Capacity Analysis of Adaptive IRS-Aided Transmission with Direct Link in
Nakagami-m Fading Channels
Abstract
This paper introduces a new analytical framework to evaluate the
capacity of intelligent reconfigurable surface (IRS)-aided wireless
networks in the presence of a direct link (DL). The obtained analysis is
used to characterize the signal-to-noise ratio (SNR) at the user
equipment (UE) while using adaptive power and rate transmission. In
particular, we consider the channel inversion with a fixed rate, optimum
power and rate adaptation, and the truncated channel inversion with a
fixed rate. The obtained expressions are derived in a unified
closed-form. All the single-hop channel gains are modeled as independent
and identically distributed Nakagami-m fading channels. Consequently,
the channels’ gains at the receiver become independent and
non-identically distributed. The moment generating function (MGF) is
used to derive an accurate approximation of the probability density and
cumulative distribution functions of the instantaneous SNR, which are
used to evaluate the channel capacity at low and high SNRs to quantify
the achievable multiplexing gain. The obtained analytical and simulation
results indicated that a strong DL may significantly enhance the channel
capacity gain obtained using the IRS. In particular scenarios, the
capacity improved by about 30% for a large number of IRS elements when
the DL Nakagami fading parameter m is increased from 2 to 6.