Open Access

ARTICLE

Deep Learning-Based Decoding and AP Selection for Radio Stripe Network

Aman Kumar Mishra, Vijayakumar Ponnusamy^*

Department of ECE, SRMIST, Chengalpattu, 603203, India

* Corresponding Author: Vijayakumar Ponnusamy. Email:

Intelligent Automation & Soft Computing 2022, 32(1), 131-148. https://doi.org/10.32604/iasc.2022.021017

Received 19 June 2021; Accepted 04 August 2021; Issue published 26 October 2021

Abstract

Cell-Free massive MIMO (mMIMO) offers promising features such as higher spectral efficiency, higher energy efficiency and superior spatial diversity, which makes it suitable to be adopted in beyond 5G (B5G) networks. However, the original form of Cell-Free massive MIMO requires each AP to be connected to CPU via front haul (front-haul constraints) resulting in huge economic costs and network synchronization issues. Radio Stripe architecture of cell-free mMIMO is one such architecture of cell-free mMIMO which is suitable for practical deployment. In this paper, we propose DNN Based Parallel Decoding in Radio Stripe (DNNBPDRS) to decode the symbols of User Equipments (UEs) in the uplink in a parallel fashion to reduce computational complexity by reducing delay in processing. Moreover, to solve the issue of Access Point (AP) selection in radio stripe networks, we propose a Channel link-based AP selection (CLBAPS) algorithm to choose the best APs in terms of channel link quality. The proposed DNNBPDRS framework not only improves Symbol Error Rate (SER) performance when compared to counterparts but is also proved to be comparatively far lesser computational complex. Moreover, the numerical result indicates the proposed AP selection algorithm CLBAPS performs better than random selection of AP in radio stripe networks.

---

Keywords

---