Ian D. Marsland

Threshold Selection for SNR-based Selective Digital Relaying in Cooperative Wireless Networks

This paper studies selective relaying schemes based on signal-to-noise-ratio (SNR) to minimize the end-to-end (e2e) bit error rate (BER) in cooperative digital relaying systems using BPSK modulation. In the SNR-based selective relaying, the relay either retransmits or remains silent depending on the SNRs of the source-relay, relay-destination, and source-destination links. Different models assuming the availability of different sets of instantaneous and average SNR information at the relay are studied. For each model, the optimal strategy to minimize the e2e BER is a different threshold rule on the source-relay SNR, if the link SNRs are uncorrelated in time and space. Approximations for the optimal threshold values that minimize the e2e BER and the resulting performance are derived analytically for BPSK modulation. Using the derived threshold the e2e BER can be reduced significantly compared to simple digital relaying. By studying the performance under different models, it is shown that knowledge of the instantaneous source-destination SNR at the relay can be exploited. The gain from this knowledge is higher when the average source-destination SNR is large. However, knowledge of the instantaneous relay-destination SNR at the relay does not change performance significantly.

On the performance of iterative noncoherent detection of coded M-PSK signals

Differential encoding is often used in conjunction with noncoherent demodulation to overcome carrier phase synchronization problems in communication systems employing M-ary phase-shift keying (M-PSK). It is generally acknowledged that differential encoding leads to a degradation in performance over absolutely encoded M-PSK systems with perfect carrier synchronization. In this paper, we show that when differential encoding is combined with convolutional encoding and interleaving, this degradation does not necessarily occur. We propose a novel noncoherent receiver for differentially encoded M-PSK signals that is capable of significantly outperforming optimal coherent receivers for absolutely encoded M-PSK using the same convolutional code. This receiver uses an iterative decoding technique and is based on a multiple differential detector structure to overcome the effect of the carrier phase error. In addition, to better illustrate the benefits of the powerful combination of convolutional encoding, interleaving, and differential encoding, we also present an iterative coherent receiver for differentially encoded M-PSK.

Improved transmit null steering for MIMO-OFDM downlinks with distributed base station antenna arrays

Space-division multiple-access (SDMA) is a communication technique that enables a base station to communicate with several mobile users simultaneously. The ability of the base station to spatially separate several users depends on the pairwise cross correlations between the channel matrices of the users (the inter-user correlation). In this paper, we propose an improved null steering downlink multiple-input-multiple-output-orthogonal frequency-division multiplexing (OFDM) system that reduces both the inter-user correlation and the near-far problem resulting in a significant enhancement in system performance. In this system, several base station multiantenna arrays are distributed in a given area. Each array communicates with the base station via optical fiber links, and all transmitter signal processing is performed at the base station. Multiantenna users are spatially separated such that only a subset of the users is served by each tone of the OFDM symbol. The served users are selected based on an algorithm that reduces the inter-user correlations. Distributing the arrays around the users also balances the channel matrix leading to significant reduction in the effect of the near-far problem. The channel matrix of each user is assumed correlated and Ricean distributed. Several data symbols can be spatially multiplexed to each user over each OFDM tone with high reliability and with good total system capacity.

Suboptimal Detectors for Alpha-Stable Noise: Simplifying Design and Improving Performance

The design of detectors for binary signals in symmetric alpha-stable noise is considered. Since the optimal detector is impractically complex, many suboptimal detectors have been proposed such as the Gaussian, soft limiter, myriad and Cauchy detectors. However, no adequate explanation for the difference in performance between these detectors has been proposed. In this paper, we propose a novel framework, based on the optimal decision regions, that is used to justify the performance of many suboptimal detectors and compare them to the optimal one. Moreover, the analysis of the framework provides a novel method to significantly improve the performance of the soft limiter detector by employing an adaptive threshold that is a function of the signal level. As the number of samples per symbol increases, the performance of the proposed adaptive detector approaches the optimal performance at almost no additional complexity over the conventional Gaussian detector.

A Comparison of Rateless Codes at Short Block Lengths

Raptor codes and rate-compatible low-density parity-check (RC-LDPC) codes have drawn much attention in recent years as they can approach channel capacity without requiring channel information at the transmitter. Raptor codes have been shown to uniformly approach the binary-input AWGN channel capacity, especially at low SNRs, whereas RC-LDPC codes have the potential to provide higher throughput than Raptor codes at high SNRs. In this paper, we use different message word sizes to compare the throughput of three rateless codes, namely, Raptor codes, rate-compatible irregular repeat-accumulate (RC-IRA) codes, and the rate-compatible quasi-cyclic LDPC (RC/QC-LDPC) codes proposed in the 3GPP2 and 802.20 standards. The comparison is focused on short message word lengths under 16-symbol quadrature amplitude modulation (16-QAM). The simulation results in the AWGN channel show that RC-IRA and RC/QC-LDPC codes outperform Raptor codes at high SNRs. Under frequency flat Rayleigh fading channels, RC-IRA codes outperform RC/QC-LDPC codes at high SNRs and perform slightly worse at low SNRs. We also show that for short block lengths, the throughput of RC-IRA codes is not particularly sensitive to the mother code rate, the belief propagation (BP) algorithm scheduling, the existence of parallel edges during check node combining, and the symbol degree distribution (for fixed average left degree).

An Incremental Redundancy Hybrid ARQ Scheme via Puncturing and Extending of Polar Codes

We construct polar codes for the specific purpose of incremental redundancy hybrid automatic repeat request (IR-HARQ) schemes. The rate compatibility of our scheme is ensured by both puncturing and extending of the code. A new puncturing algorithm for polar codes is proposed, and we develop an algorithm for finding good extending sequences for polar codes from any arbitrary punctured rate, with the goal of improving the throughput as much as possible. Simulation results for different types of puncturing and extending algorithms are presented. We show how the proposed extending algorithm, when properly operated with a good puncturing algorithm and a well-chosen puncturing rate, yields IR-HARQ coding schemes which can operate within 1 dB of Shannon capacity over a very wide range of signal-to-noise ratios.

A novel spatial modulation using MIMO spatial multiplexing

In this paper we propose a new Multiple-Input Multiple-Output (MIMO) transmission scheme that combines the generalised spatial modulation (GSM) with MIMO spatial multiplexing technique. Unlike the GSM which uses NA active antennas to transmit the same symbol, the proposed scheme uses the NA antennas to transmit different symbols simultaneously, which leads to increase the spectral efficiency of the system. An optimal detector is used at the receiver to jointly estimate the transmitted symbols as well as the index of active antennas combination. However, the optimal detector suffers from a high computational complexity. To solve this problem we propose a suboptimal detector which is based on a zero forcing detector. The performance of the proposed scheme is evaluated in an uncorrelated flat fading channel and compared with the optimal spatial modulation and vertical Bell Labs layered space-time.

Simplified LLR-based Viterbi decoder for convolutional codes in symmetric alpha-stable noise

The design of a simplified Viterbi decoder for signals in symmetric alpha-stable noise is considered. The conventional Viterbi decoder, which has a branch metric optimized for Gaussian noise, performs poorly in symmetric alpha-stable noise. Since the optimal maximum likelihood (ML) branch metric is impractically complex, simplified approaches are needed. A simple 1-norm nonlinearity has been used instead of the Euclidean distance in the Gaussian branch metric to improve the performance of the Viterbi decoder. It shows performance improvement for higher values of alpha; however, the performance degrades when alpha approaches 1. In this paper, we propose a simplified branch metric which depends on a piecewise linear approximation of the log likelihood ratio (LLR). The Viterbi decoder with the proposed branch metric gives near-optimal performance for different values of alpha at low complexity. The simulation results show that the performance improvement of the Viterbi decoder with the proposed branch metric is approximately 1.5-4 dB compared to the Viterbi decoder with the 1-norm nonlinearity for different values of alpha.

Near Optimal Viterbi Decoders for Convolutional Codes in Symmetric Alpha-Stable Noise

The design of Viterbi decoders for signals in noise modeled using the symmetric α-stable distribution is considered. The traditional Viterbi decoder, which has a branch metric optimized for Gaussian noise, performs poorly in symmetric α-stable noise. Since the optimal maximum likelihood branch metric is impractically complex, many suboptimal metrics have been proposed, such as the hard decision, p-norm and absolute (1-norm) metric. A Viterbi decoder that uses the absolute branch metric has better performance and lower complexity, however, its performance degrades when α decreases. In this paper, the effects of the suboptimal metrics on the performance of the Viterbi decoder are analyzed, and a clear justification for the performance of the decoder that uses the Gaussian and absolute metrics is provided. Moreover, this analysis is used to design a low complexity suboptimal branch metric that improves the performance of the Viterbi decoder by about 0.75 to 2 dB compared to the absolute branch metric for different values of α, at almost no additional complexity.

Adaptive Discrete-Rate MIMO Communications with Rate-Compatible LDPC Codes

By using rate-compatible (RC) low density parity-check (LDPC) codes with adaptive modulation, we propose an adaptive, discrete-rate multiple-input multiple-output (MIMO) communications system. Given the high spectral efficiency of MIMO and the flexibility of an incremental redundancy (IR) protocol, combined with adaptive coding and modulation (ACM), the designed communications system is capable of achieving high data rates, for a low amount of overhead. A novel ACM power- and bit-allocation protocol is proposed to implement this system. We adapt the existing water-filling algorithm (WFA) to the discrete and finite bit rate constraints inherent in any communications system. This constrained WFA is shown to significantly improve the throughput performance of the communications system, over the case where a regular WFA is used. The results given in this paper show that the combination of IR and ACM with MIMO creates a wireless communications system that can easily adapt to channel fluctuations and provide high-data rates.