Design of Waveform Set for Multiuser Ultra-Wideband Communications

The thesis investigates the design of analogue waveform sets for multiuser and UWB communications using suitably chosen Hermite-Rodriguez basis functions. The non-linear non-convex optimization problem with time and frequency domains constraints has been transformed into suitable forms and then solved using a standard optimization package. The proposed approach is more flexible and efficient than existing approaches in the literature. Numerical results show that orthogonal waveform sets with high spectral efficiency can be produced

An energy-efficient spectrum-aware reinforcement learning-based clustering algorithm for cognitive radio sensor networks

It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach

Route selection for multi-hop cognitive radio networks using reinforcement learning: an experimental study

Cognitive radio (CR) enables unlicensed users to explore and exploit underutilized licensed channels (or white spaces). While multi-hop CR network has drawn significant research interest in recent years, majority work has been validated through simulation. A key challenge in multi-hop CR network is to select a route with high quality of service (QoS) and lesser number of route breakages. In this paper, we propose three route selection schemes to enhance the network performance of CR networks, and investigate them using a real testbed environment, which consists of universal software radio peripheral and GNU radio units. Two schemes are based on reinforcement learning (RL), while a scheme is based on spectrum leasing (SL). RL is an artificial intelligence technique, whereas SL is a new paradigm that allows communication between licensed and unlicensed users in CR networks. We compare the route selection schemes with an existing route selection scheme in the literature, called highest-channel (HC), in a multi-hop CR network. With respect to the QoS parameters (i.e., throughput, packet delivery ratio, and the number of route breakages), the experimental results show that RL approaches achieve a better performance in comparison with the HC approach, and also achieve close to the performance achieved by the SL approach

A Hierarchical Load Balancing Strategy Considering Communication Delay Overhead for Large Distributed Computing Systems

Load balancing technology can effectively exploit potential enormous compute power available on distributed systems and achieve scalability. Communication delay overhead on distributed system, which is time-varying and is usually ignored or assumed to be deterministic for traditional load balancing strategies, can greatly degrade the load balancing performance. Considering communication delay overhead and its time-varying feature, a hierarchical load balancing strategy based on generalized neural network (HLBSGNN) is presented for large distributed systems. The novelty of the HLBSGNN is threefold: (1) the hierarchy with optimized communication is employed to reduce load balancing overhead for large distributed computing systems, (2) node computation rate and communication delay randomness imposed by the communication medium are considered, and (3) communication and migration overheads are optimized via forecasting delay. Comparisons with traditional strategies, such as centralized, distributed, and random delay strategies, indicate that the HLBSGNN is more effective and efficient