Semi-definite relaxation (SDR) has demonstrated the capability of approaching maximum-likelihood (ML) performance. In this work, we first develop a new SDR-based detector that exploits forward error correction (FEC) code information in the detection stage. The joint SDR detector substantially improves overall receiver performance by generating highly reliable information to downstream decoder. For further performance improvement, we integrate the joint SDR detector with decoder using a feedback link to form an iterative turbo receiver. Meanwhile, we propose a simplified SDR receiver that solves only one SDR problem per codeword instead of solving multiple SDR problems in the iterative turbo processing. This simplification significantly reduces the complexity of SDR turbo receiver, while maintaining a similarly superior error performance.

This work investigates robust receiver design against pilot-contaminated channel estimation in large multi-antenna systems. Given bounded channel estimate errors, we tackle the multi-user detection problem by presenting a novel idea of exploiting the forward error correction (FEC) code diversity. Unlike the traditional approach based on worst-case or probabilistic channel error, we distribute different channel codes among pilot-interfering users. We then develop a quadratic-programming receiver to utilize the special FEC signature of target user through a set of linear code constraints. Numerical results demonstrate substantial performance gain over existing robust detectors.

We consider a receiver design for a Hybrid Automatic-Repeat-reQuest (HARQ) system protected by polar codes against transmission errors. This integrated detection-decoding receiver targets a packet link for which channel state information during HARQ retransmission is unavailable at the receiver. Under such channel uncertainties, we propose a second-order cone programming (SOCP) approach for diversity combining without resorting to decision-directed channel estimation prone to error propagation. We formulate an integrated SOCP receiver by jointly exploiting the constraints from diversity channel models, subspace separation, and forward error correction codes. Unlike traditional turbo receiver algorithms that require iterative exchange of soft information between detector and decoder, our proposed SOCP receiver solves as a single-integrated convex optimization problem. This formulation is also versatile and extendable to a plurality of practical scenarios. We further investigate the means for enhancing the receiver performance and the HARQ throughput. Numerical results demonstrate the substantial performance benefits of the proposed joint SOCP receiver.

Massive multiple-input-multiple-output (MIMO) systems have been proposed to support high rate multiple access. As channel estimation in massive MIMO suffers from the well-known impairment of pilot contamination, we propose a novel approach to multi-user detection by exploiting forward error correction (FEC) code diversity. Unlike traditional approaches solely based on worst-case or probabilistic channel estimation errors, we develop a joint quadratic-programming (QP) receiver anchored with a set of FEC code constraints. Exploiting the user signatures presented by FEC channel codes of distinct permutations, our receiver can effectively recover signals from pilot-interfering users. The code-anchored robust design (CARD) method can also be applied to a chance-constrained receiver, which shows further performance gain compared with the direct integration of FEC code constraints in joint QP receiver. The effectiveness of CARD receivers is demonstrated by numerical results that establish substantial performance gain of the proposed receivers over existing robust designs. In addition, we present a distributed multi-cell processing scheme for enhanced performance via alternating direction method of multipliers.

We develop a novel diversity combiner for joint reception in wireless relay networks with unknown relay-to-destination channel. The partially available channel information poses an obstacle for utilization of the relay diversity. We tackle this problem by formulating a unified second-order cone programming (SOCP) receiver which jointly detects and decodes transmitted signals from both source and relay nodes. Our results demonstrate that the proposed diversity combiners can achieve performance close to that of the maximum-ratio combiner based on full channel information.

This work investigates the design of linear precoders in cooperative multi-cell MIMO downlink coverage for finite-alphabet source signals. Traditional design of multi-cell MIMO downlink precoder relies on Gaussian input assumption, which may lead to performance loss when true data inputs consist of discrete non-Gaussian symbols. This work presents optimized precoders for finite-alphabet input by maximizing the sum rate under per-base station power constraints. Specifically, we propose two distributed algorithms: a finite-alphabet signal Gaussian interference gradient projection algorithm and a block diagonalization alternating optimization algorithm while supporting interference cancellation. Our numerical results demonstrate considerable performance gain in terms of approximate transmission data rate as well as decoded bit error rate over precoding schemes designed by using the non-realistic Gaussian input assumption.

We develop a linear programming based approach for the joint detection and decoding of LDPC coded distributed space-time signaling transmitted in a wireless relay network. Traditional receivers typically decouple the detection and decoding processes as two separate blocks or require iterative turbo exchange of extrinsic information between the soft detector and decoder. We exploit the constraints imposed on the channel input signals and jointly consider the training symbols as well as the LDPC code information by formulating a unified linear programming (LP) receiver. Moreover, in consideration of the vast amount of LDPC parity check inequalities, we present an adaptive procedure to significantly reduce the complexity of the proposed LP receiver.

This work studies the design of linear precoders for cooperative multi-cell MIMO downlink systems with finite alphabet inputs. Traditionally, multi-cell MIMO downlink precoder designs rely on Gaussian input assumption, which may lead to performance loss when the true inputs admit discrete non-Gaussian symbols. In this work, we present optimized precoders by maximizing weighted sum rate (of finite-alphabet-input) under a set of single base station power constraints. Specifically, we propose a simple gradient algorithm for general multi-cell MIMO downlink channel and a block diagonalization gradient algorithm while supporting interference cancellation.