Energy-Spectral Efficiency Trade-Off in Virtual MIMO Cellular Systems

Virtual multiple-input multiple-output (V-MIMO) technology promises significant performance enhancements to cellular systems in terms of spectral efficiency (SE) and energy efficiency (EE). How these two conflicting metrics scale up in large cellular V-MIMO networks is unclear. This paper studies the EE-SE trade-off of the uplink of a multi-user cellular V-MIMO system with decode-and-forward type protocols. We first express the trade-off in an implicit function and further derive closed-form formulas of the trade-off in low and high SE regimes. Unlike conventional MIMO systems, the EE-SE trade-off of the V-MIMO system is shown to be susceptible to many factors including protocol design (e.g., resource allocation) and scenario characteristics (e.g., user density). Focusing on the medium and high SE regimes, we propose a heuristic resource allocation algorithm to optimize the EE-SE trade-off. The fundamental performance limits of the optimized V-MIMO system are subsequently investigated and compared with conventional MIMO systems in different scenarios. Numerical results reveal a surprisingly chaotic behavior of V-MIMO systems when the user density scales up. Our analysis indicates that low frequency reuse factor, adaptive resource allocation, and user density control are critical to harness the full benefits of cellular V-MIMO systems.</p

Performance Evaluation of Connectivity and Capacity of Dynamic Spectrum Access Networks

Recent measurements on radio spectrum usage have revealed the abundance of under- utilized bands of spectrum that belong to licensed users. This necessitated the paradigm shift from static to dynamic spectrum access (DSA) where secondary networks utilize unused spectrum holes in the licensed bands without causing interference to the licensed user. However, wide scale deployment of these networks have been hindered due to lack of knowledge of expected performance in realistic environments and lack of cost-effective solutions for implementing spectrum database systems. In this dissertation, we address some of the fundamental challenges on how to improve the performance of DSA networks in terms of connectivity and capacity. Apart from showing performance gains via simulation experiments, we designed, implemented, and deployed testbeds that achieve economics of scale. We start by introducing network connectivity models and show that the well-established disk model does not hold true for interference-limited networks. Thus, we characterize connectivity based on signal to interference and noise ratio (SINR) and show that not all the deployed secondary nodes necessarily contribute towards the network\u27s connectivity. We identify such nodes and show that even-though a node might be communication-visible it can still be connectivity-invisible. The invisibility of such nodes is modeled using the concept of Poisson thinning. The connectivity-visible nodes are combined with the coverage shrinkage to develop the concept of effective density which is used to characterize the con- nectivity. Further, we propose three techniques for connectivity maximization. We also show how traditional flooding techniques are not applicable under the SINR model and analyze the underlying causes for that. Moreover, we propose a modified version of probabilistic flooding that uses lower message overhead while accounting for the node outreach and in- terference. Next, we analyze the connectivity of multi-channel distributed networks and show how the invisibility that arises among the secondary nodes results in thinning which we characterize as channel abundance. We also capture the thinning that occurs due to the nodes\u27 interference. We study the effects of interference and channel abundance using Poisson thinning on the formation of a communication link between two nodes and also on the overall connectivity of the secondary network. As for the capacity, we derive the bounds on the maximum achievable capacity of a randomly deployed secondary network with finite number of nodes in the presence of primary users since finding the exact capacity involves solving an optimization problem that shows in-scalability both in time and search space dimensionality. We speed up the optimization by reducing the optimizer\u27s search space. Next, we characterize the QoS that secondary users can expect. We do so by using vector quantization to partition the QoS space into finite number of regions each of which is represented by one QoS index. We argue that any operating condition of the system can be mapped to one of the pre-computed QoS indices using a simple look-up in Olog (N) time thus avoiding any cumbersome computation for QoS evaluation. We implement the QoS space on an 8-bit microcontroller and show how the mathematically intensive operations can be computed in a shorter time. To demonstrate that there could be low cost solutions that scale, we present and implement an architecture that enables dynamic spectrum access for any type of network ranging from IoT to cellular. The three main components of this architecture are the RSSI sensing network, the DSA server, and the service engine. We use the concept of modular design in these components which allows transparency between them, scalability, and ease of maintenance and upgrade in a plug-n-play manner, without requiring any changes to the other components. Moreover, we provide a blueprint on how to use off-the-shelf commercially available software configurable RF chips to build low cost spectrum sensors. Using testbed experiments, we demonstrate the efficiency of the proposed architecture by comparing its performance to that of a legacy system. We show the benefits in terms of resilience to jamming, channel relinquishment on primary arrival, and best channel determination and allocation. We also show the performance gains in terms of frame error rater and spectral efficiency

Capacity of hybrid cognitive radio networks with distributed VAAs

Due to copyright restrictions, the access to the full text of this article is only available via subscription.A cooperative hybrid cognitive radio (CR) network is proposed to simultaneously operate on a dedicated licensed band and a secondary band. The licensed band is used for communications between a base station (BS) and mobile CR users, whereas the secondary band is used to facilitate the licensed band communication by coordinating multiple CR users to form distributed virtual antenna arrays (VAAs). The capacity of the proposed CR network is studied at both the link and system levels. At the link level (single VAA case), we present an amplify-and-forward-based cooperative signaling scheme that employs power control to prevent harmful noise propagation. The resulting virtual multiple-input-multiple-output (MIMO) link capacity is derived and compared with the real MIMO system. At the system level (multiple VAAs case), the system capacity is derived as a function of multiple parameters, including the primary user density, CR user density, primary exclusion region radius, and VAA radius. Under an average interference power constraint, the maximum system capacity is further calculated by solving an optimization problem with adjustable system parameters. Numerical studies reveal that the proposed cooperative hybrid CR network has a fundamental advantage over a pure CR network by being insensitive to the characteristics of the coexisting primary network. This merit, however, relies on a high CR user density and a wide bandwidth of the secondary band.Scottish Funding Council for the Joint Research Institute in Signal and Image Processing with the University of Edinburgh ; RCUK for the U.K.–China Science Bridges ; Natural Sciences and Engineering Research Council of Canada ; National Natural Science Foundation of China (NSFC) ; National 863 High Technology Program of China ; Ministry of Science and Technology (MOST) of China International Science and Technology Collaboration Program ; National Basic Research Program of China “973” ; Guangxi Science Foundation ; NSFC ; Foundation of Guangxi Key Laboratory of Information and Communication ; Key Laboratory of Cognitive Radio and Information Processing (Guilin University of Electronic Technology) Ministry of Educatio

Capacity of hybrid cognitive radio networks with distributed VAAs

Due to copyright restrictions, the access to the full text of this article is only available via subscription.A cooperative hybrid cognitive radio (CR) network is proposed to simultaneously operate on a dedicated licensed band and a secondary band. The licensed band is used for communications between a base station (BS) and mobile CR users, whereas the secondary band is used to facilitate the licensed band communication by coordinating multiple CR users to form distributed virtual antenna arrays (VAAs). The capacity of the proposed CR network is studied at both the link and system levels. At the link level (single VAA case), we present an amplify-and-forward-based cooperative signaling scheme that employs power control to prevent harmful noise propagation. The resulting virtual multiple-input-multiple-output (MIMO) link capacity is derived and compared with the real MIMO system. At the system level (multiple VAAs case), the system capacity is derived as a function of multiple parameters, including the primary user density, CR user density, primary exclusion region radius, and VAA radius. Under an average interference power constraint, the maximum system capacity is further calculated by solving an optimization problem with adjustable system parameters. Numerical studies reveal that the proposed cooperative hybrid CR network has a fundamental advantage over a pure CR network by being insensitive to the characteristics of the coexisting primary network. This merit, however, relies on a high CR user density and a wide bandwidth of the secondary band.Scottish Funding Council for the Joint Research Institute in Signal and Image Processing with the University of Edinburgh ; RCUK for the U.K.–China Science Bridges ; Natural Sciences and Engineering Research Council of Canada ; National Natural Science Foundation of China (NSFC) ; National 863 High Technology Program of China ; Ministry of Science and Technology (MOST) of China International Science and Technology Collaboration Program ; National Basic Research Program of China “973” ; Guangxi Science Foundation ; NSFC ; Foundation of Guangxi Key Laboratory of Information and Communication ; Key Laboratory of Cognitive Radio and Information Processing (Guilin University of Electronic Technology) Ministry of Educatio