Fairness of User Clustering in MIMO Non-Orthogonal Multiple Access Systems

In this letter, a downlink multiple-input-multiple-output non-orthogonal multiple access scenario is considered. We investigate a dynamic user clustering problem from a fairness perspective. In order to solve this optimization problem, three sub-optimal algorithms, namely, top-down A, top-down B, and bottom up, are proposed to realize the different tradeoffs of complexity and throughput of the worst user. In addition, for each given user clustering case, we optimize the power allocation coefficients for the users in each cluster by adopting a bisection search-based algorithm. Numerical results show that the proposed algorithms can lower the complexity with an acceptable degradation on the throughput compared with the exhaustive search method. It is worth noting that the top-down B algorithm can achieve a good tradeoff between the complexity and the throughput among the three proposed algorithms

Optimization of Grant-Free NOMA With Multiple Configured-Grants for mURLLC

15 pages, 15 figures, submitted to IEEE JSAC SI on Next Generation Multiple Access. arXiv admin note: text overlap with arXiv:2101.0051515 pages, 15 figures, submitted to IEEE JSAC SI on Next Generation Multiple Access. arXiv admin note: text overlap with arXiv:2101.0051

Analytic Expressions and Bounds for Special Functions and Applications in Communication Theory

This paper is devoted to the derivation of novel analytic expressions and bounds for a family of special functions that are useful in wireless communication theory. These functions are the well-known Nuttall Q-function, incomplete Toronto function, Rice Ie-function, and incomplete Lipschitz-Hankel integrals. Capitalizing on the offered results, useful identities are additionally derived between the above functions and Humbert, Φ1, function as well as for specific cases of the Kampé de Fériet function. These functions can be considered as useful mathematical tools that can be employed in applications relating to the analytic performance evaluation of modern wireless communication systems, such as cognitive radio, cooperative, and free-space optical communications as well as radar, diversity, and multiantenna systems. As an example, new closed-form expressions are derived for the outage probability over nonlinear generalized fading channels, namely, α-η-μ, α-λ-μ, and α-κ-μ as well as for specific cases of the η-μ and λ-μ fading channels. Furthermore, simple expressions are presented for the channel capacity for the truncated channel inversion with fixed rate and corresponding optimum cutoff signal-to-noise ratio for single-antenna and multiantenna communication systems over Rician fading channels. The accuracy and validity of the derived expressions is justified through extensive comparisons with respective numerical results

Contrastive Learning based Semantic Communications

Recently, there has been a growing interest in learning-based semantic communication because it can prioritize the preservation of meaningful semantic information over the accuracy of the transmitted symbols, resulting in improved communication efficiency. However, existing learning-based approaches still face limitations in defining semantic level loss and often struggle to find a good trade-off between preserving semantic information and preserving intricate details. In addition, the existing semantic communication approaches cannot effectively train semantic encoders and decoders without the support of downstream models. To address these limitations, this paper proposes a contrastive learning (CL)-based semantic communication system. First, inspired by practical observations, we introduce the concept of semantic contrastive loss and propose a semantic contrastive coding (SemCC) approach that treats data corruption during transmission as a form of data augmentation within the CL framework. Moreover, we propose a semantic re-encoding (SemRE) operation, which uses a duplicate of the semantic encoder deployed at the receiver to guide the entire training process when the downstream model is inaccessible. Further, we design the training procedure for SemCC and SemRE approaches, respectively, to balance the semantic information and intricate details. Finally, simulations are performed to demonstrate the superiority of the proposed approaches over competing approaches. In particular, our approaches achieve a significant accuracy improvement of up to 53% on the CIFAR-10 dataset with a bandwidth compression ratio of 1/24, and also obtain comparable image reconstruction quality as the bandwidth compression ratio is improved

Towards Optimally Efficient Search with Deep Learning for Large-Scale MIMO Systems

This paper investigates the optimal signal detection problem with a particular interest in large-scale multiple-input multiple-output (MIMO) systems. The problem is NP-hard and can be solved optimally by searching the shortest path on the decision tree. Unfortunately, the existing optimal search algorithms often involve prohibitively high complexities, which indicates that they are infeasible in large-scale MIMO systems. To address this issue, we propose a general heuristic search algorithm, namely, hyper-accelerated tree search (HATS) algorithm. The proposed algorithm employs a deep neural network (DNN) to estimate the optimal heuristic, and then use the estimated heuristic to speed up the underlying memory-bounded search algorithm. This idea is inspired by the fact that the underlying heuristic search algorithm reaches the optimal efficiency with the optimal heuristic function. Simulation results show that the proposed algorithm reaches almost the optimal bit error rate (BER) performance in large-scale systems, while the memory size can be bounded. In the meanwhile, it visits nearly the fewest tree nodes. This indicates that the proposed algorithm reaches almost the optimal efficiency in practical scenarios, and and thereby it is applicable for large-scale systems. Besides, the code for this paper is available at https://github.com/skypitcher/hats