Adaptive multiple access assists multiple users over multiple‐input‐multiple‐output non‐orthogonal multiple access wireless networks

Abstract

The works in this study examine three scenarios. The scenario (I) as a common scenario with the base station (BS) and users are equipped with multiple antennae over Nakagami-m fading channels. To improve system performance, the transmit antenna selection (TAS) and maximization of successive interference cancellation (max-SIC) framework are deployed. A novel cumulative distribute function (CDF) for the scenario (I) is proposed, and the outage probability (OP) in novel closed form can be obtained. It is challenging due to scenario (I) consists of massive users equipped with multiple antennae over Nakagami-m fading channels. Therefore, this study proposes scenario (II) with a maximum of SIC and minimum of instantaneous bit rate (max-SIC-min-rate) framework for aiding multiple access to reduce the algorithm complexity of the scenario (I). Some adaptive multiple access strategies are proposed as adaptive PA factors, adaptive min-max bit rate threshold, and adaptive Nakagami-m coefficient are proposed. Finally, the scenario (III) is examined by also applying the max-SIC-min-rate framework as the scenario (II), however, comparing to the adaptive min-max bit rate threshold to investigate the OP, system throughput, and energy-efficient (EE) performances. The obtained results as shown in the numerical results section confirm that, on one hand, the system performance of the scenario (II) is approximate or better than the scenario (I) at the same transmit power; on another hand, the scenario (III) reaches the system throughput and EE performances better than the other scenarios at the high transmit power. The analysis results are proved and verified by Monte Carlo simulation results.

Approaching K-Means for Multiantenna UAV Positioning in Combination With a Max-SIC-Min-Rate Framework to Enable Aerial IoT Networks

“…The studies described the application of the max-SIC-minrate framework reported in [48], [49]. This study presents Algorithms 1 and 2, which are applied to the max-SICmin-rate framework to investigate the outage probability at the UAV and terminal devices.…”

“…In a previous study, the authors assumed that the devices are arranged from the nearest device D 1 to the farthest device D N [49], [50] or from the nearest device D N to the farthest device D 1 [51] for simplicity. Therefore, the power allocation factors in [49], [50] follow the arrangement α 1 < . .…”

“…> α N , as in [51]. By applying conventional SIC, the authors sequentially decoded messages x N → x 1 as in [49], [50] or x 1 → x N as in [51]. By contrast, the devices are non-ordered after UAV positioning, therefore, conventional SIC cannot be applied to decode the messages.…”

Design of Relay Switching to Combat an Eavesdropper in IoT-NOMA Wireless Networks

“…In the concept of NOMA, the farthest device D N was allocated the biggest PA factor since the farthest device D N owned poorest CSI. Inspired by studies [21,37], the PA factor α i for i = {N, . .…”

Sensor Clustering Using a K-Means Algorithm in Combination with Optimized Unmanned Aerial Vehicle Trajectory in Wireless Sensor Networks

“…Applied to all beyond 5G/6G wireless communications, nonorthogonal multiple access (NOMA) provides massive connections, low latency and high reliability [ 14 , 17 , 18 , 19 , 20 , 21 ]. In the current study, we took advantage of NOMA’s benefits, applying NOMA at the UAV to superimpose coding of the data received from wireless sensors and to forward this superimposed signal to a mobile data centre.…”