Medium access control protocol design for wireless communications and networks review

<p><span>Medium access control (MAC) protocol design plays a crucial role to increase the performance of wireless communications and networks. The channel access mechanism is provided by MAC layer to share the medium by multiple stations. Different types of wireless networks have different design requirements such as throughput, delay, power consumption, fairness, reliability, and network density, therefore, MAC protocol for these networks must satisfy their requirements. In this work, we proposed two multiplexing methods for modern wireless networks: Massive multiple-input-multiple-output (MIMO) and power domain non-orthogonal multiple access (PD-NOMA). The first research method namely Massive MIMO uses a massive number of antenna elements to improve both spectral efficiency and energy efficiency. On the other hand, the second research method (PD-NOMA) allows multiple non-orthogonal signals to share the same orthogonal resources by allocating different power level for each station. PD-NOMA has a better spectral efficiency over the orthogonal multiple access methods. A review of previous works regarding the MAC design for different wireless networks is classified based on different categories. The main contribution of this research work is to show the importance of the MAC design with added optimal functionalities to improve the spectral and energy efficiencies of the wireless networks.</span></p>

Medium Access Control Optimization For Structural Health Monitoring Using Wireless Sensor Network

<p>Wireless sensor networks (WSNs) are designed for sensing phenomena and acquiring data. In structural health monitoring (SHM) of engineering structures, increasingly large number of sensor nodes are deployed to acquire data at the spatial density, needed for structural integrity assessment. During catastrophic events like earthquake there is a surge in simultaneous production and transmission of data to a central server at remote location. The increased contention for the wireless channel increases the probability of packet collisions resulting in packet drops, multiple transmission attempts and consequent delays. It is also not uncommon to find certain nodes (e.g. closer to sink) having better success rate in transmission of data and thereby leading to biased data delivery. Many solutions to the problem exist and clustering is the most commonly used method among then, wherein sensor nodes are grouped together. While the existing clustering algorithms do solve the network contention problems, the problem of cluster bias induced due to the proximity to sink node still remains to be addressed. Moreover all the existing solutions are very much node centric. This thesis presents a new perspective on cluster based WSNs designed to tackle Medium Access Control (MAC) layer congestion associated with burst packet generation in an unbiased manner, thereby making it more efficient for applications like SHM. In addition to solving the network bias problem, the proposed design also ensures faster transmission times, increased throughput and energy efficiency.</p>

Medium Access Control Optimization For Structural Health Monitoring Using Wireless Sensor Network

<p>Wireless sensor networks (WSNs) are designed for sensing phenomena and acquiring data. In structural health monitoring (SHM) of engineering structures, increasingly large number of sensor nodes are deployed to acquire data at the spatial density, needed for structural integrity assessment. During catastrophic events like earthquake there is a surge in simultaneous production and transmission of data to a central server at remote location. The increased contention for the wireless channel increases the probability of packet collisions resulting in packet drops, multiple transmission attempts and consequent delays. It is also not uncommon to find certain nodes (e.g. closer to sink) having better success rate in transmission of data and thereby leading to biased data delivery. Many solutions to the problem exist and clustering is the most commonly used method among then, wherein sensor nodes are grouped together. While the existing clustering algorithms do solve the network contention problems, the problem of cluster bias induced due to the proximity to sink node still remains to be addressed. Moreover all the existing solutions are very much node centric. This thesis presents a new perspective on cluster based WSNs designed to tackle Medium Access Control (MAC) layer congestion associated with burst packet generation in an unbiased manner, thereby making it more efficient for applications like SHM. In addition to solving the network bias problem, the proposed design also ensures faster transmission times, increased throughput and energy efficiency.</p>

Context Awareness and Intelligence in Cognitive Radio Networks: Design and Applications

<p>CR technology, which is the next-generation wireless communication system, improves the utilization of the overall radio spectrum through dynamic adaptation to local spectrum availability. In CR networks, unlicensed or Secondary Users (SUs) may operate in underutilized spectrum (called white spaces) owned by the licensed or Primary Users (PUs) conditional upon PUs encountering acceptably low interference levels. Ideally, the PUs are oblivious to the presence of the SUs. Context awareness enables an SU to sense and observe its operating environment, which is complex and dynamic in nature; while intelligence enables the SU to learn knowledge, which can be acquired through observing the consequences of its prior action, about its operating environment so that it carries out the appropriate action to achieve optimum network performance in an efficient manner without following a strict and static predefined set of policies. Traditionally, without the application of intelligence, each wireless host adheres to a strict and static predefined set of policies, which may not be optimum in many kinds of operating environment. With the application of intelligence, the knowledge changes in line with the dynamic operating environment. This thesis investigates the application of an artificial intelligence approach called reinforcement learning to achieve context awareness and intelligence in order to enable the SUs to sense and utilize the high quality white spaces. To date, the research focus of the CR research community has been primarily on the physical layer of the open system interconnection model. The research into the data link layer is still in its infancy, and our research work focusing on this layer has been pioneering in this field and has attacted considerable international interest. There are four major outcomes in this thesis. Firstly, various types of multi-channel medium access control protocols are reviewed, followed by discussion of their merits and demerits. The purpose is to show the additional functionalities and challenges that each multi-channel medium access control protocol has to offer and address in order to operate in CR networks. Secondly, a novel cross-layer based quality of service architecture called C2net for CR networks is proposed to provide service prioritization and tackle the issues associated with CR networks. Thirdly, reinforcement learning is applied to pursue context awareness and intelligence in both centralized and distributed CR networks. Analysis and simulation results show that reinforcement learning is a promising mechanism to achieve context awareness and intelligence. Fourthly, the versatile reinforcement learning approach is applied in various schemes for performance enhancement in CR networks.</p>

Context Awareness and Intelligence in Cognitive Radio Networks: Design and Applications

<p>CR technology, which is the next-generation wireless communication system, improves the utilization of the overall radio spectrum through dynamic adaptation to local spectrum availability. In CR networks, unlicensed or Secondary Users (SUs) may operate in underutilized spectrum (called white spaces) owned by the licensed or Primary Users (PUs) conditional upon PUs encountering acceptably low interference levels. Ideally, the PUs are oblivious to the presence of the SUs. Context awareness enables an SU to sense and observe its operating environment, which is complex and dynamic in nature; while intelligence enables the SU to learn knowledge, which can be acquired through observing the consequences of its prior action, about its operating environment so that it carries out the appropriate action to achieve optimum network performance in an efficient manner without following a strict and static predefined set of policies. Traditionally, without the application of intelligence, each wireless host adheres to a strict and static predefined set of policies, which may not be optimum in many kinds of operating environment. With the application of intelligence, the knowledge changes in line with the dynamic operating environment. This thesis investigates the application of an artificial intelligence approach called reinforcement learning to achieve context awareness and intelligence in order to enable the SUs to sense and utilize the high quality white spaces. To date, the research focus of the CR research community has been primarily on the physical layer of the open system interconnection model. The research into the data link layer is still in its infancy, and our research work focusing on this layer has been pioneering in this field and has attacted considerable international interest. There are four major outcomes in this thesis. Firstly, various types of multi-channel medium access control protocols are reviewed, followed by discussion of their merits and demerits. The purpose is to show the additional functionalities and challenges that each multi-channel medium access control protocol has to offer and address in order to operate in CR networks. Secondly, a novel cross-layer based quality of service architecture called C2net for CR networks is proposed to provide service prioritization and tackle the issues associated with CR networks. Thirdly, reinforcement learning is applied to pursue context awareness and intelligence in both centralized and distributed CR networks. Analysis and simulation results show that reinforcement learning is a promising mechanism to achieve context awareness and intelligence. Fourthly, the versatile reinforcement learning approach is applied in various schemes for performance enhancement in CR networks.</p>