MIMO

• use of multiple antennas at both thetransmitter and receiver to improvecommunication performance

• the terms input and output refer to the radiochannel carrying the signal, not to thedevices having antennas.

• offers significant increases in data throughputand link range without additional bandwidth orincreased transmit power.

• It achieves this goal by spreading the sametotal transmit power over theantennas to achieve an array gain thatimproves the spectral efficiency (more bits persecond per hertz of bandwidth)

• and/or to achieve a diversity gain that improvesthe link reliability (reduced fading)

Spatial multiplexing

• (seen abbreviated SM or SMX) is a

transmission technique in MIMOwireless communication to transmitindependent and separately encoded datasignals, so-called streams, from each of themultiple transmit antennas.

Functions of MIMO

• Precoding• spatial multiplexing or SM, and• diversity coding.

Forms of MIMO

• Single-user MIMO• Multi-user MIMO• Enhanced multiuser MIMO

Principal single-user MIMO techniques

• Bell Laboratories Layered Space-Time (BLAST),Gerard. J. Foschini (1996)

• Per Antenna Rate Control (PARC), Varanasi,Guess (1998), Chung, Huang, Lozano (2001)

• Selective Per Antenna Rate Control (SPARC),Ericsson (2004)

MIMO testing

• MIMO signal testing focuses first on the transmitter/receiver system. The random phases of thesub-carrier signals can produce instantaneous power levels that cause the amplifier to compress,momentarily causing distortion and ultimately symbol errors. Signals with a high PAR (peak-to-average ratio) can cause amplifiers to compress unpredictably during transmission. OFDM signalsare very dynamic and compression problems can be hard to detect because of their noise-likenature.[17]

• Knowing the quality of the signal channel is also critical. A channel emulator can simulate how adevice performs at the cell edge, can add noise or can simulate what the channel looks like atspeed. To fully qualify the performance of a receiver, a calibrated transmitter, such as a vectorsignal generator (VSG), and channel emulator can be used to test the receiver under a variety ofdifferent conditions. Conversely, the transmitter's performance under a number of differentconditions can be verified using a channel emulator and a calibrated receiver, such as a vectorsignal analyzer (VSA).

• Understanding the channel allows for manipulation of the phase and amplitude of each transmitterin order to form a beam. To correctly form a beam, the transmitter needs to understand thecharacteristics of the channel. This process is called channel sounding or channel estimation. Aknown signal is sent to the mobile device that enables it to build a picture of the channelenvironment. The mobile device sends back the channel characteristics to the transmitter. Thetransmitter can then apply the correct phase and amplitude adjustments to form a beam directedat the mobile device. This is called a closed-loop MIMO system. For beamforming, it is required toadjust the phases and amplitude of each transmitter. In a beamformer optimized for spatialdiversity or spatial multiplexing, each antenna element simultaneously transmits a weightedcombination of two data symbols

channel state information (CSI) refersto known channel properties of a

communication link• Instantaneous CSI (or short-term CSI) means that the current channel conditions are known, which

can be viewed as knowing the impulse response of a digital filter. This gives an opportunity to adaptthe transmitted signal to the impulse response and thereby optimize the received signal for spatialmultiplexing or to achieve low bit error rates.

• Statistical CSI (or long-term CSI) means that a statistical characterization of the channel is known.This description can include, for example, the type of fading distribution, the average channel gain,the line-of-sight component, and the spatial correlation. As with instantaneous CSI, this informationcan be used for transmission optimization.

• The CSI acquisition is practically limited by how fast the channel conditions are changing. In fastfading systems where channel conditions vary rapidly under the transmission of a singleinformation symbol, only statistical CSI is reasonable. On the other hand, in slow fading systemsinstantaneous CSI can be estimated with reasonable accuracy and used for transmission adaptationfor some time before being outdated.

• In practical systems, the available CSI often lies in between these two levels; instantaneous CSI withsome estimation/quantization error is combined with statistical information.

Estimation of CSI

• Since the channel conditions vary,instantaneous CSI needs to be estimated on ashort-term basis. A popular approach is so-called training sequence (or pilot sequence),where a known signal is transmitted and thechannel matrix is estimated using thecombined knowledge of the transmitted andreceived signal.

– Least-square estimation– MMSE estimation

Least-square estimation

• If the channel and noise distributions areunknown, then the least-square estimator (also knownas the minimum-variance unbiased estimator) is[4]

• where tr denotes the trace. The error is minimized when isa scaled identity matrix. This can only be achieved when isequal to (or larger than) the number of transmit antennas.The simplest example of an optimal training matrix is toselect as a (scaled) identity matrix of the same size that thenumber of transmit antennas.

MMSE estimation

• If the channel and noise distributions are known, then this a priori information can be exploited todecrease the estimation error. This approach is known as Bayesian estimation and for Rayleighfading channels it exploits that

• and is minimized by a training matrix that in general can only be derived through numericaloptimization. But there exist heuristic solutions with good performance based on waterfilling. Asopposed to least-square estimation, the estimation error for spatially correlated channels can beminimized even if is smaller than the number of transmit antennas.[2] Thus, MMSE estimation canboth decrease the estimation error and shorten the required training sequence. It needs howeveradditionally the knowledge of the channel correlation matrix and noise correlation matrix . Inabsence of an accurate knowledge of these correlation matrices, robust choices need to be madeto avoid MSE degradation.

Data transmission

Protocol layers and sub-topics

• Courses and textbooks in the field of data transmission typically deal withthe following OSI model protocol layers and topics:

• Layer 1, the physical layer:– Channel coding including

• Digital modulation schemes• Line coding schemes• Forward error correction (FEC) codes

– Bit synchronization– Multiplexing– Equalization– Channel models

• Layer 2, the data link layer: Channel access schemes, media access control(MAC)

• Packet mode communication and Frame synchronization• Error detection and automatic repeat request (ARQ)• Flow control• Layer 6, the presentation layer: Source coding (digitization and data

compression), and information theory.• Cryptography (may occur at any layer)

Error-correcting codes

• are usually distinguished between convolutional codes andblock codes:

• Convolutional codes are processed on a bit-by-bit basis. Theyare particularly suitable for implementation in hardware, andthe Viterbi decoder allows optimal decoding.

• Block codes are processed on a block-by-block basis. Earlyexamples of block codes are repetition codes, Hamming codesand multidimensional parity-check codes. They were followedby a number of efficient codes, Reed–Solomon codes beingthe most notable due to their current widespread use. Turbocodes and low-density parity-check codes (LDPC) are relativelynew constructions that can provide almost optimal efficiency.

Code rate• In telecommunication and information theory, the code rate (or

information rate[1]) of a forward error correction code is the proportion ofthe data-stream that is useful (non-redundant). That is, if the code rate isk/n, for every k bits of useful information, the coder generates totally nbits of data, of which n-k are redundant.

• If R is the gross bitrate or data signalling rate (inclusive of redundant errorcoding), the net bitrate (the useful bit rate exclusive of error-correctioncodes) is ≤ R•k/n.

• For example: The code rate of a convolutional code may typically be 1/2,2/3, 3/4, 5/6, 7/8, etc., corresponding to that one redundant bit is insertedafter every single, second, third, etc., bit. The code rate of the ReedSolomon block code denoted RS(204,188) is 188/204, corresponding tothat 204 - 188 = 16 redundant bytes are added to each block of 188 byteof useful information.

• A few error correction codes do not have a fixed code rate -- ratelesserasure codes.

http://4g-lte-world.blogspot.in/2012/12/transport-block-size-code-rate-protocol.html

• In simple words, code rate can be defined as how effectively data can betransmitted in 1ms transport block or in other words, it is the ratio ofactual amount of bits transmitted to the maximum amount of bits thatcould be transmitted in one transport block

code rate = (TBS + CRC) / (RE x Bits per RE)

whereTBS = Transport block size as we calculated from Table 7.1.7.2.1-1CRC = Cyclic redundancy check i.e. Number of bits appended for errordetectionRE = Resource elements assigned to PDSCH or PUSCHBits per RE = Modulation scheme used

Type of Errors

There are two main types of errors intransmissions:1. Single bit error2. Burst error

Equalization

• Equalization (British: equalisation) is theprocess of adjusting the balance betweenfrequency components within an electronicsignal

space–time code• A space–time code (STC) is a method employed to improve the reliability

of data transmission in wireless communication systems using multipletransmit antennas. STCs rely on transmitting multiple, redundant copies ofa data stream to the receiver in the hope that at least some of them maysurvive the physical path between transmission and reception in a goodenough state to allow reliable decoding.

• Space time codes may be split into two main types:• Space–time trellis codes (STTCs)[1] distribute a trellis code over multiple

antennas and multiple time-slots and provide both coding gain anddiversity gain.

• Space–time block codes (STBCs)[2][3] act on a block of data at once(similarly to block codes) and also provide coding gain and diversity gain.

Low-density parity-check (LDPC)• Low-density parity-check (LDPC) codes are a class of recently re-discovered highly

efficient linear block codes. They can provide performance very close to thechannel capacity (the theoretical maximum) using an iterated soft-decisiondecoding approach, at linear time complexity in terms of their block length.Practical implementations can draw heavily from the use of parallelism.

• LDPC codes were first introduced by Robert G. Gallager in his PhD thesis in 1960,but due to the computational effort in implementing encoder and decoder and theintroduction of Reed–Solomon codes, they were mostly ignored until recently.

• LDPC codes are now used in many recent high-speed communication standards,such as DVB-S2 (Digital video broadcasting), WiMAX (IEEE 802.16e standard formicrowave communications), High-Speed Wireless LAN (IEEE 802.11n)[citation needed],10GBase-T Ethernet (802.3an) and G.hn/G.9960 (ITU-T Standard for networkingover power lines, phone lines and coaxial cable). Other LDPC codes arestandardized for wireless communication standards within 3GPP MBMS (seefountain codes).

4x4 MIMO with multiplexing of two spatial streams

In this 4 x 4 MIMO example, example, the 4 spatial streams are not equally modulated. Streams 1and 3 are QPSK modulated (per subcarrier -OFDM) and streams 2 and 4 are 64QAM modulated

(per subcarrier -OFDM).