitous computing and wireless communi-cations targeting a reliable connectivity of things, i.e., computers, sensors, and everyday objects equipped with trans-ceivers. From a backhaul bandwidth, network resource sharing, and optimi-zation perspective, cloud-based pro-cessing and radio access network virtualization provide a revolutionary approach toward balancing the degree of centralization of physical and virtual resources management. In this article we analyze these three trends, present key technology enablers, and assess suitable performance merits in an effort

Radio Interface Challenges and Technology Enablers

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to set the scene for the wireless evolution in the era beyond 2020.

Wireless Evolution Trends and ChallengesOver the last decade, cellular networks have evolved from providers of ubiquitous coverage for voice- communication services to access ports available “anytime-anywhere” for high data rate, Internet-based data services. A three-order of magnitude increase in the supported data rates has been achieved, from several kb/s in second generation, (2G) general packet radio service (GPRS) to tens of Mb/s in the latest long-term evolution (LTE) systems. Nevertheless, even these fourth-generation (4G) data rates may soon prove inadequate, since the need for mobile data capacity is growing at an unprecedented rate. Recent market stud-ies conducted by global organizations [1], wireless forums [2], telecom companies [3], and operators [4], indicate that mobile data traffic has doubled every year. Projecting this demand a decade ahead, we are faced with the so-called 1000x data challenge or capacity crunch, justified by several trends:

■■ The increase in the number of mobile devices: by 2017 the average number of devices/holder is expect-ed to reach 1.4.

■■ The increased penetration of machine-to-machine (M2M) communications, as billions of low-data-rate devices with cellular connectivity are expected to be deployed and operated in the future.

■■ The increase in the usage of high-end portable devices such as tablets and smartphones. Each smartphone is expected to generate more than 2.7 GB of data per month (compared to today’s rate of 350 MB/month) by 2017.

■■ The shift to data-hungry mobile video services: cur-rently, half of mobile traffic involves video streaming, and in five years, it is expected to dominate the total load, comprising approximately two-thirds of mobile traffic.Existing radio access solutions, such as the latest LTE

releases, and their corresponding evolutionary paths (LTE-Advanced) may not be able to fulfill these demands, as the International Telecommunication Union (ITU) vi-sion work item (IMT 2020+) also suggests [5]. Traditional single-link physical layer (PHY) optimization techniques seem to have exhausted their potential in terms of achieving spectral efficiency levels [6]. Multicell multi-link cross-layer [PHY-medium access control (MAC)] techniques, such as coordinated multipoint (CoMP) or network multiple-input, multiple-output (MIMO) [7], based on on the concept of transforming all the interfer-ence into useful signal via cooperation, to enhance sys-tem capacity, target only cell-edge users, and suffer from scalability issues. Dense multitier heterogeneous net-work deployments or HetNets, empowered by interfer-ence coordination approaches [8] apply transmissions

orthogonalization in various domains (frequency, time, space, power, and code). While these are alternative measures to address the capacity crunch, they lack scal-ability and are fundamentally limited by the available bandwidth resources.

Existing cellular-based architectures were conceived for standalone working units with limited processing and intercommunication signaling capabilities. Support-ing access nodes cooperation requires complex proto-cols and flexible and efficient overhead signaling design. Hence, the actual benefits of the proposed technologies prove negligible compared to their theoretically predict-ed potential [9].

In the following sections of this article, each of the three envisioned system concept trends that are expect-ed to be the cornerstones of Wireless Evolution toward 2020 and beyond, namely heterogeneous network archi-tectures, M2M communications, and cloud-based radio access networks, are discussed. The expectations, major limiting factors, and promising technology enablers are addressed for each system concept trend.

Hierarchical Cooperation and Coordination in Heterogeneous Network ArchitecturesWhile hierarchical cell structures (HCSs), such as micro/pico/relay cells, and machine-type clusters, offer important capacity benefits, through the concept of an ever-densified access point (AP) deployment, a major drawback arises: a higher proportion of cell-edge users/devices. As the num-ber of users/devices increases, operators increase the den-sity of cells, implying that the proportion of cell-edge users increases accordingly. This problem traditionally leads to the decrease of the per-user capacity as the num-ber of devices increases. HCS is expected to offer more flexible deployment and higher capacities, but increasingly more information has to be shared between network ele-ments to maintain a certain quality of service (QoS).

Cooperation and CoordinationThe deployment of a large number of small cells as an overlay in a macrocell network comes with new techni-cal challenges in terms of managing the ever increasing interference levels. Recent research in information theory and wireless communication networking has indicated two technology candidates for overcoming the interference barriers:1) Cooperation, namely, the utilization of a network’s ele-

ment resources by another element. Cooperative

One Of the cOre Objectives Of the prOpOsed system cOncept is tO maintain the per-user capacity despite the dramatic increase in netwOrk density.

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relaying, e.g., has attracted considerable attention as it can provide many benefits including improved cover-age, lower transmit power, and/or higher network throughput. However, careful consideration is required since it can also increase the overall interference level—because of the additional transmissions—compared to noncooperative scenarios [10]. Cooperation basically involves the exchange of information in the data plane, and thus, uncontrollable cooperation may easily increase the overall interference level.

2) Coordination, which commonly assumes a centralized controller, involves a number of elements observing changes in the environment and deciding, either indi-vidually or jointly, to adjust their behavior to maxi-mize the individual as well as global performance benefit, under certain constraints. In the Third-Gener-ation Partnership Project (3GPP), CoMP describes a plethora of cooperation/coordination approaches, which vary in the amount of data and control informa-tion shared by the transceiver nodes through the backhaul network, and in the extent to which coordi-nation is applied (e.g., full network coordination ver-sus clustered-based approaches) [11]–[15]. In a recent approach based on interference alignment (IA) [16], interfering transmitters precode their signals to align in the unwanted user receive space, allowing these receivers to cancel more interferers than they other-wise could. This can be viewed as a cooperative approach, as transmitters go beyond maximizing the

performance of their own link to allow other users cancel interference as well.

Multiple Hierarchical Layer System ConceptTo satisfy the challenging performance requirements of scalability and adaptivity in future wireless networks, a multiple hierarchical layer coordination and coopera-tion system concept needs to be introduced, which, at the same time, minimizes the required information exchange between layers. One of the core objectives of such a system concept is to maintain the per-user capacity despite the dramatic increase in network den-sity, in contrast to conventional networks, where the per-user capacity decreases as the network density increases (see Figure 1).

Maintaining per-user capacity independently of the network density is an ambitious target. Recent research on cooperative MIMO has provided evidence that this goal may be achievable, at least under specific idealized assumptions [17]. Research shows that in particular sce-narios, when applying cooperation techniques, the system capacity may scale linearly with the number of devices. Ex-ploiting the gains of cooperative MIMO, near-global MIMO cooperation is achieved, and in this way, interference can be fully removed as a limitation. The capacity may further increase if multiple simultaneous transmissions among clusters/nodes are possible through coordination. For example, this means that two communication pairs do not interfere with each other. The above topology can be extended into a hierarchical one, where each cluster can continually be subdivided into more clusters.

Targeting Optimal Capacity Scaling Under Overhead Signaling LimitationsDistributed MIMO (D-MIMO) networks (also known as network MIMO) have attracted research interest for their potential to satisfy high data rate requirements in future wireless networks [18]. The generic system model is composed of a number of distributed APs that com-municate with a number of clients (served nodes), form-ing a virtual MIMO array, which mimics a conventional colocated MIMO system. Depending on the ratio between the number of APs and clients, as well as the kind of information that is shared among the network elements, several different techniques have been pro-posed for interference mitigation, including IA, dirty paper coding, and joint multiuser beamforming.

The signaling overhead increases significantly with the number of APs and clients due to the substantial amount of information that must be shared among the network elements for performing various operations, e.g., channel state information (CSI) estimation, time/frequency synchronization, and data sharing [19]–[21]. In this sense, when all the transmitters are communicat-ing during the data portion of the frame, the effective

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figure 1 the per-user capacity in (a) conventional networks and (b) future networks.

cOOrdinatiOn, which cOmmOnly assumes a centralized cOntrOller, invOlves a number Of elements Observing changes in the envirOnment and deciding, either individually Or jOintly, tO adjust their behaviOr tO maximize the individual as well as glObal perfOrmance benefit, under certain cOnstraints.

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sum rate, i.e., the volume of the ac-tual transmitted information bits, is reduced by a nonnegligible factor compared to the information–theo-retical sum rate [19]. In [21], it was shown that the limiting factor for such systems is not just the complex-ity and overhead signaling over the backbone wired network, but also the intrinsic limitations of channel estimations.

The optimal partitioning (Figure 2) of a D-MIMO system in terms of the maximum effective sum rate is stud-ied in [22], by formulating the parti-tioning optimization problem as an elegant knapsack problem and com-puting its exact solution. In Figure 3, the normalized sum rate (NSR) is de-picted as a function of the number of APs for the ideal case without overhead considerations along with the effective NSR achieved with the proposed optimal partitioning for various signal-to-noise ratio (SNR) and channel coherence time (CCT) values (i.e., degrees of variability of channel behavior with time). The optimal capacity scaling is a function of SNR and CCT. Linearity in capacity scaling is not generally maintained as the number of APs increases, due to the required overhead signaling. Nevertheless, per-formance considerably improves and approximates the ideal (linear) case as CCT increases.

New Multiple Access Concepts for Machine-to-Machine CommunicationsM2M communications over cellular networks introduce a number of technical challenges, mainly due to the large number of devices to be supported, small data transmis-sions, broad variety of QoS requirements, and the vast application range.

Most of the existing M2M applications use GPRS, e.g., the short message service, since it provides a manage-able, cost-efficient way for M2M deployment, as long as the number of devices remains relatively small. GPRS uses a packet-radio principle for carrying end users’ data, such as Internet protocol information to/from GPRS terminals and/or external networks. GPRS is designed for bursty traffic, which is typical traffic generated by several M2M applications. The selection of GPRS for supporting M2M applications and services is further argued by vendors and service providers for offering immediate M2M busi-ness entry, low-cost and convenient deployment, ubiq-uitous and international operability, roaming between mobile operators, all the benefits of a proven real-world tested technology, and being open and standardized. Nev-ertheless, GPRS has several limitations that raise serious concerns for its suitability for future M2M applications.

The spectral efficiency of a GPRS cell usually does not ex-ceed 100–150 kb/s/cell/MHz. If voice users are assumed to be active, the number of supported data users becomes limited (<30). Therefore, it is obvious that the GPRS capac-ity is not suitable for supporting the envisioned M2M ap-plications and services with thousands of devices per cell.

Limitations in Supporting M2M over LTELTE offers higher capacity and more flexible radio resource management (RRM) compared to third- generation packet data services such as high-speed pack-et access (HSPA) technologies. However, LTE has been designed for broadband applications, while most M2M applications transmit and receive small amounts of data,

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leading to an unreasonable ratio between payload and required control information and nonoptimized transmis-sion protocols. Important aspects, such as the need for low-energy devices or lower latencies, have to be consid-ered for M2M communications. To address these require-ments, 3GPP introduced a number of machine-type communication (MTC) studies and work items [23].

Packet scheduling is a key RRM mechanism for opti-mizing system resource utilization while guaranteeing QoS requirements. In LTE, to optimally allocate the physical re-source blocks to the users’ equipment (UEs) and/or MTC devices (MTCDs), the scheduler should exploit channel and traffic information in a fast time-scale. Hence, associated uplink (UL) and downlink signaling channels for carrying the channel quality, traffic, and allocation information are necessary. However, the unique characteristics of M2M traf-fic, such as the large number of devices and the bursty low-rate load nature, perplex scheduling as both complexity and signaling are dramatically increased. According to the 3GPP specifications, up to ten UEs (or MTCDs) may be supported

in a single subframe. Hence, the support of hundreds of MTCDs demanding simultaneous access to the shared chan-nel, as envisioned for future M2M scenarios, is not feasible. LTE also provides a random access transport channel in the UL, which may carry UL scheduling requests. This is done through a contention-based mechanism providing access to one of the 64 available orthogonal sequences per cell. If more messages need to be transmitted, collisions will occur.

In addition to the requirement to support a large number of (often small data) M2M devices, another major technical challenge is the vast dynamic range of M2M QoS charac-teristics. A set of nine QoS classes have been prescribed in 3GPP, classifying services (or radio bearers) based on the resource type [guaranteed bit rate (GBR)/non-GBR], prior-ity order, packet delay budget, and packet loss rate charac-teristics. In M2M, however, the scheduling entities have to deal with extremely diverse QoS criteria. For example, de-lay tolerance may span from tens of milliseconds (vehicle collisions) to several minutes (environmental monitoring). In addition, the error rate tolerances may scale accordingly.

Enabling M2M in Wireless World 2020Several approaches have been proposed to address existing system limitations in supporting the envisioned M2M communication scenarios, including group-based scheduling for overhead signaling reduction and semi-persistent scheduling of variable time granularity.

The issue of QoS class definition for MTCDs and the need to introduce new MTCD-QoS class identifiers (QCIs) is of para-mount importance for the adoption and realization of the M2M vision. This is because the number and range of each class must be care-fully chosen to match diverse traffic characteristics. In contrast to current policies, a dynamic formation of QoS classes, according to a particular ap-plication scenario, may be more ap-propriate for M2M communications given that the MTCDs topology and individual characteristics are not a priori known [24].

An analytical model that relates the scheduling period, the average offered traffic load, and QoS require-ments, in terms of packet delay and dropped packet rate was proposed in [25]. The proposed model may be used for tuning the scheduling deci-sions to meet the probabilistic QoS targets, and estimating the minimum bandwidth to be reserved for differ-ent M2M loads.

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Fixed grant access and its queue-awarevariation exhibit the same performance(5% delay CCDF point ~= 40 TTIs) forscheduled access period 20 and 45 TTIsaccordingly.This means we can lower the schedulingperiod (and thus free resource blocks)by a factor of 45/20 and achieve the sameQoS performance, at the expense ofincreased overhead to report machinesbuffer status to the base station.

figure 4 the scheduled access for m2m over lte frames: data versus overhead signaling resource consumption tradeoff.

tO satisfy the challenging perfOrmance requirements Of scalability and adaptivity in future wireless netwOrks, a multiple hierarchical layer cOOrdinatiOn and cOOperatiOn system cOncept needs tO be intrOduced.

march 2014 | ieee vehicular technology magazine ||| 51

In Figure 4, the delay outage performance is illustrat-ed for the case of fixed periodic scheduling of M2M devic-es, and for the proposed extension of this low complexity scheme [26] that allows for queue-aware decisions. It is shown that queue-awareness can lower the scheduling period (and, therefore, free resource blocks) at the ex-pense of signaling allocated to report buffer status.

Enabling M2M in future wireless systems may require the consideration of both an evolutionary path, target-ing the reduction of overhead signaling and the dynamic formation of QoS classes, and a revolutionary path, em-phasizing the need to rethink the way we design wireless networks in the era beyond the Internet of Things.

Novel physical layer solutions realizing the aggrega-tion of multiple data streams from various devices into one resource block must be developed, as well as novel hybrid multiple-access schemes, combining the merits of contention-based and scheduled access. The former requires flexibility and adaptivity in the design of data structure and resource management strategies. The lat-ter calls for the introduction of a hybrid protocol, which decides on the access mode to be used based on the trad-eoff between the expected throughput and protocol com-plexity. Such a hybrid contention/reservation protocol was proposed in [27], where the expected throughput is predicted by exploiting an analytical framework ground-ed on the queueing theory.

A New Cell-Less System Concept Enabled by Cloud-Based Radio Access ArchitecturesThe applicability of cooperation and coordination technol-ogies and the feasibility of adopting M2M in future wireless communication seems to be inherently limited by the fun-damental cell-centric system concept, mainly relying on the design principle of standalone working units with limit-ed processing and signaling capabilities. The ambitious objective of sustaining per-user capacity for an increasing number of served nodes, by efficiently managing interfer-ence, calls for a revolutionary system concept that has to be based on a cell-less network architecture controlled by a super-centralized entity. In such a context, cell boundar-ies collapse and a large number of low-cost infrastructure access-providing units are deployed practically every-where. These units are connected through a backhaul net-work to a cloud-based infrastructure, where D-MIMO communication processing takes place. The ingredients of this novel system concept include:

■■ a number of infrastructure access nodes, at least as many as the number of served devices

■■ global and centralized data communications processing

■■ user-centric optimization enabled by a large number of spatial degrees of freedom

■■ a novel system architecture, composed by a physical and a virtual substrate jointly designed and optimized;

the physical substrate involves all wireless physical entities, whereas the virtual substrate refers to the management of all processing modules residing in the cloud, to which physical substrate elements are dynamically mapped.

Universal Resource Management: When Physical Layer and Medium Access Control Boundaries CollapseBreaking the interference barrier in future wireless net-works cannot solely rely on a single wireless innovation, such as the adoption of partitioned D-MIMO, because of two important factors:1) the prohibitive amount of overhead signaling involved2) the complexity of the processing involved as the num-

ber of nodes increases dramatically.The cell-less wireless vision introduces a user-centric

system concept, where every served node enjoys the universal bandwidth availability as if it were alone in the network, provided that there are enough (spatial) degrees of freedom, adequate backhaul bandwidth, over the air signaling for CSI, and processing capability to sup-port this D-MIMO in real time. The availability of enough spatial degrees of freedom relies on the assumption of ultradense networks, where a large number of APs, at least as many as the number of nodes to be served, and potentially of low-complexity/high-energy efficiency, are deployed. Coordination and cooperation are optimally applied to maximize performance and minimize backhaul and feedback signaling overhead. To keep up with large-scale MIMO processing and optimization involved, wire-less engineering needs to embrace and integrate two key scientific/technological concepts:

■■ centralized processing cloud-based virtualized radio access network architecture

■■ large and complex system optimization.The successful integration of these important pillars

of cloud-based future wireless system architecture heav-ily depends on two different types of mapping: the effi-cient mapping of physical (wireless) to virtual (cloud) resources, and the accurate formulation/representation of wireless network assumptions by means of large and complex systems optimization constraints.

From a wireless engineering perspective, addressing Universal Resources Management (URM), i.e., the selec-tion of the (sub)set of serving and served nodes and the

nOvel physical layer sOlutiOns realizing the aggregatiOn Of multiple data streams frOm variOus devices intO One resOurce blOck must be develOped, as well as nOvel hybrid multiple-access schemes, cOmbining the merits Of cOntentiOn-based and scheduled access.

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coordination and cooperation scheme to be employed by means of cloud-based processing, and large system opti-mization, in an ultradense deployment scenario, where the AP nodes are at least as many as the number of nodes to be served, involves three important research challenges:

■■ the characterization of signal-to-interference-and-noise-ratio (SINR) distribution as the network density grows

■■ the optimal partitioning of the D-MIMO problem involved in order to optimize effective throughput

■■ the solution of the URM problem dynamically and adaptively.

URM in a Coordination-Only ScenarioTo get some insight into the fundamental benefits and the expected performance gains of URM, the case of coordination only (without cooperation) was studied first. A large number of serving nodes, at least as many as the nodes to be served, is considered in a cell-less scenario. The objective of URM is to select the optimal set of serving-served nodes pairs to share all network resources (time, frequency, etc.) by exploiting the avail-able spatial degrees of freedom and to optimally allocate transmitted power. Optimality needs to be considered not only with respect to the total network rate and the rate per served node, but also in terms of backhaul, sig-naling, and computational complexity requirements.

In Figure 5, the performance of a large-scale network of simultaneously connected pairs of serving and served nodes (coordination-only case) is illustrated for an in-creasing number of nodes in terms of network rate and per served node rate.

In Figure 6, the performance gains of URM for coordi-nation are depicted for an increasing number of serving nodes in two different cases: fixed power allocation and universal power optimization. In both cases, connected pairs selection is based on a simple best serving node channel criterion. URM leverages multiple serving nodes diversity gains. It is expected that advances in large sys-tem optimization and cloud-enabled virtualization will improve this scaling.

Wireless World 2020—Expectations and ChallengesIn this article, the most important radio interface chal-lenges and promising technology enablers that are expected to shape the wireless future beyond 2020 were presented. On one hand, the requirement to sustain per user/served node capacity, as the number of nodes increases, with the advent of the ever-densified small-cell and HCS network, and the need to support billions of machine-type communications, introduce the quest for optimal (linear) capacity scaling. On the other hand, optimal capacity scaling may be achievable but can only be meaningful under reasonable backhaul bandwidth, overhead signaling, and computational complexity assumptions.

Realizing this vision may involve three paradigm shifts, leading to novel wireless system concepts:

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the Objective Of urm is tO select the Optimal set Of serving-served nOdes pairs tO share all netwOrk resOurces (time, frequency, etc.) by explOiting the available spatial degrees Of freedOm and tO Optimally allOcate transmitted pOwer.

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1) a multiple hierarchical layer system concept, relying on the efficient combination of coordination and cooperation to achieve optimal capacity scaling

2) flexible and adaptive multiple access schemes to sup-port large numbers of machines with various QoS requirements, relying on the efficient combination of scheduled and random access principles

3) a cell-less system concept where physical layer and medium access control boundaries collapse in a user-centric approach, enabled by the integration of network MIMO, large and complex system optimization, and cloud-based radio access network virtualization.

AcknowledgmentsThe author wishes to thank the members of her research group, Dr. Antonis Gotsis, Dr. Athanasios Lioumpas, and Dr. Petros Bithas, for providing some of the simulation results and for many inspiring discussions.

Author InformationAngeliki Alexiou (aalexiou@ieee.org) received her diploma in electrical and computer engineering from the National Technical University of Athens in 1994 and the Ph.D. degree in electrical engineering from Imperial Col-lege of Science, Technology, and Medicine, University of London, in 2000. Since May 2009, she has been an assis-tant professor at the Department of Digital Systems, University of Piraeus, Greece, where she conducts research and teaches undergraduate and postgraduate courses in the fields of broadband communications and advanced wireless technologies. She is with Alcatel-Lucent, in Swindon, United Kingdom, first as a member of technical staff (January 1999–February 2006) and later as a technical manager (March 2006–April 2009). Her current research interests include multiple antenna systems and multihop communications, advanced signal processing, and efficient RRM for wireless systems beyond IMT-advanced and M2M communications. She is chair of the Working Group on Radio Communication Technologies of the Wireless World Research Forum. She is a Member of the IEEE, the IET, and the Technical Chamber of Greece.

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