Spectral and Energy Efficiency in Cognitive Radio Systems with Unslotted Primary Users and Sensing Uncertainty

This paper studies energy efficiency (EE) and average throughput maximization for cognitive radio systems in the presence of unslotted primary users. It is assumed that primary user activity follows an ON-OFF alternating renewal process. Secondary users first sense the channel possibly with errors in the form of miss detections and false alarms, and then start the data transmission only if no primary user activity is detected. The secondary user transmission is subject to constraints on collision duration ratio, which is defined as the ratio of average collision duration to transmission duration. In this setting, the optimal power control policy which maximizes the EE of the secondary users or maximizes the average throughput while satisfying a minimum required EE under average/peak transmit power and average interference power constraints are derived. Subsequently, low-complexity algorithms for jointly determining the optimal power level and frame duration are proposed. The impact of probabilities of detection and false alarm, transmit and interference power constraints on the EE, average throughput of the secondary users, optimal transmission power, and the collisions with primary user transmissions are evaluated. In addition, some important properties of the collision duration ratio are investigated. The tradeoff between the EE and average throughput under imperfect sensing decisions and different primary user traffic are further analyzed.Comment: This paper is accepted for publication in IEEE Transactions on Communication

A fuzzy-based optimization technique for the energy and spectrum efficiencies trade-off in cognitive radio-enabled 5G network

Abstract: Please refer to full text to view abstract

Power-spectrum trading for full-duplex D2D communications in cellular networks

Device-to-device (D2D) communications allows two adjacent mobile terminals transmit signal directly without going through base stations, which has been considered as one of the key technologies for future mobile networks. As full-duplex (FD) communications can improve the performance (i.e., throughput, energy efficiency (EE)) of communications systems, it is commonly used in practical D2D communications scenarios. However, FD-enabled D2D communications also results in self-interference. To fully realize the potential benefits of FD-enabled D2D communications, an effective resource allocation mechanism is critical to avoid not only the self-interference of FD-enabled D2D communications but also the interference between D2D users (DUs) and cellular users (CUs). In this paper, we investigate the resource allocation issue for FD-enabled DUs and traditional CUs. Considering the asymmetry of energy and spectrum resources of DUs and CUs, we propose a power-spectrum trading mechanism to achieve mutual benefits for both types of users. A concave-convex procedure algorithm is employed to solve the optimization problem of power allocation, and then a maximum weighted bipartite matching based method is proposed to select proper D2D pairs to maximize the overall system throughput. Numerical results show that the proposed scheme can remarkably improve the overall throughput and EE of FD-enabled D2D communications system

Single carrier frequency domain equalization and energy efficiency optimization for MIMO cognitive radio.

This dissertation studies two separate topics in wireless communication systems. One topic focuses on the Single Carrier Frequency Domain Equalization (SC-FDE), which is a promising technique to mitigate the multipath effect in the broadband wireless communication. Another topic targets on the energy efficiency optimization in a multiple input multiple output (MIMO) cognitive radio network. For SC-FDE, the conventional linear receivers suffer from the noise amplification in deep fading channel. To overcome this, a fractional spaced frequency domain (FSFD) receiver based on frequency domain oversampling (FDO) is proposed for SC-FDE to improve the performance of the linear receiver under deep fading channels. By properly designing the guard interval, a larger sized Discrete Fourier Transform (DFT) is equipped to oversample the received signal in frequency domain. Thus, the effect of frequency-selective fading can still be eliminated by a one-tap frequency domain filter. Two types of FSFD receivers are proposed based on the least square (LS) and minimum mean square error (MMSE) criterion. Both the semi-analytical analysis and simulation results are given to evaluate the performance of the proposed receivers. Another challenge in SC-FDE is the in-phase/quadrature phase (IQ) imbalance caused by unideal radio frequency (RF) front-end at the transmitter or the receiver. Most existing works in single carrier transmission employ linear compensation methods, such as LS and MMSE, to combat the interference caused by IQ imbalance. Actually, for single carrier transmissions, it is possible for the receivers to adopt advanced nonlinear compensation methods to improve the system performance under IQ imbalance. For such purpose, an iterative decision feedback receiver is proposed to compensate the IQ imbalance caused by unideal RF front-end in SC-FDE. Numerical results show that the proposed iterative IQ imbalance compensation can significantly improve the performance of SC-FDE system under IQ imbalance compared with the conventional linear method. For the energy efficiency optimization in the MIMO cognitive radio network, multiple secondary users (SUs) coexisting with a primary user (PU) adjust their antenna radiation patterns and power allocations to achieve energy-efficient transmission. The optimization problems are formulated to maximize the energy efficiency of a cognitive radio network in both distributed and centralized point of views. Also, constraints on the transmission power and the interference to PU are introduced to protect the PU’s transmission. In order to solve the non-convex optimization problems, convex relaxations are used to transform them into equivalent problems with better tractability. Then three optimization algorithms are proposed to find the energy-efficient transmission strategies. Simulation results show that the proposed energy-efficiency optimization algorithms outperform the existing algorithms

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

Cognitive Radio Systems: Performance Analysis and Optimal Resource Allocation

Rapid growth in the use of wireless services coupled with inefficient utilization of scarce spectrum resources has led to the analysis and development of cognitive radio systems. Cognitive radio systems provide dynamic and more efficient utilization of the available spectrum by allowing unlicensed users (i.e., cognitive or secondary users) to access the frequency bands allocated to the licensed users (i.e., primary users) without causing harmful interference to the primary user transmissions. The central goal of this thesis is to conduct a performance analysis and obtain throughput- and energy-efficient optimal resource allocation strategies for cognitive radio systems. Cognitive radio systems, which employ spectrum sensing mechanisms to learn the channel occupancy by primary users, generally operate under sensing uncertainty arising due to false alarms and miss-detections. This thesis analyzes the performance of cognitive radio systems in a practical setting with imperfect spectrum sensing. In the first part of the thesis, optimal power adaptation schemes that maximize the achievable rates of cognitive users with arbitrary input distributions in underlay cognitive radio systems subject to transmit and interference power constraints are studied. Simpler approximations of optimal power control policies in the low-power regime are determined. Low-complexity optimal power control algorithms are proposed. Next, energy efficiency is considered as the performance metric and power allocation strategies that maximize the energy efficiency of cognitive users in the presence of time-slotted primary users are identified. The impact of different levels of channel knowledge regarding the transmission link between the secondary transmitter and secondary receiver, and the interference link between the secondary transmitter and primary receiver on the optimal power allocation is addressed. In practice, the primary user may change its status during the transmission phase of the secondary users. In such cases, the assumption of time-slotted primary user transmission no longer holds. With this motivation, the spectral and energy efficiency in cognitive radio systems with unslotted primary users are analyzed and the optimal frame duration and energy-efficient optimal power control schemes subject to a collision constraint are jointly determined. The second line of research in this thesis focuses on symbol error rate performance of cognitive radio transmissions in the presence of imperfect sensing decisions. General formulations for the optimal decision rule and error probabilities for arbitrary modulation schemes are provided. The optimal decision rule for rectangular quadrature amplitude modulation (QAM) is characterized, and closed-form expressions for the average symbol error probability attained with the optimal detector under both transmit power and interference constraints are derived. Furthermore, throughput of cognitive radio systems for both fixed-rate and variable-rate transmissions in the finite-blocklength regime is studied. The maximum constant arrival rates that the cognitive radio channel can support with finite blocklength codes while satisfying statistical quality of service (QoS) constraints imposed as limitations on the buffer violation probability are characterized. In the final part of the thesis, performance analysis in the presence of QoS requirements is extended to general wireless systems, and energy efficiency and throughput optimization with arbitrary input signaling are studied when statistical QoS constraints are imposed as limitations on the buffer violation probability. Effective capacity is chosen as the performance metric to characterize the maximum throughput subject to such buffer constraints by capturing the asymptotic decay-rate of buffer occupancy. Initially, constant-rate source is considered and subsequently random arrivals are taken into account

Energy Efficiency of Ultra-Dense Small Cell Radio Access Networks for 5G and Beyond

Small cell base station (BS) densification in the radio access network (RAN) is an effective solution to improve the RAN capacity. However, small cell BS densification by adding more non-zero energy-consuming BSs increases energy consumption, compromising energy efficiency, which can be mitigated by adopting sleep mode. A comprehensive evaluation framework is applied in this research to analyse the capacity, energy consumption, and energy efficiency performance of the ultra-dense small cell RANs as a complete energy efficiency assessment, which is lacking in the literature. The impact of advanced techniques millimetre wave (mmWave), antenna array beamforming, and integrated access and backhaul (IAB) on RAN energy efficiency are also investigated. MATLAB- based simulation results show that the ultra-dense small cell RANs, where the number of BSs greatly exceeds the number of active user equipment (UEs), can only be energy efficient if all the empty cells without UE association are turned off completely. Energy efficiency enhancement comes from capacity improvement and energy consumption constraint. Specifically, the ultra-dense small cell RANs can achieve maximum performance improvement of 7.56-fold and 2.35-fold regarding capacity, 3780.11-fold and 32.38-fold regarding energy consumption using the current power model, and 28591.53-fold and 75.97-fold regarding energy efficiency in homogeneous and heterogeneous infrastructures, respectively, comparing the cases with and without the sleep mode. In addition, mmWave and IAB trade energy consumption and energy efficiency for capacity improvement and backhaul cost reduction. With mmWave and IAB, dense small cell RAN can achieve a maximum of 2.55-fold and 1.70-fold for capacity improvement, 2.46-fold and 2.89-fold for energy consumption reduction using the current power model, and 6.27-fold and 8.34-fold energy efficiency enhancement for UE densities of 900 and 300 UEs/km2, respectively, comparing the cases with and without the sleep mode