Performance Analysis of Energy-Detection-Based Massive SIMO

TL;DR

This paper analyzes an energy-detection-based massive SIMO system, deriving error probabilities, optimal decision regions, and an iterative algorithm for constellation design to enhance energy efficiency and reliability.

Contribution

It introduces a novel iterative algorithm for optimal constellation design in energy-detection massive SIMO systems, with comprehensive performance analysis.

Findings

Derived average symbol error probability expressions.

Proposed an iterative algorithm for constellation optimization.

Numerical results show improved system performance.

Abstract

Recently, communications systems that are both energy efficient and reliable are under investigation. In this paper, we concentrate on an energy-detection-based transmission scheme where a communication scenario between a transmitter with one antenna and a receiver with significantly many antennas is considered. We assume that the receiver initially calculates the average energy across all antennas, and then decodes the transmitted data by exploiting the average energy level. Then, we calculate the average symbol error probability by means of a maximum a-posteriori probability detector at the receiver. Following that, we provide the optimal decision regions. Furthermore, we develop an iterative algorithm that reaches the optimal constellation diagram under a given average transmit power constraint. Through numerical analysis, we explore the system performance.