Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Transmit and Receive Antenna Port Selection for Channel Capacity Maximization in Fluid-MIMO Systems

Christos N. Efrem, Ioannis Krikidis

TL;DR

This letter presents a new joint convex relaxation (JCR) problem by using an upper bound on the channel capacity and exploiting the binary nature of optimization variables, and develops and analyzes two optimization algorithms with different performance-complexity tradeoffs.

Abstract

In this letter, we study a discrete optimization problem, namely, the maximization of channel capacity in fluid multiple-input multiple-output (fluid-MIMO) systems through the selection of antenna ports/positions at both the transmitter and the receiver. First, we present a new joint convex relaxation (JCR) problem by using an upper bound on the channel capacity and exploiting the binary nature of optimization variables. Then, we develop and analyze two optimization algorithms with different performance-complexity tradeoffs. The first algorithm is based on JCR and reduced exhaustive search (JCR&RES), while the second on JCR and alternating optimization (JCR&AO).

Transmit and Receive Antenna Port Selection for Channel Capacity Maximization in Fluid-MIMO Systems

TL;DR

This letter presents a new joint convex relaxation (JCR) problem by using an upper bound on the channel capacity and exploiting the binary nature of optimization variables, and develops and analyzes two optimization algorithms with different performance-complexity tradeoffs.

Abstract

In this letter, we study a discrete optimization problem, namely, the maximization of channel capacity in fluid multiple-input multiple-output (fluid-MIMO) systems through the selection of antenna ports/positions at both the transmitter and the receiver. First, we present a new joint convex relaxation (JCR) problem by using an upper bound on the channel capacity and exploiting the binary nature of optimization variables. Then, we develop and analyze two optimization algorithms with different performance-complexity tradeoffs. The first algorithm is based on JCR and reduced exhaustive search (JCR&RES), while the second on JCR and alternating optimization (JCR&AO).
Paper Structure (10 sections, 2 theorems, 12 equations, 5 figures, 1 table, 2 algorithms)

This paper contains 10 sections, 2 theorems, 12 equations, 5 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. Problem Formulation
  4. Joint Convex Relaxation
  5. Optimization Algorithms
  6. Exhaustive Search
  7. Joint Convex Relaxation & Reduced Exhaustive Search
  8. Joint Convex Relaxation & Alternating Optimization
  9. Numerical Results
  10. Conclusion

Key Result

Proposition 1

With a slight abuse of notation, we can rewrite the channel capacity given by equation:Channel_capacity_original as follows

Figures (5)

  • Figure 1: Fluid-MIMO system consisting of a transmitter and a receiver equipped with multiple fluid antennas.
  • Figure 2: Common legend for the next figures.
  • Figure 3: Channel capacity vs the number of ports per fluid antenna. JCR&AO requires about 1-3 AO-iterations to converge, on average, for all MIMO setups.
  • Figure 4: Channel capacity vs the average SNR per receive fluid antenna. JCR&AO requires around 1-3 AO-iterations to converge, on average, for all MIMO configurations.
  • Figure 5: Channel capacity vs the size of fluid antennas per wavelength. JCR&AO requires approximately 1-3 AO-iterations until convergence, on average, for all MIMO setups.

Theorems & Definitions (2)

  • Proposition 1
  • Proposition 2