Recently, the full-duplex multiple-input multiple-output relay (FD-MIMO-R) has been widely considered a promising way to increase the spectrum efficiency of a cooperative communication network. Under frequency-selective fading channels, an FD-MIMO-R system simultaneously exhibits the self-interference (SI) and inter-symbol interference (ISI). Most existing transceiver designs aim to cancel or mitigate these two interferences by treating them as harmful signals. However, SI and ISI are inherently generated from the desired signal, which implies that further performance improvement is possible if they can be properly exploited. This concept motivates us to propose novel finite-order space-time source and relay filters so that the end-to-end mean-square error can be minimized. Our design is based on the following two phases. First we derive the optimum spectrums of the source and relay filters with a majorization theory and alternating optimization. The finite-order filters are then developed by applying the weighted least-square criterion. Numerical experiments show that our proposed filters are indeed able to significantly improve the system performance in either flat or frequency-selective fading channels.

We present an enhanced informed dynamic scheduling (IDS) strategy to avoid propagations of unreliable messages in the residual belief-propagation (RBP) algorithm for decoding low-density parity-check (LDPC) codes. To reduce the resource needed for updating the degree-one variable nodes (VNs) in an RBP-based algorithm for decoding 5G New Radio (NR) LDPC codes, we further introduce a process consisting of several simple procedures for managing the resource consumption. Simulation results demonstrate that our RBP algorithm based on the proposed IDS strategy outperforms existing RBP-based algorithms at the early decoding iterations. By using our degree-one VN resource managing approach, the performance of both the proposed and the existing decoders is significantly improved in decoding the 5G NR codes.

Belief propagation (BP) decoding of low-density parity-check (LDPC) codes with various dynamic decoding schedules have been proposed to improve the efficiency of the conventional flooding schedule. As the ultimate goal of an ideal LDPC code decoder is to have correct bit decisions, a dynamic decoding schedule should be variable node (VN)-centric and be able to find the VNs with probable incorrect decisions and having a good chance to be corrected if chosen for update. We propose a novel and effective metric called conditional innovation (CI) which serves this design goal well. To make the most of dynamic scheduling which produces high-reliability bit decisions, we limit our search for the candidate VNs to those related to the latest updated nodes only. Based on the CI metric and the new search guideline separately or in combination, we develop several highly efficient decoding schedules. To reduce decoding latency, we introduce multi-edge updating versions which offer extra latency-performance tradeoffs. Numerical results show that both single-edge and multi-edge algorithms provide better decoding performance against most dynamic schedules and the CI-based algorithms are particularly impressive at the first few decoding iterations.

In this paper, we establish a connection between length-compatible (LC) punctured/shortened polar codes and compound polar codes. Regarding an LC code as a compound code to be transmitted over two different channels, we propose four channel-coded bit mapping rules which generalize some known LC code constructing methods. Performance of the punctured and shortened codes constructed by applying different mapping rules can thus be predicted checking if the Bhattacharyya bound is minimized.

Bit-flipping (BF) decoding of low-density parity-check codes is of low complexity but gives inferior performance in general. To improve performance and provide new BF decoder options for complexity-performance tradeoffs, we propose new designs for the flipping function (FF), the flipped bit selection (FBS) rule and the checksum weight updating schedule. The new FF adjusts the checksum weights in every iteration while our FBS rules take more information into account. These two modifications represent efforts to track more closely the evolutions of both check and variable nodes' reliabilities. Two selective update schedules are proposed to offer more performance and complexity tradeoffs. The combinations of the new FBS rule and known FFs result in new BF decoders with improved performance and a modest complexity increase. On the other hand, combining the new FF and FBS rule gives a new decoder with performance comparable to that of the normalized min-sum algorithm while if we use a much simpler FBS rule instead, the decoder suffers little performance loss with reduced complexity. We also present a simple decision-theoretical argument to justify the new checksum weight formula and a time-expanded factor graph model to explain the proposed selective weight-updating schedules.