Abstract

We consider a multi-user multiple-input multiple-output (MIMO) setup where full-duplex (FD) multi-antenna nodes apply linear beamformers to simultaneously transmit and receive multiple streams over Rician fading channels. The exact first and second positive moments of the residual self-interference (SI), involving the squared norm of a sum of non-identically distributed random variables, are derived in closed-form. The method of moments is hence invoked to provide a Gamma approximation for the residual SI distribution. The proposed theorem holds under arbitrary linear precoder/decoder design, number of antennas and streams, and SI cancellation capability.

Highlights

  • INTRODUCTIONT O DATE, wireless systems, have been designed and dimensioned under a complete separation of the transmit and receive functions, a.k.a., half-duplex (HD) mode

  • T O DATE, wireless systems, have been designed and dimensioned under a complete separation of the transmit and receive functions, a.k.a., half-duplex (HD) mode. This is typically achieved via orthogonal radio frequency (RF) partitioning, e.g., in time-division duplex (TDD) and frequencydivision duplex (FDD) systems

  • Consider L cells, where in each cell l, l ∈ L = {1, . . . , L}, a FD multi-antenna node l0 communicates with respect to multiple FD radios lk, k ∈ K = {1, . . . , K }

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Summary

INTRODUCTION

T O DATE, wireless systems, have been designed and dimensioned under a complete separation of the transmit and receive functions, a.k.a., half-duplex (HD) mode. This is typically achieved via orthogonal radio frequency (RF) partitioning, e.g., in time-division duplex (TDD) and frequencydivision duplex (FDD) systems. The Rician fading model is employed to capture the residual SI under arbitrary cancellation through tuning of the distribution parameters by design or measurements. We exploit the method of moments in order to obtain an explicit Gamma approximation for the distribution of the residual SI over FD multi-user MIMO Rician fading channels. Notation: X is a matrix with (n, m)-th entry {X}n,m; x is a vector with k-th element {x}k; T , †, and + are the transpose, Hermitian-transpose, and pseudo-inverse; E{.} is the expected value; V{.} is the variance; P(.) is the probability density function (pdf); |.| is the modulus; . is the norm; and I0(.) is the zeroth-order Bessel function of the first kind, respectively

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