Multiple Symbol Double Differential Transmission for Amplify-and-Forward Cooperative Diversity Networks in Time-Varying Channel

In the cooperative diversity wireless networks, the task to perform cooperation communication amongst neighbouring nodes is very challenging. Subjected to rapidly increasing mobility of the nodes i.e. wireless devices in fast moving vehicles and trains, at the destination end the receiver may not ideally estimate the channel characteristics and frequency offsets. Due to these circumstances which results in time-varying channels, the performance network degrades drastically. In order to enhance the performance in such environment, Double Differential (DD) modulation employing multiple symbol based detection is proposed which takes mobility environment of different nodes into consideration. By utilizing the DD transmission approach, the channel properties and frequency offset estimation is omitted in the amplify-andforward cooperative networks. The MATLAB simulation and numerical analysis on Bit Error Rate (BER) are carried out with consideration on considering flat-fading (i.e. the frequency non-selective) Rayleigh channels and when frequency offsets. The results depict that the proposed method over fading channels without channel estimation requirements and in the presence of frequency offsets performs better as compared to the conventional DD transmission. Optimized power allocation is also carried out to enhance the network performance by minimizing the BER analytical expression. It is demonstrated that the proposed power allocation scheme offers enhancement over the equally distributed power allocation approach

Performance analysis of diversity techniques in wireless communication systems: Cooperative systems with CCI and MIMO-OFDM systems

This Dissertation analyzes the performance of ecient digital commu- nication systems, the performance analysis includes the bit error rate (BER) of dier- ent binary and M-ary modulation schemes, and the average channel capacity (ACC) under dierent adaptive transmission protocols, namely, the simultaneous power and rate adaptation protocol (OPRA), the optimal rate with xed power protocol (ORA), the channel inversion with xed rate protocol (CIFR), and the truncated channel in- version with xed transmit power protocol (CTIFR). In this dissertation, BER and ACC performance of interference-limited dual-hop decode-and-forward (DF) relay- ing cooperative systems with co-channel interference (CCI) at both the relay and destination nodes is analyzed in small-scale multipath Nakagami-m fading channels with arbitrary (integer as well as non-integer) values of m. This channel condition is assumed for both the desired signal as well as co-channel interfering signals. In addition, the practical case of unequal average fading powers between the two hops is assumed in the analysis. The analysis assumes an arbitrary number of indepen- dent and non-identically distributed (i.n.i.d.) interfering signals at both relay (R) and destination (D) nodes. Also, the work extended to the case when the receiver employs the maximum ratio combining (MRC) and the equal gain combining (EGC) schemes to exploit the diversity gain

Learning-based communication system design – autoencoder for (differential) block coded modulation designs and path loss predictions

Shannon’s channel coding theorem states the existence of long random codes that can make the error probability arbitrarily small. Recently, advanced error-correcting codes such as turbo and low-density parity-check codes have almost reached the theoretical Shannon limit for binary additive white Gaussian noise channels. However, designing optimal high-rate short-block codes with automatic bit-labeling for various wireless networks is still an unsolved problem. Deep-learning-based autoencoders (AE) have appeared as a potential near-optimal solution for designing wireless communications systems. We take a holistic approach that jointly optimizes all the components of the communication networks by performing data-driven end-to-end learning of the neural network-based transmitter and receiver together. Specifically, to tackle the fading channels, we show that AE frameworks can perform near-optimal block coded-modulation (BCM) and differential BCM (d-BCM) designs in the presence and absence of the channel state information knowledge. Moreover, we focus on AE-based designing of high-rate short block codes with automatic bit-labeling that are capable of outperforming conventional networks with larger margins as the rate R increases. We also investigate the BCM and d-BCM from an information-theoretic perspective. With the advent of internet-of-things (IoT) networks and the widespread use of small devices, we face the challenge of limited available bandwidth. Therefore, novel techniques need to be utilized, such as full-duplex (FD) mode transmission reception at the base station for the full utilization of the spectrum, and non-orthogonal multiple access (NOMA) at the user-end for serving multiple IoT devices while fulfilling their quality-of-service requirement. Furthermore, the deployment of relay nodes will play a pivotal role in improving network coverage, reliability, and spectral efficiency for the future 5G networks. Thus, we design and develop novel end-to-end-learning-based AE frameworks for BCM and d-BCM in various scenarios such as amplify-and-forward and decode-and-forward relaying networks, FD relaying networks, and multi-user downlink networks. We focus on interpretability and understand the AE-based BCM and d-BCM from an information-theoretic perspective, such as the AE’s estimated mutual information, convergence, loss optimization, and training principles. We also determine the distinct properties of AE-based (differential) coded-modulation designs in higher-dimensional space. Moreover, we also studied the reproducibility of the trained AE framework. In contrast, large bandwidth and worldwide spectrum availability at mm-wave bands have also shown a great potential for 5G and beyond, but the high path loss (PL) and significant scattering/absorption loss make the signal propagation challenging. Highly accurate PL prediction is fundamental for mm-wave network planning and optimization, whereas existing methods such as slope-intercept models and ray tracing fall short in capturing the large street-by-street variation seen in urban cities. We also exploited the potential benefits of AE framework-based compression capabilities in mm-wave PL prediction. Specifically, we employ extensive 28 GHz measurements from Manhattan Street canyons and model the street clutters via a LiDAR point cloud dataset and 3D-buildings by a mesh-grid building dataset. We aggressively compress 3D-building shape information using convolutional-AE frameworks to reduce overfitting and propose a machine learning (ML)-based PL prediction model for mm-wave propagation.EPSRC-UKRI fundin

Multiple antenna communication over fading channels : Performance limits and cooperative transmission strategies

Ph.DDOCTOR OF PHILOSOPH

Practical packet combining for use with cooperative and non-cooperative ARQ schemes in wireless sensor networks

Although it is envisaged that advances in technology will follow a "Moores Law" trend for many years to come, one of the aims of Wireless Sensor Networks (WSNs) is to reduce the size of the nodes as much as possible. The issue of limited resources on current devices may therefore not improve much with future designs as a result. There is a pressing need, therefore, for simple, efficient protocols and algorithms that can maximise the use of available resources in an energy efficient manner. In this thesis an improved packet combining scheme useful on low power, resource-constrained sensor networks is developed. The algorithm is applicable in areas where currently only more complex combining approaches are used. These include cooperative communications and hybrid-ARQ schemes which have been shown to be of major benefit for wireless communications. Using the packet combining scheme developed in this thesis more than an 85% reduction in energy costs are possible over previous, similar approaches. Both simulated and practical experiments are developed in which the algorithm is shown to offer up to approximately 2.5 dB reduction in the required Signal-to-Noise ratio (SNR) for a particular Packet Error Rate (PER). This is a welcome result as complex schemes, such as maximal-ratio combining, are not implementable on many of the resource constrained devices under consideration. A motivational side study on the transitional region is also carried out in this thesis. This region has been shown to be somewhat of a problem for WSNs. It is characterised by variable packet reception rate caused by a combination of fading and manufacturing variances in the radio receivers. Experiments are carried out to determine whether or not a spread-spectrum architecture has any effect on the size of this region, as has been suggested in previous work. It is shown that, for the particular setup tested, the transitional region still has significant extent even when employing a spread-spectrum architecture. This result further motivates the need for the packet combining scheme developed as it is precisely in zones such as the transitional region that packet combining will be of most benefit