Design and Analysis of Opportunistic MAC Protocols for Cognitive Radio Wireless Networks

As more and more wireless applications/services emerge in the market, the already heavily crowded radio spectrum becomes much scarcer. Meanwhile, however,as it is reported in the recent literature, there is a large amount of radio spectrum that is under-utilized. This motivates the concept of cognitive radio wireless networks that allow the unlicensed secondary-users (SUs) to dynamically use the vacant radio spectrum which is not being used by the licensed primary-users (PUs). In this dissertation, we investigate protocol design for both the synchronous and asynchronous cognitive radio networks with emphasis on the medium access control (MAC) layer. We propose various spectrum sharing schemes, opportunistic packet scheduling schemes, and spectrum sensing schemes in the MAC and physical (PHY) layers for different types of cognitive radio networks, allowing the SUs to opportunistically utilize the licensed spectrum while confining the level of interference to the range the PUs can tolerate. First, we propose the cross-layer based multi-channel MAC protocol, which integrates the cooperative spectrum sensing at PHY layer and the interweave-based spectrum access at MAC layer, for the synchronous cognitive radio networks. Second, we propose the channel-hopping based single-transceiver MAC protocol for the hardware-constrained synchronous cognitive radio networks, under which the SUs can identify and exploit the vacant channels by dynamically switching across the licensed channels with their distinct channel-hopping sequences. Third, we propose the opportunistic multi-channel MAC protocol with the two-threshold sequential spectrum sensing algorithm for asynchronous cognitive radio networks. Fourth, by combining the interweave and underlay spectrum sharing modes, we propose the adaptive spectrum sharing scheme for code division multiple access (CDMA) based cognitive MAC in the uplink communications over the asynchronous cognitive radio networks, where the PUs may have different types of channel usage patterns. Finally, we develop a packet scheduling scheme for the PU MAC protocol in the context of time division multiple access (TDMA)-based cognitive radio wireless networks, which is designed to operate friendly towards the SUs in terms of the vacant-channel probability. We also develop various analytical models, including the Markov chain models, M=GY =1 queuing models, cross-layer optimization models, etc., to rigorously analyze the performance of our proposed MAC protocols in terms of aggregate throughput, access delay, and packet drop rate for both the saturation network case and non-saturation network case. In addition, we conducted extensive simulations to validate our analytical models and evaluate our proposed MAC protocols/schemes. Both the numerical and simulation results show that our proposed MAC protocols/schemes can significantly improve the spectrum utilization efficiency of wireless networks

Cognitive Radio for Emergency Networks

In the scope of the Adaptive Ad-hoc Freeband (AAF) project, an emergency network built on top of Cognitive Radio is proposed to alleviate the spectrum shortage problem which is the major limitation for emergency networks. Cognitive Radio has been proposed as a promising technology to solve todayâ?~B??~D?s spectrum scarcity problem by allowing a secondary user in the non-used parts of the spectrum that aactully are assigned to primary services. Cognitive Radio has to work in different frequency bands and various wireless channels and supports multimedia services. A heterogenous reconfigurable System-on-Chip (SoC) architecture is proposed to enable the evolution from the traditional software defined radio to Cognitive Radio

Machine Learning-Enabled Resource Allocation for Underlay Cognitive Radio Networks

Due to the rapid growth of new wireless communication services and applications, much attention has been directed to frequency spectrum resources and the way they are regulated. Considering that the radio spectrum is a natural limited resource, supporting the ever increasing demands for higher capacity and higher data rates for diverse sets of users, services and applications is a challenging task which requires innovative technologies capable of providing new ways of efficiently exploiting the available radio spectrum. Consequently, dynamic spectrum access (DSA) has been proposed as a replacement for static spectrum allocation policies. The DSA is implemented in three modes including interweave, overlay and underlay mode [1]. The key enabling technology for DSA is cognitive radio (CR), which is among the core prominent technologies for the next generation of wireless communication systems. Unlike conventional radio which is restricted to only operate in designated spectrum bands, a CR has the capability to operate in different spectrum bands owing to its ability in sensing, understanding its wireless environment, learning from past experiences and proactively changing the transmission parameters as needed. These features for CR are provided by an intelligent software package called the cognitive engine (CE). In general, the CE manages radio resources to accomplish cognitive functionalities and allocates and adapts the radio resources to optimize the performance of the network. Cognitive functionality of the CE can be achieved by leveraging machine learning techniques. Therefore, this thesis explores the application of two machine learning techniques in enabling the cognition capability of CE. The two considered machine learning techniques are neural network-based supervised learning and reinforcement learning. Specifically, this thesis develops resource allocation algorithms that leverage the use of machine learning techniques to find the solution to the resource allocation problem for heterogeneous underlay cognitive radio networks (CRNs). The proposed algorithms are evaluated under extensive simulation runs. The first resource allocation algorithm uses a neural network-based learning paradigm to present a fully autonomous and distributed underlay DSA scheme where each CR operates based on predicting its transmission effect on a primary network (PN). The scheme is based on a CE with an artificial neural network that predicts the adaptive modulation and coding configuration for the primary link nearest to a transmitting CR, without exchanging information between primary and secondary networks. By managing the effect of the secondary network (SN) on the primary network, the presented technique maintains the relative average throughput change in the primary network within a prescribed maximum value, while also finding transmit settings for the CRs that result in throughput as large as allowed by the primary network interference limit. The second resource allocation algorithm uses reinforcement learning and aims at distributively maximizing the average quality of experience (QoE) across transmission of CRs with different types of traffic while satisfying a primary network interference constraint. To best satisfy the QoE requirements of the delay-sensitive type of traffics, a cross-layer resource allocation algorithm is derived and its performance is compared against a physical-layer algorithm in terms of meeting end-to-end traffic delay constraints. Moreover, to accelerate the learning performance of the presented algorithms, the idea of transfer learning is integrated. The philosophy behind transfer learning is to allow well-established and expert cognitive agents (i.e. base stations or mobile stations in the context of wireless communications) to teach newly activated and naive agents. Exchange of learned information is used to improve the learning performance of a distributed CR network. This thesis further identifies the best practices to transfer knowledge between CRs so as to reduce the communication overhead. The investigations in this thesis propose a novel technique which is able to accurately predict the modulation scheme and channel coding rate used in a primary link without the need to exchange information between the two networks (e.g. access to feedback channels), while succeeding in the main goal of determining the transmit power of the CRs such that the interference they create remains below the maximum threshold that the primary network can sustain with minimal effect on the average throughput. The investigations in this thesis also provide a physical-layer as well as a cross-layer machine learning-based algorithms to address the challenge of resource allocation in underlay cognitive radio networks, resulting in better learning performance and reduced communication overhead