207 research outputs found
Energy detection based spectrum sensing over enriched multipath fading channels
Energy detection has been for long constituting the most popular sensing method in RADAR and cognitive radio systems. The present paper investigates the sensing behaviour of an energy detector over Hoyt fading channels, which have been extensively shown to provide rather accurate characterization of enriched multipath fading conditions. To this end, a simple series representation and an exact closed-form expression are firstly derived for the corresponding average probability of detection for the conventional single-channel communication scenario. These expressions are subsequently employed in deriving novel analytic results for the case of both collaborative detection and square-law selection diversity reception. The derived expressions have a relatively tractable algebraic representation which renders them convenient to handle both analytically and numerically. As a result, they can be utilized in quantifying the effect of fading in energy detection based spectrum sensing and in the determination of the trade-offs between sensing performance and energy efficiency in cognitive radio communications. Based on this, it is shown that the performance of the energy detector depends highly on the severity of fading as even slight variations of the fading conditions affect the value of the average probability of detection. It is also clearly shown that the detection performance improves substantially as the number of branches or collaborating users increase. This improvement is substantial in both moderate and severe fading conditions and can practically provide full compensation for the latter cases
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Performance analysis of energy detector over generalised wireless channels in cognitive radio
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.This thesis extensively analyses the performance of an energy detector which is
widely employed to perform spectrum sensing in cognitive radio over different generalised
channel models. In this analysis, both the average probability of detection and
the average area under the receiver operating characteristic curve (AUC) are derived
using the probability density function of the received instantaneous signal to noise
ratio (SNR). The performance of energy detector over an ŋ --- µ fading, which is used
to model the Non-line-of-sight (NLoS) communication scenarios is provided. Then,
the behaviour of the energy detector over к --- µ shadowed fading channel, which is
a composite of generalized multipath/shadowing fading channel to model the lineof-
sight (LoS) communication medium is investigated. The analysis of the energy
detector over both ŋ --- µ and к --- µ shadowed fading channels are then extended to
include maximal ratio combining (MRC), square law combining (SLC) and square
law selection (SLS) with independent and non-identically (i:n:d) diversity branches.
To overcome the problem of mathematical intractability in analysing the energy
detector over i:n:d composite fading channels with MRC and selection combining
(SC), two different unified statistical properties models for the sum and the maximum
of mixture gamma (MG) variates are derived. The first model is limited by the value
of the shadowing severity index, which should be an integer number and has been
employed to study the performance of energy detector over composite α --- µ /gamma
fading channel. This channel is proposed to represent the non-linear prorogation
environment. On the other side, the second model is general and has been utilised to
analyse the behaviour of energy detector over composite ŋ --- µ /gamma fading channel.
Finally, a special filter-bank transform which is called slantlet packet transform
(SPT) is developed and used to estimate the uncertain noise power. Moreover, signal
denoising based on hybrid slantlet transform (HST) is employed to reduce the noise
impact on the performance of energy detector. The combined SPT-HST approach
improves the detection capability of energy detector with 97% and reduces the total
computational complexity by nearly 19% in comparison with previously implemented
work using filter-bank transforms. The aforementioned percentages are measured at
specific SNR, number of selected samples and levels of signal decompositionMartyrs Foundatio
Adaptive Communication for Wireless Massive MIMO Systems
The demand for high data rates in wireless communications is increasing rapidly. One way to provide reliable communication with increased rates is massive multiple-input multiple-output (MIMO) systems where a large number of antennas is deployed. We analyze three systems utilizing a large number of antennas to provide enhancement in the performance of wireless communications. First, we consider a general form of spatial modulation (SM) systems where the number of transmitted data streams is allowed to vary and we refer to it as generalized spatial modulation with multiplexing (GSMM). A Gaussian mixture model (GMM) is shown to accurately model the transmitted spatially modulated signal using a precoding framework. Using this transmit model, a general closed-form expression for the achievable rate when operating over Rayleigh fading channels is evaluated along with a tight upper and a lower bounds for the achievable rate. The obtained expressions are flexible enough to accommodate any form of SM by adjusting the precoding set. Followed by that, we study quantized distributed wireless relay networks where a relay consisting of many geographically dispersed nodes is facilitating communication between unconnected users. Due to bandwidth constraints, distributed relay networks perform quantization at the relay nodes, and hence they are referred to as quantized distributed relay networks. In such systems, users transmit their data simultaneously to the relay nodes through the uplink channel that quantize their observed signals independently to a few bits and broadcast these bits to the users through the downlink channel. We develop algorithms that can be employed by the users to estimate the uplink channels between all users and all relay nodes when the relay nodes are performing simple sign quantization. This setup is very useful in either extending coverage to unconnected regions or replacing the existing wireless infrastructure in case of disasters. Using the uplink channel estimates, we propose multiple decoders that can be deployed at the receiver side. We also study the performance of each of these decoders under different system assumptions. A different quantization framework is also proposed for quantized distributed relay networking where the relay nodes perform vector quantization instead of sign quantization. Applying vector quantization at the relay nodes enables us to propose an algorithm that allocates quantization resources efficiently among the relay nodes inside the relay network. We also study the beamforming design at the users’ side in this case where beamforming design is not trivial due to the quantization that occurs at the relay network. Finally, we study a different setup of distributed communication systems called cell-free massive MIMO. In cell-free massive MIMO, regular cellular communication is replaced by multiple access points (APs) that are placed randomly over the coverage area. All users in the coverage area are sharing time and frequency resources and all APs are serving all UEs while power allocation is done in a central processor that is connected to the APs through a high speed backhaul network. We study the power allocation in cell-free massive MIMO system where APs are equipped with few antennas and how the distribution of the available antennas among access points affects both the performance and the infrastructure cost
Multidimensional Index Modulation for 5G and Beyond Wireless Networks
This study examines the flexible utilization of existing IM techniques in a
comprehensive manner to satisfy the challenging and diverse requirements of 5G
and beyond services. After spatial modulation (SM), which transmits information
bits through antenna indices, application of IM to orthogonal frequency
division multiplexing (OFDM) subcarriers has opened the door for the extension
of IM into different dimensions, such as radio frequency (RF) mirrors, time
slots, codes, and dispersion matrices. Recent studies have introduced the
concept of multidimensional IM by various combinations of one-dimensional IM
techniques to provide higher spectral efficiency (SE) and better bit error rate
(BER) performance at the expense of higher transmitter (Tx) and receiver (Rx)
complexity. Despite the ongoing research on the design of new IM techniques and
their implementation challenges, proper use of the available IM techniques to
address different requirements of 5G and beyond networks is an open research
area in the literature. For this reason, we first provide the dimensional-based
categorization of available IM domains and review the existing IM types
regarding this categorization. Then, we develop a framework that investigates
the efficient utilization of these techniques and establishes a link between
the IM schemes and 5G services, namely enhanced mobile broadband (eMBB),
massive machine-type communications (mMTC), and ultra-reliable low-latency
communication (URLLC). Additionally, this work defines key performance
indicators (KPIs) to quantify the advantages and disadvantages of IM techniques
in time, frequency, space, and code dimensions. Finally, future recommendations
are given regarding the design of flexible IM-based communication systems for
5G and beyond wireless networks.Comment: This work has been submitted to Proceedings of the IEEE for possible
publicatio
Application of statistical decision theory to the detection of radiotelegraph signals
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