Paul D. Teal

Spatial correlation for general distributions of scatterers

The well-known results of the spatial correlation function for two-dimensional and three-dimensional diffuse fields of narrowband signals are generalized to the case of general distributions of scatterers. A method is presented that allows closed-form expressions for the correlation function to be obtained for arbitrary scattering distribution functions. These closed-form expressions are derived for a variety of commonly used scattering distribution functions.

Review: Reinforcement learning for context awareness and intelligence in wireless networks: Review, new features and open issues

In wireless networks, context awareness and intelligence are capabilities that enable each host to observe, learn, and respond to its complex and dynamic operating environment in an efficient manner. These capabilities contrast with traditional approaches where each host adheres to a predefined set of rules, and responds accordingly. In recent years, context awareness and intelligence have gained tremendous popularity due to the substantial network-wide performance enhancement they have to offer. In this article, we advocate the use of reinforcement learning (RL) to achieve context awareness and intelligence. The RL approach has been applied in a variety of schemes such as routing, resource management and dynamic channel selection in wireless networks. Examples of wireless networks are mobile ad hoc networks, wireless sensor networks, cellular networks and cognitive radio networks. This article presents an overview of classical RL and three extensions, including events, rules and agent interaction and coordination, to wireless networks. We discuss how several wireless network schemes have been approached using RL to provide network performance enhancement, and also open issues associated with this approach. Throughout the paper, discussions are presented in a tutorial manner, and are related to existing work in order to establish a foundation for further research in this field, specifically, for the improvement of the RL approach in the context of wireless networking, for the improvement of the RL approach through the use of the extensions in existing schemes, as well as for the design and implementation of RL in new schemes.

Cognitive Radio-based Wireless Sensor Networks: Conceptual design and open issues

Traditional static spectrum allocation policies have been to grant each wireless service exclusive usage of certain frequency bands, leaving several spectrum bands unlicensed for industrial, scientific and medical purposes. The rapid proliferation of low-cost wireless applications in unlicensed spectrum bands has resulted in spectrum scarcity among those bands. Since most applications in Wireless Sensor Networks (WSNs) utilize the unlicensed spectrum, network-wide performance of WSNs will inevitably degrade as their popularity increases. Sharing of under-utilized licensed spectrum among unlicensed devices is a promising solution to the spectrum scarcity issue. Cognitive Radio (CR) is a new paradigm in wireless communication that allows sensor nodes as the unlicensed users or Secondary Users (SUs) to detect and use the under-utilized licensed spectrum temporarily. Given that the licensed or Primary Users (PUs) are oblivious to the presence of SUs, the SUs access the licensed spectrum opportunistically without interfering the PUs, while improving their own performance. In this paper, we propose an approach to build Cognitive Radio-based Wireless Sensor Networks (CR-WSNs). We believe that CR-WSN is the next-generation WSN. Realizing that both WSNs and CR present unique challenges to the design of CR-WSNs, we provide an overview and conceptual design of WSNs from the perspective of CR. The open issues are discussed to motivate new research interests in this field. We also present our method to achieving context-awareness and intelligence, which are the key components in CR networks, to address an open issue in CR-WSN.

A context-aware and Intelligent Dynamic Channel Selection scheme for cognitive radio networks

The tremendous growth in ubiquitous low-cost wireless applications that utilize the unlicensed spectrum bands has laid increasing stress on the limited and scarce radio spectrum resources. Given that the licensed or Primary Users (PUs) are oblivious to the presence of unlicensed or Secondary Users (SUs), Cognitive Radio (CR) is a new paradigm in wireless communication that allows the SUs to detect and use the underutilized licensed spectrums opportunistically and temporarily. In this paper, we propose a Context-aware and Intelligent Dynamic Channel Selection scheme that helps SUs to select channel adaptively for data transmission to enhance QoS, particularly throughput and delay. Our scheme is suitable for CR networks with mobile hosts. We formulate and design our scheme using Reinforcement Learning that offers a simple and yet practical solution. Channel heterogeneity, which is a feature unique to CR networks that has been ignored in previous studies, is considered in this paper. Simulation results reveal that the proposed scheme achieves very good performance.

Short-term mobile channel prediction using discrete scatterer propagation model and subspace signal processing algorithms

The fading envelope encountered in narrowband mobile communications often dominates the impairments to communications usage of the channel. Signal processing techniques are extensively used to mitigate the limitations imposed by the fading, and many of the techniques require knowledge of the channel transfer function. The possibility of predicting the changing channel behaviour, in particular the position of the short-term fades, is therefore of interest for the subsequent communications signal processing. Different models of the multipath propagation encourage different algorithms for the model parameter estimation and channel behaviour prediction. These algorithms are reviewed and we present results from simulations showing the performance and limitations of prediction behaviour.

A constrained optimization approach for multi-zone surround sound

A recent approach to surround sound is to perform exact control of the sound field over a region of space. Here, the driving signals for an array of loudspeakers are chosen to create a desired sound field over an extended area. An interesting subtopic is multi-zone surround sound, where two or more listeners can experience totally independent sound fields. However, multi-zone surround sound is a challenge because implementation can be very non-robust. We formulate multi-zone sound reproduction as a convex optimization problem, where the sound energy leakage into other listener zones is limited to fixed levels, and a constraint is placed on the loudspeaker weights to improve the robustness. An interior point algorithm is devised for computing the loudspeaker weights, and its performance is compared with least squares approaches of multi-zone reproduction in typical two-zone cases.

Simulation and performance bounds for real-time prediction of the mobile multipath channel

The fading encountered in multipath mobile communication channels is often the primary cause of degradation in communication system performance. There are many situations in mobile communications in which it would be advantageous for a communications system to have real time information on how a signal will fade in advance of the fade actually occurring. This paper looks at the ways in which this real time prediction of the mobile channel can be achieved. Physical models of mobile channels which allow prediction are discussed. Algorithms based on these models, and their performance, are presented. Performance bounds for model based prediction based on the Cramer Rao bound are also derived. The algorithms are also applied to measured channel data.

Applications of Reinforcement Learning to Cognitive Radio Networks

Cognitive Radio (CR) enables an unlicensed user to change its transmission and reception parameters adaptively according to spectrum availability in a wide range of licensed channels. The concept of a Cognition Cycle (CC) is the key element of CR to provide context awareness and intelligence so that each unlicensed user is able to observe and carry out an optimal action on its operating environment for performance enhancement. The CC can be applied in various application schemes in CR networks such as Dynamic Channel Selection (DCS), topology management, congestion control, and scheduling. In this paper, Reinforcement Learning (RL) is applied to implement the conceptual of the CC. We provide an extensive overview of our work including single-agent and multi-agent approaches to show that RL is a promising technique. Our contribution in this paper is to propose various application schemes using our RL approach to warrant further research on RL in CR networks.

On Multi-Channel MAC Protocols in Cognitive Radio Networks

Cognitive Radio (CR) exploits underutilized licensed spectrums to improve its bandwidth availability. Using CR technology, a node is able to adapt its transmission and reception radio parameters including channel frequency dynamically according to local spectrum availability. For channel access between wireless nodes, a cognitive Medium Access Control (MAC) protocol is necessary to coordinate the CRs. Multi-channel MAC protocol extensions have been proposed in IEEE802.11 to enable a node to operate in multiple channels in order to improve network-wide throughput. These multi-channel MAC protocols have several functions that can be leveraged by cognitive MAC protocols due to their similarities in certain aspects, though the CR has an additional requirement to cope with the existence of licensed users that have higher authority over the channels. Current research in cognitive MAC protocols assumes the availability of a common control channel at all times, which is an approach in the multi-channel MAC protocols. This approach has certain hardware requirements that may not be readily available at CR nodes. Hence, other approached may be necessary. In this paper, various types of multi-channel MAC protocols are reviewed, followed by discussion of their merits and demerits in multi-channel environments. The purpose is to show the additional functionalities and challenges that each multi-channel MAC protocol has to offer and address in order to operate in multihop CR networks. By providing discussion on possible technology leverage from multi-channel to cognitive MAC protocols, we aim to establish a foundation for further research and discussion.

Context-awareness and intelligence in Distributed Cognitive Radio Networks: A Reinforcement Learning approach

Cognitive Radio (CR) is a next-generation wireless communication system that exploits underutilized licensed spectrum to improve the utilization of the overall radio spectrum. A Distributed Cognitive Radio Network (DCRN) is a distributed wireless network established by a number of CR hosts in the absence of fixed network infrastructure. Context-awareness and intelligence are key characteristics of CR networks that enable the CR hosts to be aware of their operating environment in order to achieve a joint action that improves network-wide performance in a distributed manner through learning. In this paper, we advocate the use of Reinforcement Learning (RL) in application schemes that require context-awareness and intelligence such as the Dynamic Channel Selection (DCS), scheduling, and congestion control. We investigate the performance of the RL in respect to DCS. We show that RL and our enhanced RL approach are able to converge to a joint action that provide better network-wide performance. We also show the effects of network density and various essential parameters in RL on the performance.