2 research outputs found
Error Rate Analysis of Amplitude-Coherent Detection over Rician Fading Channels with Receiver Diversity
Amplitude-coherent (AC) detection is an efficient detection technique that
can simplify the receiver design while providing reliable symbol error rate
(SER). Therefore, this work considers AC detector design and SER analysis using
M-ary amplitude shift keying (MASK) modulation over Rician fading channels.
More specifically, we derive the optimum, near-optimum and a suboptimum AC
detectors and compare their SER to the coherent, noncoherent and the heuristic
AC detectors. Moreover, the analytical SER of the heuristic detector is derived
using two different approaches for single and multiple receiving antennas. One
of the derived expressions is expressed in terms of a single integral that can
be evaluated numerically, while the second approach gives a closed-form
analytical expression for the SER, which is also used to derive a simple
formula for the asymptotic SER at high signal-to-noise ratios (SNRs). The
obtained analytical and simulation results show that the SER of the AC and
coherent MASK detectors are comparable, particularly for high values of the
Rician K-factor, and small number of receiving antennas. Moreover, the obtained
results show that the SER of the optimal AC detector is equivalent to that of
the coherent detector. However, the optimal AC detector complexity is
prohibitively high, particularly at high SNRs. In most of the scenarios, the
heuristic AC detector significantly outperforms the optimum noncoherent
detector, except for the binary ASK case at low SNRs. Moreover, the obtained
results show that the heuristic AC detector is immune to phase noise, and thus,
it outperforms the coherent detector in scenarios where system is subject to
considerable phase noise
Performance Analysis of Integrated Sensing and Communications Under Gain-Phase Imperfections
This paper evaluates the performance of uplink integrated sensing and
communication systems in the presence of gain and phase imperfections.
Specifically, we consider multiple unmanned aerial vehicles (UAVs) transmitting
data to a multiple-input-multiple-output base-station (BS) that is responsible
for estimating the transmitted information in addition to localising the
transmitting UAVs. The signal processing at the BS is divided into two
consecutive stages: localisation and communication. A maximum likelihood (ML)
algorithm is introduced for the localisation stage to jointly estimate the
azimuth-elevation angles and Doppler frequency of the UAVs under gain-phase
defects, which are then compared to the estimation of signal parameters via
rotational invariance techniques (ESPRIT) and multiple signal classification
(MUSIC). Furthermore, the Cramer-Rao lower bound (CRLB) is derived to evaluate
the asymptotic performance and quantify the influence of the gain-phase
imperfections which are modelled using Rician and von Mises distributions,
respectively. Thereafter, in the communication stage, the location parameters
estimated in the first stage are employed to estimate the communication
channels which are fed into a maximum ratio combiner to preprocess the received
communication signal. An accurate closed-form approximation of the achievable
average sum data rate (SDR) for all UAVs is derived. The obtained results show
that gain-phase imperfections have a significant influence on both localisation
and communication, however, the proposed ML is less sensitive when compared to
other algorithms. The derived analysis is concurred with simulations.Comment: 38 pages, 7 figure