Optimal time sharing in underlay cognitive radio systems with RF energy harvesting

Due to the fundamental tradeoffs, achieving spectrum efficiency and energy efficiency are two contending design challenges for the future wireless networks. However, applying radio-frequency (RF) energy harvesting (EH) in a cognitive radio system could potentially circumvent this tradeoff, resulting in a secondary system with limitless power supply and meaningful achievable information rates. This paper proposes an online solution for the optimal time allocation (time sharing) between the EH phase and the information transmission (IT) phase in an underlay cognitive radio system, which harvests the RF energy originating from the primary system. The proposed online solution maximizes the average achievable rate of the cognitive radio system, subject to the $\varepsilon$-percentile protection criteria for the primary system. The optimal time sharing achieves significant gains compared to equal time allocation between the EH and IT phases.Comment: Proceedings of the 2015 IEEE International Conference on Communications (IEEE ICC 2015), 8-12 June 2015, London, U

Spectrum Sharing for Device-to-Device Communication in Cellular Networks

This paper addresses two fundamental and interrelated issues in device-to-device (D2D) enhanced cellular networks. The first issue is how D2D users should access spectrum, and we consider two choices: overlay (orthogonal spectrum between D2D and cellular UEs) and underlay (non-orthogonal). The second issue is how D2D users should choose between communicating directly or via the base station, a choice that depends on distance between the potential D2D transmitter and receiver. We propose a tractable hybrid network model where the positions of mobiles are modeled by random spatial Poisson point process, with which we present a general analytical approach that allows a unified performance evaluation for these questions. Then, we derive analytical rate expressions and apply them to optimize the two D2D spectrum sharing scenarios under a weighted proportional fair utility function. We find that as the proportion of potential D2D mobiles increases, the optimal spectrum partition in the overlay is almost invariant (when D2D mode selection threshold is large) while the optimal spectrum access factor in the underlay decreases. Further, from a coverage perspective, we reveal a tradeoff between the spectrum access factor and the D2D mode selection threshold in the underlay: as more D2D links are allowed (due to a more relaxed mode selection threshold), the network should actually make less spectrum available to them to limit their interference.Comment: 14 pages; 11 figures; submitted to IEEE Transactions on Wireless Communication

Cognitive Wireless Power Transfer in the Presence of Reactive Primary Communication User

This paper studies a cognitive or secondary multi-antenna wireless power transfer (WPT) system over a multi-carrier channel, which shares the same spectrum with a primary wireless information transfer (WIT) system that employs adaptive water-filling power allocation. By controlling the transmit energy beamforming over sub-carriers (SCs), the secondary energy transmitter (S-ET) can directly charge the secondary energy receiver (S-ER), even purposely interfere with the primary WIT system, such that the primary information transmitter (P-IT) can reactively adjust its power allocation (based on water-filling) to facilitate the S-ER's energy harvesting. We investigate how the secondary WPT system can exploit the primary WIT system's reactive power allocation, for improving the wireless energy harvesting performance. In particular, our objective is to maximize the total energy received at the S-ER from both the S-ET and the P-IT, by optimizing the S-ET's energy beamforming over SCs, subject to its maximum transmit power constraint, and the maximum interference power constraint imposed at the primary information receiver (P-IR) to protect the primary WIT. Although the formulated problem is non-convex and difficult to be optimally solved in general, we propose an efficient algorithm to obtain a high-quality solution by employing the Lagrange dual method together with a one-dimensional search. We also present two benchmark energy beamforming designs based on the zero-forcing (ZF) and maximum-ratio-transmission (MRT) principles, respectively, as well as the conventional design without considering the primary WIT system's reaction. Numerical results show that our proposed design leads to significantly improved energy harvesting performance at the S-ER, as compared to these benchmark schemes

RF-Powered Cognitive Radio Networks: Technical Challenges and Limitations

The increasing demand for spectral and energy efficient communication networks has spurred a great interest in energy harvesting (EH) cognitive radio networks (CRNs). Such a revolutionary technology represents a paradigm shift in the development of wireless networks, as it can simultaneously enable the efficient use of the available spectrum and the exploitation of radio frequency (RF) energy in order to reduce the reliance on traditional energy sources. This is mainly triggered by the recent advancements in microelectronics that puts forward RF energy harvesting as a plausible technique in the near future. On the other hand, it is suggested that the operation of a network relying on harvested energy needs to be redesigned to allow the network to reliably function in the long term. To this end, the aim of this survey paper is to provide a comprehensive overview of the recent development and the challenges regarding the operation of CRNs powered by RF energy. In addition, the potential open issues that might be considered for the future research are also discussed in this paper.Comment: 8 pages, 2 figures, 1 table, Accepted in IEEE Communications Magazin

Wireless Information and Power Transfer for IoT Applications in Overlay Cognitive Radio Networks

This paper proposes and investigates an overlay spectrum sharing system in conjunction with the simultaneous wireless information and power transfer (SWIPT) to enable communications for the Internet of Things (IoT) applications. Considered is a cooperative cognitive radio network, where two IoT devices (IoDs) exchange their information and also provide relay assistance to a pair of primary users (PUs). Different from most existing works, in this paper, both IoDs can harvest energy from the radio-frequency (RF) signals received from the PUs. By utilizing the harvested energy, they provide relay cooperation to PUs and realize their own communications. For harvesting energy, a time-switching (TS) based approach is adopted at both IoDs. With the proposed scheme, one round of bidirectional information exchange for both primary and IoT systems is performed in four phases, i.e., one energy harvesting (EH) phase and three information processing (IP) phases. Both IoDs rely on the decode-and-forward operation to facilitate relaying, whereas the PUs employ selection combining (SC) technique. For investigating the performance of the considered network, this paper first provides exact expressions of user outage probability (OP) for the primary and IoT systems under Nakagami-m fading. Then, by utilizing the expressions of user OP, the system throughput and energy efficiency are quantified together with the average end-to-end transmission time. Numerical and simulation results are provided to give useful insights into the system behavior and to highlight the impact of various system/channel parameters.Comment: 12 figures, 14 page

Cooperative Beamforming for Cognitive Radio-Based Broadcasting Systems with Asynchronous Interferences

In order to address the asynchronous interference issue for a generalized scenario with multiple primary and multiple secondary receivers, in this paper, we propose an innovative cooperative beamforming technique. In particular, the cooperative beamforming design is formulated as an optimization problem that maximizes the weighted sum achievable transmission rate of secondary destinations while it maintains the asynchronous interferences at the primary receivers below their target thresholds. In light of the intractability of the problem, we propose a two-phase suboptimal cooperative beamforming technique. First, it finds the beamforming directions corresponding to different secondary destinations. Second, it allocates the power among different beamforming directions. Due to the multiple interference constraints corresponding to multiple primary receivers, the power allocation scheme in the second phase is still complex. Therefore, we also propose a low complex power allocation algorithm. The proposed beamforming technique is extended for the cases, when cooperating CR nodes (CCRNs) have statistical or erroneous channel knowledge of the primary receivers. We also investigate the performance of joint CCRN selection and beamforming technique. The presented numerical results show that the proposed beamforming technique can significantly reduce the asynchronous interference signals at the primary receivers and increase the sum transmission rate of secondary destinations compared to the well known zero-forcing beamforming (ZFBF) technique.Comment: Submitted to the IEEE Transactions on Wireless Communication

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

Effective Capacity Analysis for Cognitive Networks under QoS Satisfaction

Spectrum sensing and dynamic spectrum access (DSA) techniques in cognitive radio networks (CRN) have been extensively investigated since last decade. Recently, satisfaction of quality-of-service (QoS) demands for secondary users (SU) has attracted great attention. The SU can not only discover the transmission opportunities, but also cognitively adapts the dynamic spectrum access strategies to its own QoS requirement and the environment variations. In this paper, we study how the delay QoS requirement affects the strategy on network performance. We first treat the delay-QoS in interference constrained cognitive radio network by applying the effective capacity concept, focusing on the two dominant DSA schemes: underlay and overlay. We obtain the effective capacity of the secondary network and determine the power allocation policies that maximize the throughput of the cognitive user. The underlay and overlay approaches may have their respective advantages under diverse propagation environment and system parameters. If the cognitive network can dynamically choose the DSA strategy under different environment, its performance could be further improved. We propose a selection criterion to determine whether to use underlay or overlay scheme under the given QoS constraint and the PUs’ spectrum-occupancy probability. Thus, the throughput of the CRN could be increased. Performance analysis and numerical evaluations are provided to demonstrate the effective capacity of CRN based on the underlay and the overlay schemes, taking into consideration the impact of delay QoS requirement and other related parameters

Channel assembling and resource allocation in multichannel spectrum sharing wireless networks

Submitted in fulfilment of the academic requirements for the degree of Doctor of Philosophy (Ph.D.) in Engineering, in the School of Electrical and Information Engineering, Faculty of Engineering and the Built Environment, at the University of the Witwatersrand, Johannesburg, South Africa, 2017The continuous evolution of wireless communications technologies has increasingly imposed a burden on the use of radio spectrum. Due to the proliferation of new wireless networks applications and services, the radio spectrum is getting saturated and becoming a limited resource. To a large extent, spectrum scarcity may be a result of deficient spectrum allocation and management policies, rather than of the physical shortage of radio frequencies. The conventional static spectrum allocation has been found to be ineffective, leading to overcrowding and inefficient use. Cognitive radio (CR) has therefore emerged as an enabling technology that facilitates dynamic spectrum access (DSA), with a great potential to address the issue of spectrum scarcity and inefficient use. However, provisioning of reliable and robust communication with seamless operation in cognitive radio networks (CRNs) is a challenging task. The underlying challenges include development of non-intrusive dynamic resource allocation (DRA) and optimization techniques. The main focus of this thesis is development of adaptive channel assembling (ChA) and DRA schemes, with the aim to maximize performance of secondary user (SU) nodes in CRNs, without degrading performance of primary user (PU) nodes in a primary network (PN). The key objectives are therefore four-fold. Firstly, to optimize ChA and DRA schemes in overlay CRNs. Secondly, to develop analytical models for quantifying performance of ChA schemes over fading channels in overlay CRNs. Thirdly, to extend the overlay ChA schemes into hybrid overlay and underlay architectures, subject to power control and interference mitigation; and finally, to extend the adaptive ChA and DRA schemes for multiuser multichannel access CRNs. Performance analysis and evaluation of the developed ChA and DRA is presented, mainly through extensive simulations and analytical models. Further, the cross validation has been performed between simulations and analytical results to confirm the accuracy and preciseness of the novel analytical models developed in this thesis. In general, the presented results demonstrate improved performance of SU nodes in terms of capacity, collision probability, outage probability and forced termination probability when employing the adaptive ChA and DRA in CRNs.CK201