This paper addresses the capacity problem for a class of finite-state two-way channels (FS-TWCs). Specifically, inner and outer bounds for the channel capacity of FS-TWCs are derived, and they are combined to characterize the capacity region in a limiting expression for some special FS-TWCs. Although such an expression is often incomputable, it is illustrated via an example that a reduction to single-letter form is possible as long as the system variables exhibit stationarity and the associated "average channel" satisfies certain symmetry properties.

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.

This paper considers an information bottleneck problem with the objective of obtaining a most informative representation of a hidden feature subject to a Rényi entropy complexity constraint. The optimal bottleneck trade-off between relevance (measured via Shannon's mutual information) and Rényi entropy cost is defined and an iterative algorithm for finding approximate solutions is provided. We also derive an operational characterization for the optimal trade-off by demonstrating that the optimal Rényi entropy-relevance trade-off is achievable by a simple time-sharing scalar coding scheme and that no coding scheme can provide better performance. Two examples where the optimal Shannon entropy-relevance tradeoff can be exactly determined are further given.

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.

We propose an adaptive lossy joint source-channel coding (JSCC) scheme for sending correlated sources over two-terminal discrete-memoryless two-way channels (DM-TWCs). The main idea is to couple the independent operations of the terminals via an adaptive coding mechanism, which can mitigate cross-interference resulting from simultaneous channel transmissions and concurrently exploit the sources' correlation to reduce the end-to-end reconstruction distortions. Our adaptive JSCC scheme not only subsumes existing lossy coding methods for two-way simultaneous communication but also improves their performance. Furthermore, we derive outer bounds for our two-way lossy transmission problem and establish complete JSCC theorems in some special settings. In these special cases, a non-adaptive separate source-channel coding (SSCC) scheme achieves the optimal performance, thus simplifying the design of the source-channel communication system.

An adaptive joint source-channel coding (JSCC) scheme is presented for transmitting correlated sources over discrete-memoryless two-way channels subject to distortion constraints. The proposed JSCC scheme makes use of the previously transmitted and received channel signals as well as the sources' correlation to facilitate coordination between terminals. It is shown that the adaptive scheme strictly subsumes prior lossy coding methods for two-way simultaneous transmission and yields a new adaptive separate source-channel coding result. Two examples are given to show the scheme's advantages.

In this paper, we make use of channel symmetry properties to determine the capacity region of three types of two-way networks: 1) two-user memoryless two-way channels (TWCs); 2) two-user TWCs with memory; and 3) three-user multiaccess/degraded broadcast (MA/DB) TWCs. For each network, symmetry conditions under which a Shannon-type random coding inner bound (under independent non-adaptive inputs) is tight are given. For two-user memoryless TWCs, prior results are substantially generalized by viewing a TWC as two interacting state-dependent one-way channels. The capacity of symmetric TWCs with memory, whose outputs are functions of the inputs and independent stationary and ergodic noise processes, is also obtained. Moreover, various channel symmetry properties under which the Shannon-type inner bound is tight are identified for three-user MA/DB TWCs. The results not only enlarge the class of symmetric TWCs whose capacity region can be exactly determined but also imply that interactive adaptive coding, not improving capacity, is unnecessary for such channels.

New sufficient conditions for determining in closed form the capacity region of point-to-point memoryless two-way channels (TWCs) are derived. The proposed conditions not only relax Shannon's condition which can identify only TWCs with a certain symmetry property but also generalize other existing results. Examples are given to demonstrate the advantages of the proposed conditions.

This work focuses on the construction of optimized binary signaling schemes for two-sender uncoded transmission of correlated non-uniform sources over non-orthogonal Gaussian multiple access channels. Based on an error-rate analysis under joint maximum-a-posteriori decoding, optimized binary-pulsed-amplitude modulation constellations for two senders are derived to minimize the system's error rate. The joint probability distribution of the two-senders' source is observed to induce a special layout of optimized constellations which can effectively control the interference due to non-orthogonal transmission. Numerical results further confirm that significant gains are achievable by the proposed design.

Achievability and converse results for the lossy transmission of correlated sources over Shannon's two-way channels (TWCs) are presented. A joint source-channel coding theorem for independent sources and TWCs for which adaptation cannot enlarge the capacity region is also established. We further investigate the optimality of scalar coding for TWCs with discrete modulo additive noise as well as additive white Gaussian noise. Comparing the distortion of scalar coding with the derived bounds, we observe that scalar coding achieves the minimum distortion over both families of TWCs for independent and uniformly distributed sources and independent Gaussian sources.

In this paper, decoding of LDPC convolutional codes with rational parity-check matrices (LDPC-CC-RPCM) is investigated. We show that Tanner graph of every LDPC-CC-RPCM can always be transformed into an equivalent one with enlarged girth and finite memory order suitable for practical pipeline decoder. Based on the transformed graph, a dynamic scheduling-aided decoding scheme with the enhancement of signal perturbation and error cancellation is presented to improve the convergence speed and bit-error-rate performance in both of the waterfall and error-floor regions. Simulation results also reveal that LDPC-CC-RPCM may outperform ordinary LDPC-CC with polynomial parity-check matrices in some cases under the same code rate and decoding complexity.

In this paper, convolutional encoders are studied for unequal error protection (UEP) from an algebraic theoretical viewpoint. Given any convolutional code, UEP-optimal encoders with the smallest McMillan degree are constructed to minimize the coding complexity. The noncatastrophic property of encoder is also maintained to avoid the undesired catastrophic propagation of decoding errors.

Previous studies on low-density parity-check convolutional codes (LDPC-CC) reveal that LDPC-CC with rational parity-check matrices (RPCM) suffer from the unaffordable decoding latency/complexity due to the infinite memory order and the poor bit-error-rate performance due to the existence of length-4 cycles in the Tanner graph. However, in this paper, we show that every LDPC-CC with RPCM can be associated with an equivalent Tanner graph which can avoid the infinite memory order and undesired short length cycles but still implements the same constraints specified by the RPCM. Together with the iterative decoding based on belief propagation with proper scheduling, simulation results indicate that LDPC-CC with RPCM can also provide satisfactory decoding performance.

In the literature, a class of iterative decoding algorithms which combine the traditional reliability-based decoding (RBD) with the adaptive belief propagation (ABP) have been validated to be applicable for Reed-Solomon codes. However, in the original design of the iterative decoding, the soft-information is passed only from the ABP-part to the RBD-part such that the decoding performance is somewhat limited. In this study, we first present a new reliability updating scheme for the bidirectional exchange of soft-information in the iterative decoding, which can guarantee the correction of the most errors in both of the reliable and unreliable bits. A simple bit-flipping mechanism is also proposed to refine the initialization of the ABP-part for further performance improvement. Revealed by the simulation results, our proposed scheme can outperform the conventional design in terms of the bit-error-rate performance.

Bit-interleaved coded modulation with iterative decoding (BICM-ID) has been verified to be a powerful transmission scheme with remarkable bit-error-rate performance. Among those well-designed BICM-ID systems, we observe that some of the channel encoders and signal mappers are inherently with the capability of multilevel protection. A new scheduling rule called protection matching which can properly schedule the data flow between the channel encoder and signal mapper with respect to the multilevel protection capability is proposed to achieve further performance improvement. Not only theoretical analysis but also simulation results are given to verify the advantage of the proposed design.

In this paper, space-time coding schemes with full transmit diversity are investigated for unequal error protection (UEP). Effective performance indicies are proposed to measure the intrinsic UEP capability of space-time codes, based on which we demonstrate that space-time trellis codes and superorthogonal space-time trellis codes can be used for UEP as long as the corresponding encoders are properly designed. In addition, UEP convolutional codes are concatenated with space-time block codes to construct another full-diversity UEP scheme which can provide more choices of UEP levels. Finally, good UEP codes are given by a computer search.