Radial basis function-based node localization for unmanned aerial vehicle-assisted 5G wireless sensor networks

Neural Computing and Applications - Localization or positioning of wireless sensor nodes is an essential task for a wide range of applications in wireless sensor networks-based fifth generation...     

Design of Large Scale MU-MIMO System with Joint Precoding and Detection Schemes for Beyond 5G Wireless Networks

The large scale multiuser multiple input multiple output (MU-MIMO) is one of the promising communication technology for 5G wireless networks as it offers reliability, high spectral efficiency and high throughput. The lattice reduction (LR) precoding based user level local likelihood ascent search (ULAS) detection scheme is proposed in this paper for efficient signal detection in large scale MU-MIMO system. The initial solution of ULAS algorithm is obtained from the LR precoding assisted zero forcing detector. The LR precoding transforms the non-orthogonal channel matrix into nearly orthogonal channel, which helps to mitigate inter antenna interference (IAI) exists at each user. The remaining multiuser interference (MUI) imposed to each user from undesired users is cancelled by the proposed ULAS multiuser detection scheme. Thus, the proposed LR precoding assisted ULAS mitigates both IAI and MUI unlike the classical detector, those try to moderate either IAI or MUI. By contrast, the proposed ULAS detector provides performance close to optimal maximum likelihood detector with just a fraction of its complexity.

     

Design of near‐optimal local likelihood search‐based detection algorithm for coded large‐scale MU‐MIMO system

Massive multiuser multiple input multiple output (MU‐MIMO) system is aimed to improve throughput and spectral efficiency through a large number of antennas incorporated at the transmitter and/or receiver. However, the MU‐MIMO system usually suffers from interantenna interference (IAI) and multiuser interference (MUI). The IAI imposes due to closely spaced antennas at each user equipment (UE), and MUI is enforced when one user comes under the vicinity of another user in the same cellular network. Most of the previous literatures considered any one of these interferences. However, the present work proposes singular value decomposition (SVD) precoding‐assisted user‐level local likelihood ascent search (LLAS) algorithm to mitigate both IAI and MUI. In the uplink MU‐MIMO, the IAI is cancelled by SVD, and the residual MUI is mitigated by LLAS detection. The LLAS detection balances the trade‐off between the classical suboptimal likelihood ascent search (LAS) and optimal maximum likelihood (ML) detection techniques. The proposed LLAS performs local search among all 2M_T‐dimensional neighborhood vectors at each UE, where M_T represents number of transmitting antennas of each UE. Thus, its performance is near optimal, and its complexity is much lower than ML detector.