Jamming Energy Allocation in Training-Based Multiple Access Systems

TL;DR

This paper analyzes how a jammer can optimally allocate energy during training and data phases to minimize the sum-rate in a multiple access system, providing bounds and demonstrating significant impact.

Contribution

It derives bounds on sum-rate under jamming and analytically determines the optimal energy allocation for the jammer, a novel approach in training-based systems.

Findings

Optimal jamming reduces sum-rate significantly.

Energy allocation between phases is critical for effective jamming.

Bounds on ergodic sum-rate quantify jamming impact.

Abstract

We consider the problem of jamming attack in a multiple access channel with training-based transmission. First, we derive upper and lower bounds on the maximum achievable ergodic sum-rate which explicitly shows the impact of jamming during both the training phase and the data transmission phase. Then, from the jammer's design perspective, we analytically find the optimal jamming energy allocation between the two phases that minimizes the derived bounds on the ergodic sum-rate. Numerical results demonstrate that the obtained optimal jamming design reduces the ergodic sum-rate of the legitimate users considerably in comparison to fixed power jamming.