Low computational complexity joint iterative detection and decoding without ARQ in massive MIMO systems with UAVs

Abstract

Unmanned aerial vehicles (UAVs) have a wide range of military and commercial applications. UAVs play an important role in 6G networks due to their low cost and flexible positioning. UAVs can play different roles in the network and act as an air base station, as a relay, or as users in cellular networks. One of the major challenges in these systems is to increase lifespan with low energy consumption. The automatic repeat request (ARQ) protocol is used to increase throughput and obtain dependable communications for multi-input multiple-input multiple-output (MIMO) systems, even in the presence of severe propagation conditions. On the other hand, (UAV)-enabled communication system provides flexibility and reliability compared to conventional ones which have been considered in the new generation of wireless communications. Thus, using ARQ protocols in this system faces serious challenges such as increasing delay and operational implementation complexities in the receiver. Therefore, to meet the ARQ challenge in the uplink of massive MIMO systems, we have investigated utilizing joint iterative detection and decoding methods with reduced computational complexity have been proposed in a UAV-enabled communication system in the condition that users are equipped with two antennas to provide a solution to reduce energy consumption. In this structure, system performance improves as the computational complexity increases. Therefore, we look for methods that reduce complexity while maintaining system performance at an acceptable level. In this study, we seek to provide a solution that can establish a reasonable compromise between system performance and computational complexity. This aim is achieved by establishing a connection between the components of soft joint detection and decoding, linear detection with approximation methods, and sorting. The first part of the receiver is sorting the users before detection, then, in the next module, a turbo-process-based low-complexity MIMO iterative detection and decoding (IDD) algorithm, with minimum mean square error (MMSE) detector and soft channel decoder, is used. For solving the challenge of computational complexity, utilizing of approximation-based detection method is proposed. This structure works in such a way that after sorting the users, in the first iteration, a certain number of users are decoded using a hard decoding scheme after the soft detection and decoding. Therefore, these data are subtracted from the soft feedback information (prior data) to the detector, as inter user interference (IUI) and the Joint IDD module continue the iterative cycle until all users’ data are decoded.