This paper studies Gaussian random codes based on convolutional structures. Firstly, an error probability upper bound for finite-length Gaussian random codes is derived, expressed as a closed-form solution for the error exponent. It also proposes regular convolutional Gaussian random codes and analyzes the upper bound of error probability of their component codes. To improve the cutoff rate and reduce decoding complexity, polar codes are employed as component codes and SVD precoding is introduced to transform polar codes into approximate Gaussian random codes. Numerical verification shows that the proposed codes have the potential to meet high-performance requirements under achievable complexity conditions.

This contribution studies adaptive power allocation of low-density parity check-code (LDPC) coded modulation on fading channels, where in this case, the symbols within a codeword suffer from different signal-to-noise ratios (SNRs). We consider adjusting the transmission power for each symbol within the codeword so as to improve the transmission performance, through an optimization based on Gaussian approximation. Specially, we first give the expression of the optimization problem, and further propose the solution based on Lagrangian method. Numerical results demonstrate the performance gain brought by the proposed power allocation method.

The concept of unsourced multiple access (UMAC) has been developed toward the massive access scenario in future wireless communications. Recently, a basic framework of UMAC has been proposed where every message is encoded by polar codes. In this contribution, we extend the utilization of polarization-adjusted convolutional (PAC) codes into the aforementioned framework, in order to reap its advantage in excellent error performance in the context of short block length. The performance comparisons of the above mentioned schemes demonstrate that the new structure of UMAC with PAC codes is capable of exhibiting improved error performance with reducing complexity when compared to that with polar codes.

We study a downlink non-orthogonal multiple access (NOMA) system with cooperative full-duplex relaying, where the near user in terms of the base station (BS) is enabled to act as a full-duplex relay for the far user. In particular, we first derive the outage probability with closed-form expressions when the power allocations at the BS and relay (or the near user) are fixed. Then, we analytically obtain the optimal power allocations with closed-form expressions at the BS and relay to minimize the outage probability. Numerical results validate the correctness of the theoretical analysis and demonstrate the advantages of the proposed algorithms over the state of arts.

Millimeter-wave (mmWave) communication is envisioned to provide orders of magnitude capacity improvement. However, it is challenging to realize a sufficient link margin due to high path loss and blockages. To address this difficulty, in this paper, we explore the potential gain of ultra-densification for enhancing mmWave communications from a network-level perspective. By deploying the mmWave base stations (BSs) in an extremely dense and amorphous fashion, the access distance is reduced and the choice of serving BSs is enriched for each user, which are intuitively effective for mitigating the propagation loss and blockages. Nevertheless, co-channel interference under this model will become a performance-limiting factor. To solve this problem, we propose a large-scale channel state information (CSI)-based interference coordination approach. Note that the large-scale CSI is highly location-dependent, and can be obtained with a quite low cost. Thus, the scalability of the proposed coordination framework can be guaranteed. Particularly, using only the large-scale CSI of interference links, a coordinated frequency resource block allocation problem is formulated for maximizing the minimum achievable rate of the users, which is uncovered to be an NP-hard integer programming problem. To circumvent this difficulty, a greedy scheme with polynomial-time complexity is proposed by adopting the bisection method and linear integer programming tools. Simulation results demonstrate that the proposed coordination scheme based on large-scale CSI only can still offer substantial gains over the existing methods. Moreover, although the proposed scheme is only guaranteed to converge to a local optimum, it performs well in terms of both user fairness and system efficiency.

For real-time wireless communications, short forward error-correcting (FEC) codes are indispensable due to the strict delay requirement. In this paper we study the performance of short FEC codes. Reed-Solomon (RS) codes and concatenated zigzag (CZ) codes are chosen as representatives of classical algebraic codes and modern simple iterative decodable codes, respectively. Additionally, we use random binary linear codes as a baseline reference for comparison. Our main results (demonstrated by both simulation and ensemble distance spectrum analysis) are as follows: 1) Short RS codes are as good as random binary linear codes; 2) Carefully designed short low-density parity-check (LDPC) codes are almost as good as random binary linear codes when high decoding complexity can be tolerated; 3) Low complexity belief propagation decoders incur considerable performance loss at short coding lengths.