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

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

Opportunistic Access Point Selection for Mobile Edge Computing Networks

In this paper, we investigate a mobile edge computing (MEC) network with two computational access points (CAPs), where the source is equipped with multiple antennas and it has some computational tasks to be accomplished by the CAPs through Nakagami-m distributed wireless links. Since the MEC network involves both communication and computation, we first define the outage probability by taking into account the joint impact of latency and energy consumption. From this new definition, we then employ receiver antenna selection (RAS) or maximal ratio combining (MRC) at the receiver, and apply selection combining (SC) or switch-and-stay combining (SSC) protocol to choose a CAP to accomplish the computational task from the source. For both protocols along with the RAS and MRC, we further analyze the network performance by deriving new and easy-to-use analytical expressions for the outage probability over Nakagami-m fading channels, and study the impact of the network parameters on the outage performance. Furthermore, we provide the asymptotic outage probability in the low regime of noise power, from which we obtain some important insights on the system design. Finally, simulations and numerical results are demonstrated to verify the effectiveness of the proposed approach. It is shown that the number of transmit antenna and Nakagami parameter can help reduce the latency and energy consumption effectively, and the SSC protocol can achieve the same performance as the SC protocol with proper switching thresholds of latency and energy consumption