MIMO-aided near-capacity turbo transceivers: taxonomy and performance versus complexity

In this treatise, we firstly review the associated Multiple-Input Multiple-Output (MIMO) system theory and review the family of hard-decision and soft-decision based detection algorithms in the context of Spatial Division Multiplexing (SDM) systems. Our discussions culminate in the introduction of a range of powerful novel MIMO detectors, such as for example Markov Chain assisted Minimum Bit-Error Rate (MC-MBER) detectors, which are capable of reliably operating in the challenging high-importance rank-deficient scenarios, where there are more transmitters than receivers and hence the resultant channel-matrix becomes non-invertible. As a result, conventional detectors would exhibit a high residual error floor. We then invoke the Soft-Input Soft-Output (SISO) MIMO detectors for creating turbo-detected two- or three-stage concatenated SDM schemes and investigate their attainable performance in the light of their computational complexity. Finally, we introduce the powerful design tools of EXtrinsic Information Transfer (EXIT)-charts and characterize the achievable performance of the diverse near- capacity SISO detectors with the aid of EXIT charts

Channel coded iterative center-shifting K-best sphere detection for rank-deficient systems

Based on an EXtrinsic Information Transfer (EXIT) chart assisted receiver design, a low-complexity near-Maximum A Posteriori (MAP) detector is constructed for high-throughput MIMO systems. A high throughput is achieved by invoking high-order modulation schemes and/or multiple transmit antennas, while employing a novel sphere detector (SD) termed as a center-shifting SD scheme, which updates the SD’s search center during its consecutive iterations with the aid of channel decoder. Two low-complexity iterative center-shifting SD aided receiver architectures are investigated, namely the direct-hard-decision centershifting (DHDC) and the direct-soft-decision center-shifting (DSDC) schemes. Both of them are capable of attaining a considerable memory and complexity reduction over the conventional SD-aided iterative benchmark receiver. For example, the DSDC scheme reduces the candidate-list-generation-related and extrinsic-LLR-calculation related complexity by a factor of 3.5 and 16, respectively. As a further benefit, the associated memory requirements were also reduced by a factor of 16

Low-Complexity Detection/Equalization in Large-Dimension MIMO-ISI Channels Using Graphical Models

In this paper, we deal with low-complexity near-optimal detection/equalization in large-dimension multiple-input multiple-output inter-symbol interference (MIMO-ISI) channels using message passing on graphical models. A key contribution in the paper is the demonstration that near-optimal performance in MIMO-ISI channels with large dimensions can be achieved at low complexities through simple yet effective simplifications/approximations, although the graphical models that represent MIMO-ISI channels are fully/densely connected (loopy graphs). These include 1) use of Markov Random Field (MRF) based graphical model with pairwise interaction, in conjunction with {\em message/belief damping}, and 2) use of Factor Graph (FG) based graphical model with {\em Gaussian approximation of interference} (GAI). The per-symbol complexities are $O(K^2n_t^2)$ and $O(Kn_t)$ for the MRF and the FG with GAI approaches, respectively, where $K$ and $n_t$ denote the number of channel uses per frame, and number of transmit antennas, respectively. These low-complexities are quite attractive for large dimensions, i.e., for large $Kn_t$. From a performance perspective, these algorithms are even more interesting in large-dimensions since they achieve increasingly closer to optimum detection performance for increasing $Kn_t$. Also, we show that these message passing algorithms can be used in an iterative manner with local neighborhood search algorithms to improve the reliability/performance of $M$-QAM symbol detection

Iterative amplitude/phase multiple-symbol differential sphere detection for DAPSK modulated transmissions

Differentially encoded and non-coherently detected transceivers exhibit a low complexity, since they dispense with complex channel estimation. Albeit this is achieved at the cost of requiring an increased transmit power, they are particularly beneficial, for example in cooperative communication scenarios, where the employment of channel estimation for all the mobile-to-mobile links may become unrealistic. In pursuit of high bandwidth efficiency, differential amplitude and phase shift keying (DAPSK) was devised using constellations of multiple concentric rings. In order to increase resilience against the typical high-Doppler-induced performance degradation of DAPSK and/or enhance the maximum achievable error-free transmission rate for DAPSK modulated systems, multiple-symbol differential detection (MSDD) may be invoked. However, the complexity of the maximum-a-posteriori (MAP) MSDD increases exponentially with the detection window size and hence may become excessive upon increasing the window size, especially in the context of iterative detection aided channel coded system. In order to circumvent this excessive complexity, we conceive a decomposed two-stage iterative amplitude and phase (A/P) detection framework, where the challenge of having a non-constant-modulus constellation is tackled with the aid of a specifically designed information exchange between the independent A/P detection stages, thus allowing the incorporation of reduced-complexity sphere detection (SD). Consequently, a near-MAP-MSDD performance can be achieved at a significantly reduced complexity, which may be five orders of magnitude lower than that imposed by the traditional MAP-MSDD in the 16-DAPSK scenario considered

Scaling up MIMO: Opportunities and Challenges with Very Large Arrays

This paper surveys recent advances in the area of very large MIMO systems. With very large MIMO, we think of systems that use antenna arrays with an order of magnitude more elements than in systems being built today, say a hundred antennas or more. Very large MIMO entails an unprecedented number of antennas simultaneously serving a much smaller number of terminals. The disparity in number emerges as a desirable operating condition and a practical one as well. The number of terminals that can be simultaneously served is limited, not by the number of antennas, but rather by our inability to acquire channel-state information for an unlimited number of terminals. Larger numbers of terminals can always be accommodated by combining very large MIMO technology with conventional time- and frequency-division multiplexing via OFDM. Very large MIMO arrays is a new research field both in communication theory, propagation, and electronics and represents a paradigm shift in the way of thinking both with regards to theory, systems and implementation. The ultimate vision of very large MIMO systems is that the antenna array would consist of small active antenna units, plugged into an (optical) fieldbus.Comment: Accepted for publication in the IEEE Signal Processing Magazine, October 201

Dispensing with channel estimation: differentially modulated cooperative wireless communications

As a benefit of bypassing the potentially excessive complexity and yet inaccurate channel estimation, differentially encoded modulation in conjunction with low-complexity noncoherent detection constitutes a viable candidate for user-cooperative systems, where estimating all the links by the relays is unrealistic. In order to stimulate further research on differentially modulated cooperative systems, a number of fundamental challenges encountered in their practical implementations are addressed, including the time-variant-channel-induced performance erosion, flexible cooperative protocol designs, resource allocation as well as its high-spectral-efficiency transceiver design. Our investigations demonstrate the quantitative benefits of cooperative wireless networks both from a pure capacity perspective as well as from a practical system design perspective

Smart Antenna-Aided Multicarrier Transceivers for Mobile Communications

In spite of an immense interest from both the academic and the industrial communities, a practical multipleinput multiple-output (MIMO) transceiver architecture, capable of approaching channel capacity boundaries in realistic channel conditions remains largely an open problem. Consequently, in this treatise I derive an advanced iterative, so called turbo multi-antenna-multi-carrier (MAMC) receiver architecture. Following the philosophy of turbo processing, our turbo spacial division multiplexed (SDM)-orthogonal frequency division multiplexed (OFDM) receiver comprises a succession of soft-input-soft-output detection modules, which iteratively exchange soft bit-related information and thus facilitate a substantial improvement of the overall system performance. In this treatise, I explore two major aspects of the turbo wireless mobile receiver design. Firstly, I consider the problem of soft-decision-feedback aided acquisition of the propagation conditions experienced by the transmitted signal and secondly, I explore the issue of the soft-input-soft-output detection of the spatially-multiplexed information-carrying signals