Transmit Antenna Selection in Cognitive MIMO Relaying with Multiple Primary Transceivers

We consider transmit antenna selection (TAS) in cognitive multiple-input multiple-output (MIMO) relay networks, as an interference-aware design for secondary users (SUs) to ensure power and interference constraints of multiple primary users (PUs). In doing so, we derive new exact and asymptotic expressions for the outage probability of TAS with maximal ratio combining (TAS/MRC) and with selection combining (TAS/SC) over Rayleigh fading. The proposed analysis and simulations highlight that TAS/MRC and TAS/SC with decode-and-forward relaying achieve the same diversity order in cognitive MIMO networks, which scales with the minimum number of antennas at the SUs. Furthermore, we accurately characterize the outage gap between TAS/MRC and TAS/SC relaying as a concise ratio of their array gains.

Spatially Correlated Multiple-Ananna Channel Capacity Distributions

In this paper, we investigate the capacity of multiple-input multiple-output (MIMO) wireless communication systems over spatially correlated Rayleigh distributed flat fading channels with complex Gaussian additive noise. Specifically, we derive the probability density function of the mutual information between transmitted and received complex signals of MIMO systems. Using this density we derive the closed-form ergodic capacity (mean), delay-limited capacity, capacity variance and outage capacity formulas for spatially correlated channels and then evaluate these formulas numerically. Numerical results show how the channel correlation degrades the capacity of MIMO communication systems. We also show that the density of mutual information of correlated/uncorrelated MIMO systems can be approximated by a Gaussian density with derived mean and variance, even for a finite number of inputs and outputs.

On the Sphere Decoder for Frequency-Selective MIMO channels

This paper compares the complexity of the sphere decoder (SD) and a previously proposed detection scheme, denoted here as block SD (BSD), when they are applied to the detection of multiple-input multiple-output (MIMO) systems in frequency-selective channels. The complexity of both algorithms depends on their preprocessing and tree search stages. Although the BSD was proposed as a means of greatly reducing the complexity of the preprocessing stage of the SD, no study was done on how the complexity of the tree search stage could be affected by that reduced preprocessing stage. This paper shows, both analytically and through simulation, that the reduction in preprocessing complexity provided by the BSD has the side effect of increasing the complexity of its tree search stage compared to that of the SD, independent of the signal-to-noise ratio (SNR). In addition, this paper shows how sorting the columns of the frequency-selective channel matrix in the SD does not reduce the complexity of the tree search stage, contrary to what occurs in frequency-flat channels.

Reduced-complexity iterative equalization for severe time-dispersive MIMO channels

In this paper, a reduced-complexity soft-interference-cancellation minimum mean-square-error.(SIC-MMSE) iterative equalization method for severe time-dispersive multiple-input-multiple-output (MIMO) channels is proposed. To mitigate the severe time dispersiveness of the channel, a single carrier with cyclic prefix is employed, and the equalization is per-formed in the frequency domain. This simplifies the challenging problem of equalization in MIMO channels due to both the intersymbol interference (ISI) and the coantenna interference (CAI). The proposed iterative algorithm works in two stages. The first stage estimates the transmitted frequency-domain symbols using a low-complexity SIC-MMSE equalizer. The second stage converts the estimated frequency-domain symbols in the time domain and finds their means and variances to incorporate in the SIC-MMSE equalizer in the next iteration. Simulation results show the bit-/symbol-error-rate performance of the SIC-MMSE equalizer, with and without coding, for various modulation schemes.

Improved Linear Transmit Processing for Single-User and Multi-User MIMO Communications Systems

In this paper, we propose a novel linear transmit precoding strategy for multiple-input, multiple-output (MIMO) systems employing improper signal constellations. In particular, improved zero-forcing (ZF) and minimum mean square error (MMSE) precoders are derived based on modified cost functions, and are shown to achieve a superior performance without loss of spectrum efficiency compared to the conventional linear and nonlinear precoders. The superiority of the proposed precoders over the conventional solutions are verified by both simulation and analytical results. The novel approach to precoding design is also applied to the case of an imperfect channel estimate with a known error covariance as well as to the multi-user scenario where precoding based on the nullspace of channel transmission matrix is employed to decouple multi-user channels. In both cases, the improved precoding schemes yield significant performance gain compared to the conventional counterparts.

Iterative Receiver Design for MIMO Systems with Improper Signal Constellations

In this paper, we propose a novel iterative receiver
strategy for uncoded multiple-input, multiple-output (MIMO)
systems employing improper signal constellations. The proposed
scheme is shown to achieve superior performance and faster
convergence without the loss of spectrum efficiency compared
to the conventional iterative receivers. The superiority of this
novel approach over conventional solutions is verified by both
simulation and analytical results.

Low SNR Capacity for MIMO Rician and Rayleigh-Product Fading Channels with Single Co-channel Interferer and Noise

This paper studies the ergodic capacity of multiple-input multiple-output (MIMO) systems with a single co-channel interferer in the low signal-to-noise-ratio (SNR) regime. Two MIMO models namely Rician and Rayleigh-product channels are investigated. Exact analytical expressions for the minimum energy per information bit, Eb/N0min, and wideband slope, S0, are derived for both channels. Our results show that the minimum energy per information bit is the same for both channels while their wideband slopes differ significantly. Further, the impact of the numbers of transmit and receive antennas, the Rician K factor, the channel mean matrix and the interference-to-noise-ratio (INR) on the capacity, is addressed. Results indicate that interference degrades the capacity by increasing the required minimum energy per information bit and reducing the wideband slope. Simulation results validate our analytical results.

Correlation Rotation Linear Precoding for MIMO Broadcast Communications

A simple linear precoding technique is proposed for multiple input multiple output (MIMO) broadcast systems using phase shift keying (PSK) modulation. The proposed technique is based on the fact that, on an instantaneous basis, the interference between spatial links in a MIMO system can be constructive and can contribute to the power of the useful signal to improve the performance of signal detection. In MIMO downlinks this co-channel interference (CCI) can be predicted and characterised prior to transmission. Contrary to common practice where knowledge of the interference is used to eliminate it, the main idea proposed here is to use this knowledge to influence the interference and benefit from it, thus gaining advantage from energy already existing in the communication system that is left unexploited otherwise. The proposed precoding aims at adaptively rotating, rather than zeroing, the correlation between the MIMO substreams depending on the transmitted data, so that the signal of interfering transmissions is aligned to the signal of interest at each receive antenna. By doing so, the CCI is always kept constructive and the received signal to interference-plus-noise ratio (SINR) delivered to the mobile units (MUs) is enhanced without the need to invest additional signal power per transmitted symbol at the MIMO base station (BS). It is shown by means of theoretical analysis and simulations that the proposed MIMO precoding technique offers significant performance and throughput gains compared to its conventional counterparts.

QR decomposition-based matrix inversion for high performance embedded MIMO receivers

Real-time matrix inversion is a key enabling technology in multiple-input multiple-output (MIMO) communications systems, such as 802.11n. To date, however, no matrix inversion implementation has been devised which supports real-time operation for these standards. In this paper, we overcome this barrier by presenting a novel matrix inversion algorithm which is ideally suited to high performance floating-point implementation. We show how the resulting architecture offers fundamentally higher performance than currently published matrix inversion approaches and we use it to create the first reported architecture capable of supporting real-time 802.11n operation. Specifically, we present a matrix inversion approach based on modified squared Givens rotations (MSGR). This is a new QR decomposition algorithm which overcomes critical limitations in other QR algorithms that prohibits their application to MIMO systems. In addition, we present a novel modification that further reduces the complexity of MSGR by almost 20%. This enables real-time implementation with negligible reduction in the accuracy of the inversion operation, or the BER of a MIMO receiver based on this.

Real-valued fixed-complexity sphere decoder for high dimensional QAM-MIMO systems

The development of high performance, low computational complexity detection algorithms is a key challenge for real-time Multiple-Input Multiple-Output (MIMO) communication system design. The Fixed-Complexity Sphere Decoder (FSD) algorithm is one of the most promising approaches, enabling quasi-ML decoding accuracy and high performance implementation due to its deterministic, highly parallel structure. However, it suffers from exponential growth in computational complexity as the number of MIMO transmit antennas increases, critically limiting its scalability to larger MIMO system topologies. In this paper, we present a solution to this problem by applying a novel cutting protocol to the decoding tree of a real-valued FSD algorithm. The new Real-valued Fixed-Complexity Sphere Decoder (RFSD) algorithm derived achieves similar quasi-ML decoding performance as FSD, but with an average 70% reduction in computational complexity, as we demonstrate from both theoretical and implementation perspectives for Quadrature Amplitude Modulation (QAM)-MIMO systems.

Dynamic Linear Precoding for the Exploitation of Known Interference in MIMO Broadcast Systems

This paper introduces a novel channel inversion (CI) precoding scheme for the downlink of phase shift keying (PSK)-based multiple input multiple output (MIMO) systems. In contrast to common practice where knowledge of the interference is used to eliminate it, the main idea proposed here is to use this knowledge to glean benefit from the interference. It will be shown that the system performance can be enhanced by exploiting some of the existent inter-channel interference (ICI). This is achieved by applying partial channel inversion such that the constructive part of ICI is preserved and exploited while the destructive part is eliminated by means of CI precoding. By doing so, the effective signal to interference-plus-noise ratio (SINR) delivered to the mobile unit (MU) receivers is enhanced without the need to invest additional transmitted signal power at the MIMO base station (BS). It is shown that the trade-off to this benefit is a minor increase in the complexity of the BS processing. The presented theoretical analysis and simulations demonstrate that due to the SINR enhancement, significant performance and throughput gains are offered by the proposed MIMO precoding technique compared to its conventional counterparts.

A Low Complexity Real-time MIMO-Preprocessing For Fixed Complexity Sphere Decoder

Modern Multiple-Input Multiple-Output (MIMO) communication systems place huge demands on embedded processing resources in terms of throughput, latency and resource utilization. State-of-the-art MIMO detector algorithms, such as Fixed-Complexity Sphere Decoding (FSD), rely on efficient channel preprocessing involving numerous calculations of the pseudo-inverse of the channel matrix by QR Decomposition (QRD) and ordering. These highly complicated operations can quickly become the critical prerequisite for real-time MIMO detection, exaggerated as the number of antennas in a MIMO detector increases. This paper describes a sorted QR decomposition (SQRD) algorithm extended for FSD, which significantly reduces the complexity and latency
of this preprocessing step and increases the throughput of MIMO detection. It merges the calculations of the QRD and ordering operations to avoid multiple iterations of QRD. Specifically, it shows that SQRD reduces the computational complexity by over 60-70% when compared to conventional
MIMO preprocessing algorithms. In 4x4 to 7x7 MIMO cases, the approach suffers merely 0.16-0.2 dB reduction in Bit Error Rate (BER) performance.

On MIMO Radar Subarrayed Transmit Beamforming

Multiple-input-multiple-output (MIMO) radar schemes whereby the transmit array is partitioned into subarrays have recently been proposed in the literature to combine advantages of phased array and MIMO radar technology. In this work, we utilize this architecture to significantly simplify a transmit procedure in which the covariance matrix across the MIMO radar array is optimized to improve the Cramer-Rao bound (CRB) on target parameter estimation. The MIMO effective array for regular subarrayed transmit apertures is studied, and necessary conditions to obtain a filled effective aperture are presented, which is important for maintaining nonambiguous, low sidelobe beampatterns. The performance of the subarrayed transmit approach is evaluated in terms of the CRB on target parameter estimation, and the optimisation of the beamformer applied to the subarrays to minimize the CRB is considered. The subarrayed transmit scheme is found to have a CRB which is suboptimal to the full diversity transmission, as expected, but is solvable in a small fraction of the time using an iterative beamspace algorithm developed here.

Software-defined sphere decoding for FPGA-based MIMO detection

Sphere Decoding (SD) is a highly effective detection technique for Multiple-Input Multiple-Output (MIMO) wireless communications receivers, offering quasi-optimal accuracy with relatively low computational complexity as compared to the ideal ML detector. Despite this, the computational demands of even low-complexity SD variants, such as Fixed Complexity SD (FSD), remains such that implementation on modern software-defined network equipment is a highly challenging process, and indeed real-time solutions for MIMO systems such as 4 4 16-QAM 802.11n are unreported. This paper overcomes this barrier. By exploiting large-scale networks of fine-grained softwareprogrammable processors on Field Programmable Gate Array (FPGA), a series of unique SD implementations are presented, culminating in the only single-chip, real-time quasi-optimal SD for 44 16-QAM 802.11n MIMO. Furthermore, it demonstrates that the high performance software-defined architectures which enable these implementations exhibit cost comparable to dedicated circuit architectures.