Ghaith Hattab

Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks

Cognitive radio has been widely considered as one of the prominent solutions to tackle the spectrum scarcity. While the majority of existing research has focused on single-band cognitive radio, multiband cognitive radio represents great promises toward implementing efficient cognitive networks compared to single-based networks. Multiband cognitive radio networks (MB-CRNs) are expected to significantly enhance the networks throughput and provide better channel maintenance by reducing handoff frequency. Nevertheless, the wideband front-end and the multiband spectrum access impose a number of challenges yet to overcome. This paper provides an in-depth analysis on the recent advancements in multiband spectrum sensing techniques, their limitations, and possible future directions to improve them. We study cooperative communications for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also investigate several limits and tradeoffs of various design parameters for MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that differ from the conventional metrics used for single-band-based networks.

Reconfigurable Wireless Networks

Driven by the advent of sophisticated and ubiquitous applications, and the ever-growing need for information, wireless networks are without a doubt steadily evolving into profoundly more complex and dynamic systems. The user demands are progressively rampant, while application requirements continue to expand in both range and diversity. Future wireless networks, therefore, must be equipped with the ability to handle numerous, albeit challenging, requirements. Network reconfiguration, considered as a prominent network paradigm, is envisioned to play a key role in leveraging future network performance and considerably advancing current user experiences. This paper presents a comprehensive overview of reconfigurable wireless networks and an in-depth analysis of reconfiguration at all layers of the protocol stack. Such networks characteristically possess the ability to reconfigure and adapt their hardware and software components and architectures, thus enabling flexible delivery of broad services, as well as sustaining robust operation under highly dynamic conditions. The paper offers a unifying framework for research in reconfigurable wireless networks. This should provide the reader with a holistic view of concepts, methods, and strategies in reconfigurable wireless networks. Focus is given to reconfigurable systems in relatively new and emerging research areas such as cognitive radio networks, cross-layer reconfiguration, and software-defined networks. In addition, modern networks have to be intelligent and capable of self-organization. Thus, this paper discusses the concept of network intelligence as a means to enable reconfiguration in highly complex and dynamic networks. Key processes in network intelligence, such as reasoning, learning, and context awareness, are presented to illustrate how these methods can take reconfiguration to a new level. Finally, the paper is supported with several examples and case studies showing the tremendous impact of reconfiguration on wireless networks.

Cooperative Spectrum Sensing With Heterogeneous Devices: Hard Combining Versus Soft Combining

Practical cognitive radio networks (CRNs) would have users with different capabilities and access levels to prior information about the primary users (PUs). For instance, some users may have the privilege to access information about the PU network, which can be utilized to increase the detection performance, whereas others may need to use simple detectors due to energy consumptions constraints, etc. This level of heterogeneity must be accommodated to enable coexistence of different secondary users’(SUs) networks. This paper presents performance analysis and comparison of hard and soft combining cooperative spectrum sensing schemes in heterogeneous CRNs. A centralized approach is considered for cooperative spectrum sensing with SUs that may use either energy detector, cyclostationary detector, pilot-based detector, or orthogonal frequency division multiplexing-based detector. For hard combining, each cooperative SU senses the spectrum using one of the available detectors and reports one-bit local decision to the fusion center which then applies

Enhanced pilot-based spectrum sensing algorithm

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Joint Quantization and Confidence-Based Generalized Combining Scheme for Cooperative Spectrum Sensing

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Joint Resource Allocation and User Association in Multi-Antenna Heterogeneous Networks

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Towards instantaneous collision and interference detection using in-band full duplex

rule for the final decision. For soft combining, we derive an optimal soft combining scheme based on the Neyman–Pearson criterion. Then, we drive a suboptimal rule to reduce the complexity of the proposed scheme. For the sake of simplicity, we consider only energy and cyclostationary detectors for soft combining. In addition, we study the impact of the cyclic frequency offset in case of a cyclostationary detector. Finally, the paper is supported with extensive simulation results that demonstrate the performance of proposed schemes in terms of probability of detection and probability of false alarm.

Rate-Based Cell Range Expansion for Downlink Massive MIMO Heterogeneous Networks

In this paper, we develop an enhanced pilot-based spectrum sensing algorithm for cognitive radio. Unlike conventional pilot-based detectors which merely detect the presence of pilot signals, the proposed detector also utilizes the presence of the signal that carries the actual information. We analytically compare the performance of the proposed detector with the conventional one, and we show that the detection performance is significantly improved.

Energy-efficient massive cellular IoT shared spectrum access via mobile data aggregators

Cooperative spectrum sensing can effectively protect licensed users from harmful interference and satisfy the quality-of-service requirements of secondary users (SUs) in cognitive radio networks. In this paper, a novel joint quantization and confidence-based generalized (JQCG) combining scheme is proposed. A centralized approach is considered for cooperative spectrum sensing with SUs using the energy detector as a local spectrum sensing scheme. The confidence level is calculated using a fuzzy logic membership function for each cooperative SU. In the proposed JQCG combining scheme, each cooperative SU transmits multiple bits instead of transmitting one bit (hard combining) or the complete test statistic (soft combining) to report local sensing results to the fusion center (FC). We also derive optimal weights (in Neyman–Pearson sense) for each quantized level at the FC to maximize the probability of detection for a given false alarm probability. The proposed JQCG combining scheme is validated by extensive simulation results showing that it has a comparable performance to the soft combining scheme with less overhead. Extensive computer simulations show that the JQCG combining scheme significantly outperforms the hard combining and existing quantized schemes for cooperative spectrum sensing.

Green heterogeneous networks via an intelligent power control strategy and D2D communications

We study the joint optimization of resource allocation and user association in downlink multi-antenna heterogeneous networks. The resource allocation is done orthogonally in the spectrum while the user association is implemented using cell range extension. The objective is to maximize a user utility function that depends on the rate of the typical user. We resort to the Gil-Pelaez Inversion Theorem to approximate the coverage probability and present a concave formulation of the joint optimization problem. By interpreting the problem as a multi-criterion one, we propose a suboptimal user association policy. We show that the optimal resource allocation factor of each tier is equal to the optimal association probability, which can be efficiently computed using a standard convex optimization solver. Simulation results show significant (up to three times) rate gains when resource allocation is jointly optimized with user association in comparison with merely optimizing resource allocation with max-power user association.