You are here

MIMO Transceivers

Research topics:

                                                                                                            MIMO Classification

Figure 1. Classification of MIMO Techniques.

[1] Near-Capacity Wireless Transceivers and Cooperative Communications in the MIMO Era: Evolution of Standards, Waveform Design, and Future Perspectives
[2] A Universal Space-Time Architecture for Multiple-Antenna Aided Systems.
[3] MIMO-Aided Near-Capacity Turbo Transceivers: Taxonomy and Performance versus Complexity Communications.
[4] Spatial Modulation for Generalized MIMO: Challenges, Opportunities and Implementation.
[5] Multifunctional MIMO systems: A combined diversity and multiplexing design perspective.
[6] L. Hanzo, O. Alamri, M. El-Hajjar and N. Wu, Near-Capacity Multi-Functional MIMO Systems: Sphere-Packing, Iterative Detection and Cooperation, John Wiley & Sons, May 2009.
[7] L. Hanzo, T. Liew, and B. Yeap, Turbo Coding, Turbo Equalisation and Space-Time Coding for Transmission over Fading Channels, Wiley-IEEE Press, 2003.
[More Publications]

Multiple-Input Multiple-Output (MIMO) techniques can be categorised as diversity techniques, multiplexing schemes, beamforming as well as multi-functional MIMO arrangements, as shown in Figure 1. Spatial diversity can be attained by employing multiple antennas at the transmitter or the receiver. Multiple antennas can be used for transmitting and receiving appropriately encoded replicas of the same information sequence in order to achieve diversity and hence to obtain an improved BER performance. In the context of diversity techniques, the antennas are spaced as far apart as possible, so that the signals transmitted to or received by the different antennas experience independent fading and hence we attain the highest possible diversity gain.

A simple spatial diversity technique, which does not involve any loss of bandwidth, is constituted by the employment of multiple antennas at the receiver, where several techniques can be employed for combining the independently fading signal replicas, including Maximum Ratio Combining (MRC), Equal Gain Combining (EGC) and Selection Combining (SC), as shown in Figure 1.

Alamouti proposed a transmit diversity technique using two transmit antennas, whose key advantage was the employment of low-complexity single-receive-antenna-based detection, which avoids the more complex joint detection of multiple symbols. Alamouti's achievement inspired Tarokh et. al. to generalise the concept of transmit diversity schemes to more than two transmit antennas, hence conceiving the generalised concept of Space-Time Block Codes (STBC). The family of STBCs is capable of attaining the same diversity gain as Space-Time Trellis Codes (STTC) at a lower decoding complexity, when employing the same number of transmit antennas. Furthermore, inspired by the philosophy of STBCs, Hochwald et. al. proposed the transmit diversity concept known as Space-Time Spreading (STS) for the downlink of Wideband Code Division Multiple Access (WCDMA) that is capable of achieving the highest possible transmit diversity gain.

Several high-rate space-time transmission schemes having a normalised rate higher than unity have been proposed in the literature. For example, high-rate space-time codes that are linear both in space and time, namely the family of the so-called Linear Dispersion Codes (LDC), was proposed by Hassibi et. al.. LDCs strike a flexible trade-off between emulating space-time coding and/or spatial multiplexing.

STBCs and STTCs are capable of providing diversity gains for the sake of improving the achievable system performance. However, this BER performance improvement is often achieved at the expense of a rate-loss, since STBCs and STTCs may result in a throughput-loss compared to single-antenna-aided systems. As a design alternative, a specific class of MIMO systems was designed for improving the attainable spectral efficiency of the system by transmitting different signal streams independently over each of the transmit antennas, hence resulting in a multiplexing gain. This class of MIMOs subsumes Bell Labs' Layered Space-Time (BLAST) scheme proposed by Wolniansky et. al.. The BLAST scheme aims for increasing the system throughput in terms of the number of bits per symbol that can be transmitted in a given bandwidth at a given integrity.

In contrast to the family of BLAST schemes, where multiple antennas are activated by a single user for increasing the user's throughput, Space Division Multiple Access (SDMA) employs multiple antennas for the sake of supporting multiple users. SDMA exploits the unique user-specific Channel Impulse Response (CIR) of the different users for separating their received signals. On the other hand, in beamforming arrangements typically $\lambda/2$-spaced antenna elements are used for the sake of creating a spatially selective transmitter/receiver beam, where $\lambda$ represents the carrier's wavelength. Beamforming is employed for providing a directional gain by mitigating the effects of various interfering signals, provided that they arrive from sufficiently different directions. Additionally, beamforming is capable of suppressing the effects of co-channel interference, hence allowing the system to support multiple users by angularly separating them. Again, this angular separation becomes feasible only on condition, if the corresponding users are separable in terms of the angle of arrival of their beams.

Finally, multi-functional MIMOs, as the terminology suggests, combine the benefits of several MIMO schemes including diversity gains, multiplexing gains as well as beamforming gains as shown in Figure 1.

Multifunctional MIMO: Spatial Divison Multiplexing / Multiple Access, Space Time Spreading and Beamforming
Prof Lajos Hanzo, Prof. Lie-Liang Yang, Dr. Bin Hu, Dr. Mohammed El-Hajjar,
[1] Layered Steered Space–Time-Spreading-Aided Generalized MC DS-CDMA.
[2] Layered steered space-time codes using multi-dimensional sphere packing modulation.
[3] Coherent and Differential Downlink Space-Time Steering Aided Generalised Multicarrier DS-CDMA. [More Publications]

We present a novel trifunctional multiple-input–multiple-output (MIMO) scheme that intrinsically amalgamates space–time spreading (STS) to achieve a diversity gain and a Vertical Bell Labs layered space–time (V-BLAST) scheme to attain a multiplexing gain in the context of generalized multi-carrier direct-sequence code-division multiple access (MC DS-CDMA), as well as beamforming. Furthermore, the proposed system employs both time- and frequency-domain spreading to increase the number of users,which is also combined with a user-grouping technique to reduce the effects of multi-user interference.Further system performance improvements can be attained by serially concatenating our proposed scheme with an outer code that is amalgamated with a unity-rate code for the sake of improving the achievable decoding convergence behaviour of the proposed system, which is evaluated with the aid of extrinsic information transfer charts. We also propose a novel logarithmic likelihood ratio (LLR) post-processing technique to improve the iteratively detected system’s performance. Explicitly, the proposed system can attain a second-order spatial diversity gain and a frequency diversity gain of order V , where V is the number of subcarriers. Additionally, the proposed system attains a beamforming gain and a multiplexing gain that is twice that of a single-input–single-output system. Furthermore, after I = 10 decoding iterations and employing an interleaver depth of Dint = 160 000 bits, a time-domain spreading factor of Ne = 4, and V = 4 subcarriers, the overloaded system supporting K = 8 users requires an Eb/N0 that is only about 0.45 dB higher than the single-user system.

Differential MIMO: Differential Linear Dispersion Code, Space Time Shift Keying
Prof Lajos Hanzo, Dr. Mohammed El-Hajjar, Dr. Nan Wu, Dr. Shinya Sugiura
[1] Near-Capacity Irregular-Convolutional-Coding-Aided Irregular Precoded Linear Dispersion Codes.
[2] Coherent and Diffrential Space-Time Shift-Keying:A Dispersion Matrix Approach.
[3] Generalised Space-Time Shift Keying Designed for Flexible Diversity, Multiplexing and Complexity Tradeoffs.[More Publications]

The set of linear dispersion codes (LDCs), which was first proposed by Hassibi and Hochwald, constitutes a wide-ranging class of space-time codes exhibiting diverse characteristics. Hence, this family encompasses numerous existing schemes, providing a natural framework in which such design problems can be posed. The revolutionary concept of LDCs invokes a matrix-based linear modulation framework, where each space time transmission matrix is generated by a linear combination of so-called “dispersion” matrices used to disperse or map the symbols to the transmit antennas, where the weights of the constituent matrices are determined by the transmitted symbols. The set of dispersion matrices can be optimized according to different objectives.The dispersion matrices were originally designed for maximizing the continuous input continuous-output memoryless channel capacity of the MIMO system. However, the LDCs did not necessarily guarantee a low bit error ratio (BER). On the other hand, LDCs can also be optimized using the determinant criterion using the beneficial techniques of the Golden codes, where a non vanishing determinant is promised. By contrast, in this paper, we propose a novel method of optimizing the set of dispersion matrices for the sake of maximizing the discrete-input continuous-output memoryless channel (DCMC) capacity.

Serial concatenated codes (SCCs) are capable of attaining a vanishing BER, while maintaining a manageable decoding complexity. It has been demonstrated that SCCs may operate near the MIMO channel’s capacity at certain SNR values. However, the distance to the capacity is still quite significant, particularly when higher order modulation schemes are employed. The authors proposed to adopt irregular convolutional codes (IRCCs) as the outer channel code of an SCC, since IRCCs exhibit flexible extrinsic information transfer (EXIT) chart characteristics. Unfortunately, these schemes may fail to approach the capacity at different SNRs, where the inner and outer codes fail to create an open EXIT tunnel. Since LDCs have the ability to approach the MIMO channel’s capacity and to provide flexible configurations, the novel contribution of this paper is the joint design of irregular LDCs as the inner code and the IRCCs as the outer code of an SCC scheme to approach the capacity for a wide SNR range.

More explicitly, motivated by the aforementioned flexibility ofthe irregular outer code design philosophy, we circumvent the IRCC-related outer code limitations by proposing irregular precoded LDCs (IR-PLDCs) as the inner code of the SCCs and serially concatenate the resultant IR-PLDCs with the outer IRCCs to operate close to the MIMO channel’s capacity across a wide SNR region, while maintaining a vanishing BER. The rationale of using a unity-rate precoder in an SCC scheme is that it allows us to create an infinite impulse response system at the cost of a low implementation complexity, which has the benefit of improving the extrinsic information exchange among the component codes at the receiver.

Low-Complexity Spatial Modulation and Space-Time Shift Keying
[1] Spatial modulation and space-time shift keying: Optimal performance at a reduced detection complexity.
[2] Coherent and differential space-time shift keying: A dispersion matrix approach.
[3] A universal space-time architecture for multiple-antenna aided systems.
[4] Spatial modulation for generalized MIMO: Challenges, opportunities and implementation, Proceedings of the IEEE. [More Publications]

Multiple-Input Multiple-Output (MIMO) schemes are capable of providing wireless communication systems either with an increased capacity as in the vertical Bell Labs layered space-time V-BLAST scheme proposed by Foschini and/or with an improved diversity gain as in the space-time block code advocated by Alamouti. However, full-search-based Maximum Likelihood(ML) MIMO detection may impose an excessive complexity in turbo detected schemes.Fig. 2(a) portrays an example of the V-BLAST scheme, where M antennas simultaneously transmit M L-PSK or L-QAM symbols, while the receiver is equipped with N receive antennas. It is well-known that although the M L-PSK or L-QAM symbols are separately encoded at the transmitter, they have to be jointly detected in order to approach the maximum attainable capacity. Otherwise, a performance loss is inevitable for linear receivers (e.g. Zero Forcing, MMSE, etc.), which attempt to separate the parallel V-BLAST streams. We note that the V-BLAST ML detection complexity is of the order of O(LM).

                                                                                                            V-BLAST Example

Figure 2. Example of V-BLAST(M,N)-LPSK/QAM and SM(M,N)-LPSK/QAM.

As a remedy, Spatial Modulation (SM) was proposed by Mesleh et. al., where a single one outof the M transmit antennas is activated to transmit a single L-PSK/QAM symbol, so that a single-antenna-based detector may be invoked at the receiver. Fig. 2(b) demonstrates an example of a SM scheme, where log2(M) source bits are assigned to activate a transmit antenna,while log2(L) source bits are assigned to modulate an L-PSK/QAM symbol. Due to the fact that only a single transmit antenna is activated at any instant, there is no inter-antenna interference, i.e.a decorrelating operation Z=Y HH does not impose any performance loss, where Y and H refer to the received signal matrix and fading channel matrix, respectively. Hence, Z may be directly invoked for detecting the transmitted symbols. In summary, the log2(M) and log2(L) bits which are independently encoded at the transmitter may be separately detected without any performance loss. In other words, the SM detection complexity may be reduced from O(M.L) to up to O(M+L).

                                                                                                            MIMO Classification

Figure 3. An example of STSK(M,N,T,Q)-LPSK/QAM scheme.

Furthermore, in order to benefit from a diversity gain, we proposed Space-Time Shift Keying (STSK),where one out of Q dispersion matrices can be activated to disperse a single L-PSK/QAM symbol to M transmit antennas and T time-slots. We have demonstrated that a low-complexity SM detector may be invoked for STSK detection. Fig. 3 portrays the schematics of the STSK scheme.We demonstrated that MIMO schemes, including V-BLAST, SM, Space-Time Block Code (STBC) and STSK associated with the same throughput perform rather similarly with the aid of channel coding.However, SM and STSK may achieve their full ML detection capability at a substantially reduced complexity, which offers them an appealing advantage in a variety of realistic large-scale MIMO systems.

Multicarrier-Aided STSK Schemes
[1] A universal space-time architecture for multiple-antenna aided systems.
[2] Coherent and differential space-time shift keying: A dispersion matrix approach.
[3] OFDMA/SC-FDMA aided space-time shift keying for dispersive multiuser scenarios.
[4] Successive-relaying-aided decode-and-forward coherent versus noncoherent cooperative multicarrier space-time shift keying.
[5] Iterative soft multiple-symbol differential sphere decoding aided multicarrier space-time shift keying.
[More Publications]

Recently, we have developed the concept of space-time shift keying (STSK), which drew its motivation from the extremely simple architecture of spatial modulation (SM) proposed by Mesleh et. al. and complemented the simplicity of SM and SSK by the rate versus diversity trade offs provided by the linear dispersion codes (LDCs) of Hochwald and Hassibi. STSK extends the pure spatial-domain antenna activation concept of SM/SSK schemes to both the spatial and time dimensions,while maintaining a low decoding complexity. More specifically, the idea is to rely on beneficial dispersion-matrix activation, rather than on the simple antenna activation process of SM/SSK in addition to the conventional L-PSK or L-QAM signalling, which enabled us to benefit both from substantial diversity as well as multiplexing gains. However, most of the previous STSK studies were focused on narrowband scenarios,rather than on realistic wideband scenarios. Since wireless channels are typically dispersive in nature, it is necessary to circumvent the dispersive channel effects. To be specific, we proposed OFDM, MC-CDMA,OFDMA/SC-FDMA-aided multicarrier STSK for transmission over realistic dispersive channels. We also proposed a successive relaying aided cooperative multicarrier STSK scheme for gleaning cooperative space-time diversity gains and for mitigating the throughput loss associated with the classical half-duplex relaying. Furthermore, in order to dispense with channel estimation as well as to mitigate the performance degradation of conventional non-coherent receivers,we propose a soft-output multiple-symbol differential sphere decoding aided multicarrier STSK arrangement.
We proposed a novel OFDM aided STSK scheme for broadband channels, where we designed a new modality of realizing space-time coding for broadband channels using OFDM combined with the STSK scheme to amalgamate spatial-, time- and frequency-domain spreading. The transceiver architecture of the OFDM aided scheme is portrayed in Fig. 4, where the source information is encoded to the STSK space-time codewords, which are transmitted by M transmit antennas over T time-slots employing Nc parallel subcarriers. The scheme has the benefit of employing a single-stream ML detector at the receiver, since a single dispersion matrix is activated in each symbol interval.


Figure 4. Transmitter and receiver of the proposed OFDM-aided STSK system.

We also proposed a MC-CDMA aided STSK scheme for exploiting additional frequency-domain (FD) diversity. The transmission scheme is similar to that of the OFDM-aided STSK, but the STSK codewords are further spread in the Frequency Domain (FD) by the user-specific spreading sequences. Naturally, this scheme supports multi-user transmissions.


Figure 5. Transmission model of SC-FDMA aided STSK scheme. In OFDMA aided scheme the dotted block `DFT Nd' in the transmitter does not exist.

We also proposed another scheme, combining the OFDMA/SC-FDMA aided uplink/downlink(UL/DL) with the STSK system for dispersive scenarios. The schematic diagram of the transmitter and the receiver of this scheme is shown in  Fig. 5 and Fig. 6 respectively. This scheme is capable of striking the same diversity-multiplexing trade off (DMT) as a single-carrier STSK scheme, whilst additionally supporting multiuser transmissions like the MC-CDMA aided scheme and maintaining a low peak-to-average power ratio (PAPR) in SC-FDMA-aided STSK uplink scenarios. The source bits of the OFDMA aided STSK scheme are mapped to the STSK codewords and they are transmitted over multiple subcarriers, like in the OFDM aided scheme, but the subcarriers are assigned to different users by the subcarrier mapper either in contiguous sub-band or they are dispersed uniformly over the entire frequency-domain. Our SC-FDMA-aided scheme is further improved by DFT-precoding using the dotted `DFT' block of Fig. 5. After subcarrier demapping, the receiver shown in Fig. 6 detects the source information. However, this scheme has the benefit of employing low-complexity frequency-domain equalization and a low-complexity detector.


Figure 6. Receiver model of SC-FDMA aided STSK scheme. In OFDMA aided scheme the dotted block `IDFT Nd' in the receiver does not exist.


Channel Estimation for CSTSK Systems
[1] Semi-blind joint channel estimation and data detection for space-time shift keying systems.
[2] Reduced-complexity near-capacity joint channel estimation and three-stage turbo detection for coherent space-time shift keying.
[3] Adaptive space-time shift keying systems .
[More Publications]

We developed  a semi-blind adaptive coherent space-time shift keying(CSTSK) based multiple-input multiple-output system using a low-complexity iterative channel estimation and data detection scheme. We first employ the minimum number of CSTSK training blocks, which is related to the number of transmitter antennas, to obtain a rough least square channel estimate (LSCE). Low- complexity single-stream maximum likelihood (ML) data detection is then carried out based on the initial LSCE and the detected data symbols are then utilised to refine the decision-directed LSCE. We showed that a few iterations are sufficient to approach the optimal ML detection performance obtained with the aid of perfect channel state information.

We also proposed a MC-CDMA aided STSK scheme for exploiting additional frequency-domain(FD) diversity. The transmission scheme is similar to that of the OFDM-aided STSK, but the STSK codewords are further spread in the Frequency Domain (FD) by the user-specific spreading sequences. Naturally, this scheme supports multi-user transmissions.


Figure 7. BBSB channel estimation aided three-stage serial-concatenated CSTSK MIMO system.

We also propose a reduced complexity joint channel estimation and three-stage iterative demapping/decoding scheme for near-capacity CSTSK based MIMO systems. The corresponding block diagram is shown in Fig. 7. In the proposed scheme, only  a minimum number of space-time shift keying training blocks are employed for generating an initial LSCE, which is then used for initial data detection.  As usual, the detected soft information is first exchanged a number of times within the inner turbo loop between our unity-rate-code's (URC) decoder and the CSTSK soft-demapper, and the information gleaned from the inner URC decoder is then iteratively exchanged with the outer decoder in the outer turbo loop. Our channel estimation scheme is embedded into the outer turbo loop,which exploits the a posteriori information produced by the CSTSK soft-demapper.Since the channel estimation is embedded into the iterative three-stage demapping/decoding process, no additional iterative loop is required for exchanging information between the decision-directed channel estimator and the three-stage turbo detector. Hence, the computational complexity of the joint channel estimation and three-stage turbo detection remains similar to that of the three-stage turbo detection/decoding scheme. Moreover,our proposed low-complexity semi-blind scheme is capable of approaching the optimal maximum likelihood turbo detection performance attained with the aid of perfect channel state information, as confirmed by our simulation results.

Closed-Loop MIMO: Transmit Precoding and Limited Feedback
Prof Lajos Hanzo, Prof. Lie-Liang Yang, Dr. Wei Liu, Dr. Du Yang
[1] Joint Transmitter-Receiver Design in TDD Multiuser MIMO Systems:An Egocentric/Altruistic Optimization Approach.
[2] SVD-Assisted Multiuser Transmitter and Multiuser Detector Designfor MIMO Systems

[3] Imperfect Channel Knowledge Based Ergodic Channel Capacity of Transmit Beamforming [More Publications]

Recently, joint transmitter-receiver optimization in wireless communications has received wide attention and research.This is because joint transmitter-receiver optimization may outperform significantly either transmitter optimization or receiver optimization. In joint transmitter-receiver optimization, the optimization constitutes transmitter optimization and receiver optimization, where the transmitter optimization is usually depended on the receiver optimization and, vice versa, the receiver optimization is depended on the transmitter optimization. Therefore,the joint transmitter-receiver optimization is suggested to be implemented using recursive algorithms. However,recursive transmitter-receiver optimization requires information exchange between transmitter(s) and receiver(s),which might be difficult to achieve in practice, especially when the channel is fast time-varying.

We show that transmitter optimization and receiver optimization share a lot of equivalency. To be specific, the concepts of altruistic (A)-optimization (optimum), egocentric(E)-optimization (optimum) and overall (O)-optimization (optimum)have been introduced. An A-optimum solution minimizes the impact of the desired user on the other users, while an E-optimum solution minimizes the impact of the interfering users on the desired user. An E-optimum solution in multiuser detection (MUD), which minimizes in some sense the interference of the interfering users imposing on the desired user, is equivalent to an A-optimum solution in multiuser transmission (MUT), which minimizes in thesame sense the interference of the desired user imposing on its colleague users. Furthermore, we have shown thatthe O-optimum solution for MUT is achieved, when all the users achieve their A-optimum.

Based on the abovediscussion, it can be implied, especially in cellular communications, that joint transmitter receiver design canbe well-built on the principles of Eoptimum, A-optimum and O-optimum. Specifically, based on the available knowledge,transmitter-optimization of a given user should aim at achieving its A-optimum solution, so as to minimize itsimpact on the other users. By contrast, based on the available knowledge, receiver-optimization of a given usershould aim at achieving its E-optimum solution, so as to achieve the best available performance. When bothtransmitters and receivers in the context of all users are A-optimum and Eoptimum, we can say that the jointtransmitter-receiver design is optimum and, in fact, the system at this stage is O-optimum.

Multi-Cell MIMO: Base-Station Cooperation and Optical Fiber aided Distributed Antenna
Prof Lajos Hanzo, Dr. Rong Zhang, Mr. Shaoshi Yang, Ms. Xinyi Xu
[1] Distributed Downlink Multi-Cell Processing Requiring Reduced-Rate Back-haul Data Exchange
[2] Unified Bit-Based Probabilistic Data Association Aided MIMO Detection for High-Order QAM Constellations
[3] Imperfect Radio Over Fibre Aided Distributed Antennas with FractionalFrequency Reuse [More Publications]

The classic Unity Frequency Reuse (UFR) pattern typically has a low throughput at the cell-edge.As a remedy, the Fractional Frequency Reuse (FFR) philosophy may be invoked, which is capable ofimproving the cell-edge Signal-to-Interference-plus-Noise-Ratio (SINR). Similarly, DASs are alsocapable of attaining an improved coverage, hence increasing the attainable throughput of thecell-edge area and reducing the total transmit power dissipation. In contrast to employing avulnerable wireless backhaul for connecting the central Base Station (BS) and the distributedRemote Antennas (RAs), the Radio over Fibre (RoF) transmission technique is eminently applicableto construct the BS to RA links
by exploiting the benefits of optical fibre based backhauls,although naturally, this increases the system’s cost. Hence the capacity of the resultant RoFaided DAS is essentially only limited by the ’last-mile’ wireless link.

More explicitly,the FFR technique improves the cell-edge throughput by orthogonally partitioning the entirefrequency band to ensure that the co-channel interference at the cell-edge is reduced at thecost of a reduced area-spectral-efficiency. By contrast, the DAS improves the cell-edgethroughput by placing the antennas more close to the cell-edge terminals, hence the pathloss is reduced.Thus, it becomes reasonable to combine the benefits of these two techniques in order to resolve,whether the drawbacks of the FFR technique may be compensated by the throughput enhancement ofDASs.

Against this background, in this paper we investigate the throughput across theentire cellular coverage area, when employing the FFR technique beneficially combined withrealistically modelled imperfect optical fibre aided DAS operating in a multicell scenario.We will demonstrate that
1) The combination of the FFR technique and DAS may not alwayslead to a combined throughput enhancement, when the pulse-broadening effects of realisticRoF links are also taken into account;
2) Employing different configurations of the RAsin DASs results into different geographic throughput distributions, suggesting that thepositioning of the RAs is dependent on the systems geographic user-distribution.

[top] [Main Page]