Analog‐to‐Digital Conversion for Cognitive Radio: Subsampling, Interleaving, and Compressive Sensing

This chapter explores different analog-to-digital conversion techniques that are suitable to be implemented in cognitive radio receivers. This chapter details the fundamentals, advantages, and drawbacks of three promising techniques: subsampling, interleaving, and compressive sensing. Due to their major maturity, subsampling- and interleaving-based systems are described in further detail, whereas compressive sensing-based systems are described as a complement of the previous techniques for underutilized spectrum applications. The feasibility of these techniques as part of software-defined radio, multistandard, and spectrum sensing receivers is demonstrated by proposing different architectures with reduced complexity at circuit level, depending on the application requirements. Additionally, the chapter proposes different solutions to integrate the advantages of these techniques in a unique analog-to-digital conversion process

Mitigation of nonlinear receiver effects in modern radar: advanced signal processing techniques

This thesis presents a study into nonlinearities in the radar receiver and investigates advanced digital signal processing (DSP) techniques capable of mitigating the resultant deleterious effects. The need for these mitigation techniques has become more prevalent as the use of commercial radar sensors has increased rapidly over the last decade. While advancements in low-cost radio frequency (RF) technologies have made mass-produced radar systems more feasible, they also pose a significant risk to the functionality of the sensor. One of the major compromises when employing low-cost commercial off-theshelf (COTS) components in the radar receiver is system linearity. This linearity trade-off leaves the radar susceptible to interfering signals as the RF receiver can now be driven into the weakly nonlinear regime. Radars are not designed to operate in the nonlinear regime as distortion is observed in the radar output if they do. If radars are to maintain operational performance in an RF environment that is becoming increasingly crowded, novel techniques that allow the sensor to operate in the nonlinear regime must be developed. Advanced DSP techniques offer a low-cost low-impact solution to the nonlinear receiver problem in modern radar. While there is very little work published on this topic in the radar literature, inspiration can be taken from the related field of communications where techniques have been successfully employed. It is clear from the communications literature that for any mitigation algorithm to be successful, the mechanisms driving the nonlinear distortion in the receiver must be understood in great detail. Therefore, a behavioural modelling technique capable of capturing both the nonlinear amplitude and phase effects in the radar receiver is presented before any mitigation techniques are studied. Two distinct groups of mitigation algorithms are then developed specifically for radar systems with their performance tested in the medium pulse repetition frequency (MPRF) mode of operation. The first of these is the look-up table (LUT) approach which has the benefit of being mode independent and computationally inexpensive to implement. The limitations of this communications-based technique are discussed with particular emphasis placed on its performance against receiver nonlinearities that exhibit complex nonlinear memory effects. The second group of mitigation algorithms to be developed is the forward modelling technique. While this novel technique is both mode dependent and computationally intensive to implement, it has a unique formalisation that allows it to be extended to include nonlinear memory effects in a well-defined manner. The performance of this forward modelling technique is analysed and discussed in detail. It was shown in this study that nonlinearities generated in the radar receiver can be successfully mitigated using advanced DSP techniques. For this to be the case however, the behaviour of the RF receiver must be characterised to a high degree of accuracy both in the linear and weakly nonlinear regimes. In the case where nonlinear memory effects are significant in the radar receiver, it was shown that memoryless mitigation techniques can become decorrelated drastically reducing their effectiveness. Importantly however, it was demonstrated that the LUT and forward modelling techniques can both be extended to compensate for complex nonlinear memory effects generated in the RF receiver. It was also found that the forward modelling technique dealt with the nonlinear memory effects in a far more robust manner than the LUT approach leading to a superior mitigation performance in the memory rich case

Machine Learning in Digital Signal Processing for Optical Transmission Systems

The future demand for digital information will exceed the capabilities of current optical communication systems, which are approaching their limits due to component and fiber intrinsic non-linear effects. Machine learning methods are promising to find new ways of leverage the available resources and to explore new solutions. Although, some of the machine learning methods such as adaptive non-linear filtering and probabilistic modeling are not novel in the field of telecommunication, enhanced powerful architecture designs together with increasing computing power make it possible to tackle more complex problems today. The methods presented in this work apply machine learning on optical communication systems with two main contributions. First, an unsupervised learning algorithm with embedded additive white Gaussian noise (AWGN) channel and appropriate power constraint is trained end-to-end, learning a geometric constellation shape for lowest bit-error rates over amplified and unamplified links. Second, supervised machine learning methods, especially deep neural networks with and without internal cyclical connections, are investigated to combat linear and non-linear inter-symbol interference (ISI) as well as colored noise effects introduced by the components and the fiber. On high-bandwidth coherent optical transmission setups their performances and complexities are experimentally evaluated and benchmarked against conventional digital signal processing (DSP) approaches. This thesis shows how machine learning can be applied to optical communication systems. In particular, it is demonstrated that machine learning is a viable designing and DSP tool to increase the capabilities of optical communication systems

Ultra Wideband

Ultra wideband (UWB) has advanced and merged as a technology, and many more people are aware of the potential for this exciting technology. The current UWB field is changing rapidly with new techniques and ideas where several issues are involved in developing the systems. Among UWB system design, the UWB RF transceiver and UWB antenna are the key components. Recently, a considerable amount of researches has been devoted to the development of the UWB RF transceiver and antenna for its enabling high data transmission rates and low power consumption. Our book attempts to present current and emerging trends in-research and development of UWB systems as well as future expectations

Resource allocation in non-orthogonal multiple access technologies for 5G networks and beyond.

Doctoral Degree. University of KwaZulu-Natal, Durban.The increasing demand of mobile and device connectivity poses challenging requirements for 5G wireless communications, such as high energy- and spectral-efficiency and low latency. This necessitates a shift from orthogonal multiple access (OMA) to Non-Orthogonal Multiple Access (NOMA) techniques, namely, power-domain NOMA (PD-NOMA) and code-domain NOMA (CD-NOMA). The basic idea behind NOMA schemes is to co-multiplex different users on the same resource elements (time slot, OFDMA sub-carrier, or spreading code) via power domain (PD) or code domain (CD) at the transmitter while permitting controllable interference, and their successful multi-user detection (MUD) at the receiver albeit, increased computational complexity. In this work, an analysis on the performance of the existing NOMA schemes is carried out. Furthermore, we investigate the feasibility of a proposed uplink hybrid-NOMA scheme namely power domain sparse code multiple access (PD-SCMA) that integrates PD-NOMA and CD-NOMA based sparse code multiple access (SCMA) on heterogeneous networks (HetNets). Such hybrid schemes come with resource allocation (RA) challenges namely; codebook allocation, user pairing and power allocation. Therefore, hybrid RA schemes namely: Successive Codebook Ordering Assignment (SCOA) for codebook assignment (CA), opportunistic macro cell user equipment (MUE)- small cell user equipment (SUE) pairing (OMSP) for user pairing (UP), and a QoS-aware power allocation (QAPA) for power allocation (PA) are developed for an energy efficient (EE) system. The performance of the RA schemes is analyzed alongside an analytical RA optimization algorithm. Through numerical results, the proposed schemes show significant improvements in the EE of the small cells in comparison with the prevalent schemes. Additionally, there is significant sum rate performance improvement over the conventional SCMA and PD-NOMA. Secondly, we investigate the multiplexing capacity of the hybrid PD-SCMA scheme in HetNets. Particularly, we investigate and derive closed-form solutions for codebook capacity, MUE multiplexing and power capacity bounds. The system’s performance results into low outage when the system’s point of operation is within the multiplexing bounds. To alleviate the RA challenges of such a system at the transmitter, dual parameter ranking (DPR) and alternate search method (ASM) based RA schemes are proposed. The results show significant capacity gain with DPR-RA in comparison with conventional RA schemes. Lastly, we investigate the feasibility of integrating the hybrid PD-SCMA with multiple-input multipleoutput (MIMO) technique namely, M-PD-SCMA. The attention to M-PD-SCMA resides in the need of lower number of antennas while preserving the system capacity thanks to the overload in PDSCMA. To enhance spectral efficiency and error performance we propose spatial multiplexing at the transmitter and a low complex joint MUD scheme based on successive interference cancellation (SIC) and expectation propagation algorithm (EPA) at the receiver are proposed. Numerical results exhibit performance benchmark with PD-SCMA schemes and the proposed receiver achieves guaranteed bit error rate (BER) performance with a bounded increase in the number of transmit and receive antennas. Thus, the feasibility of an M-PD-SCMA system is validated

Adaptive Baseband Pro cessing and Configurable Hardware for Wireless Communication

The world of information is literally at one’s fingertips, allowing access to previously unimaginable amounts of data, thanks to advances in wireless communication. The growing demand for high speed data has necessitated theuse of wider bandwidths, and wireless technologies such as Multiple-InputMultiple-Output (MIMO) have been adopted to increase spectral efficiency.These advanced communication technologies require sophisticated signal processing, often leading to higher power consumption and reduced battery life.Therefore, increasing energy efficiency of baseband hardware for MIMO signal processing has become extremely vital. High Quality of Service (QoS)requirements invariably lead to a larger number of computations and a higherpower dissipation. However, recognizing the dynamic nature of the wirelesscommunication medium in which only some channel scenarios require complexsignal processing, and that not all situations call for high data rates, allowsthe use of an adaptive channel aware signal processing strategy to provide adesired QoS. Information such as interference conditions, coherence bandwidthand Signal to Noise Ratio (SNR) can be used to reduce algorithmic computations in favorable channels. Hardware circuits which run these algorithmsneed flexibility and easy reconfigurability to switch between multiple designsfor different parameters. These parameters can be used to tune the operations of different components in a receiver based on feedback from the digitalbaseband. This dissertation focuses on the optimization of digital basebandcircuitry of receivers which use feedback to trade power and performance. Aco-optimization approach, where designs are optimized starting from the algorithmic stage through the hardware architectural stage to the final circuitimplementation is adopted to realize energy efficient digital baseband hardwarefor mobile 4G devices. These concepts are also extended to the next generation5G systems where the energy efficiency of the base station is improved.This work includes six papers that examine digital circuits in MIMO wireless receivers. Several key blocks in these receiver include analog circuits thathave residual non-linearities, leading to signal intermodulation and distortion.Paper-I introduces a digital technique to detect such non-linearities and calibrate analog circuits to improve signal quality. The concept of a digital nonlinearity tuning system developed in Paper-I is implemented and demonstratedin hardware. The performance of this implementation is tested with an analogchannel select filter, and results are presented in Paper-II. MIMO systems suchas the ones used in 4G, may employ QR Decomposition (QRD) processors tosimplify the implementation of tree search based signal detectors. However,the small form factor of the mobile device increases spatial correlation, whichis detrimental to signal multiplexing. Consequently, a QRD processor capableof handling high spatial correlation is presented in Paper-III. The algorithm and hardware implementation are optimized for carrier aggregation, which increases requirements on signal processing throughput, leading to higher powerdissipation. Paper-IV presents a method to perform channel-aware processingwith a simple interpolation strategy to adaptively reduce QRD computationcount. Channel properties such as coherence bandwidth and SNR are used toreduce multiplications by 40% to 80%. These concepts are extended to usetime domain correlation properties, and a full QRD processor for 4G systemsfabricated in 28 nm FD-SOI technology is presented in Paper-V. The designis implemented with a configurable architecture and measurements show thatcircuit tuning results in a highly energy efficient processor, requiring 0.2 nJ to1.3 nJ for each QRD. Finally, these adaptive channel-aware signal processingconcepts are examined in the scope of the next generation of communicationsystems. Massive MIMO systems increase spectral efficiency by using a largenumber of antennas at the base station. Consequently, the signal processingat the base station has a high computational count. Paper-VI presents a configurable detection scheme which reduces this complexity by using techniquessuch as selective user detection and interpolation based signal processing. Hardware is optimized for resource sharing, resulting in a highly reconfigurable andenergy efficient uplink signal detector