Energy Efficiency Optimization for multiuser OFDM-based Cognitive Heterogeneous networks

In this paper, we consider a Cognitive Radio (CR) system where two type of users try to access to the primary spectrum : a primary user (PU) owning the spectrum license and a secondary user (SU) who does not own the spectrum license. However, the secondary communication is allowed to coexist with the primary communication as long as the interference caused by SU to PU is below a tolerable threshold. We study the optimization problem which maximizes the SU's achievable ergodic capacity under different types of power constraints and for different fading channel models. Our goal is to calculate the optimal power allocation strategies for these optimization problems. We show that modelling with Rayleigh fading for the channel between SU transmitter and PU receiver is an advantageous way to ameliorate the SU ergodic capacity. Furthermore, we consider four combinations of power constraints, since the interference power constraint and the transmit power constraint can be restricted by a peak or an average threshold. We also show that the SU ergodic capacity under average transmit power constraint and average interference power constraint outperforms the one with peak power constraints. In this case, we propose a novel decoupling method. Our method reduces the complexity of the initial problem and makes our initial problem easier to solve.

For a multiuser cognitive OFDM network, most existing adaptive resource allocation methods focus solely on fixed data-rate requirement (QoS guaranteed) or variable data-rate (no QoS guaranteed) conditions without any fairness consideration. In this paper, we investigate the resource allocation problem in a heterogeneous cognitive network with different QoS requirements. By decomposing the problem as a convex optimization, an optimal time-sharing resource allocation method is proposed to maximize the throughput of cognitive network under both subcarrier interference temperature limits and heterogeneous data-rate requirements. Finally, the performance of our proposed resource allocation algorithm is investigated by numerical results.

Most resource allocation algorithms are based on interference power constraint in cognitive radio networks. Instead of using conventional primary user interference constraint, we give a new criterion called allowable signal to interference plus noise ratio (SINR) loss constraint in cognitive transmission to protect primary users. Considering power allocation problem for cognitive users over flat fading channels, in order to maximize throughput of cognitive users subject to the allowable SINR loss constraint and maximum transmit power for each cognitive user, we propose a new power allocation algorithm. The comparison of computer simulation between our proposed algorithm and the algorithm based on interference power constraint is provided to show that it gets more throughput and provides stability to cognitive radio networks.

This paper considers a spectrum sharing wireless environment, where an OFDM-based cognitive radio system is allowed to access the spectrum originally licensed to an OFDMA primary system. A new criterion referred to as the rate loss constraint, in the form of an upper bound on the maximum rate loss of the primary user due to the secondary transmission, is proposed for primary transmission protection. In addition, assuming that some PUs are protected by the rate loss constraint, and some PUs are protected by the interference power constraint, the optimal power allocation strategy to maximize the rate of the cognitive radio system under such a hybrid protection to PUs together with a transmit power constraint is derived. Then, the relationship between the rate loss constraint and the interference power constraint is investigated, and it is shown by simulation that the cognitive radio system can achieve a significant rate gain under the proposed constraint compared with that under the conventional interference power constraint.

Green communications is an inevitable trend for future communication network design, especially for a cognitive radio network. Power allocation strategies are of crucial importance for green cognitive radio networks. However, energy-efficient power allocation strategies in green cognitive radio networks have not been fully studied. Energy efficiency maximization problems are analyzed in delay-insensitive cognitive radio, delay-sensitive cognitive radio, and simultaneously delay-insensitive and delay-sensitive cognitive radio, where a secondary user coexists with a primary user and the channels are fading. Using fractional programming and convex optimization techniques, energy-efficient optimal power allocation strategies are proposed subject to constraints on the average interference power, along with the peak/average transmit power. It is shown that the secondary user can achieve energy efficiency gains under the average transmit power constraint, in contrast to the peak transmit power constraint. Simulation results show that the fading of the channel between the primary user transmitter and the secondary user receiver and the fading of the channel between the secondary user transmitter and the primary user receiver are favorable to the secondary user with respect to the energy efficiency maximization of the secondary user, whereas the fading of the channel between the secondary user transmitter and the secondary user receiver is unfavorable to the secondary user.

In this paper, energy efficiency (EE) was investigated for a cognitive radio network (CRN) where massive multiple-input multiple-output (MIMO) was combined with femto cells. A heterogeneous network was considered to maximise EE, while massive MIMO was implemented to increase spatial reuse and focus energy into smaller spatial regions. Cooperation between femto cell based relay stations (RS) and a micro cell based secondary base station (SBS) allowed for secondary user (SU) quality-of-service (QoS) requirements to be met and also for control over primary user (PU) interference. Two key issues were addressed by the proposed model, namely improved CRN EE and reduced PU interference. The formulated EE optimisation problem was non-convex and thus converted into a semi-definite problem. Simulation results show that combining massive MIMO at the micro cell level with femto cells, lead to a CRN EE improvement. The addition of femto cells also lead to a reduction in PU interference.

SummaryCognitive heterogeneous network is a promising technology to meet the requirements of future wireless networks in cost and energy efficiency. By using vertical handover (VHO) between different network tiers, such as macro and femto, the traffic congestion can be potentially alleviated, and the spectral efficiency is significantly increased. However, performing VHO for a cognitive handover user (CHU) and simultaneously guaranteeing the quality of service of the primary user (PU) is the most challenging issue in such a network. This paper proposes an efficient VHO strategy in a cognitive heterogeneous network, which considers both adaptive average received signal and spectrum availability, namely, average received signal and spectrum availability. To analyse the performance of the proposed average received signal and spectrum availability algorithm, several metrics are investigated. First, the assignment probability of a CHU from a macro base station to a femto base station is evaluated and simplified in a closed‐form expression. Then, the outage probability of a CHU and collision probability of the PU are theoretically investigated for the proposed algorithm and are compared with 2 well‐known algorithms, the modified received signal strength–α and the received signal strength and wireless transmission loss. We also introduce a new performance metric, referred to as figure of merit, which jointly considers the assignment and outage probability of a CHU, collision probability of a PU, and the number of unnecessary handovers of a CHU. Figure of merit effectively shows the superiority of the proposed handover algorithm to conventional ones.

Energy efficient cognitive radio networks can simultaneously improve spectrum efficiency (SE) and energy efficiency (EE) of wireless communication networks. This paper studies energy efficient spectrum sensing interval, spectrum sensing energy and transmit power allocation scheme of the secondary user (SU) in CR networks. The EE maximization problem subject to the transmit power constraint and average primary user (PU) interference constraint is formulated as a non-convex optimization problem. Based on the quasiconcave property of the optimization problem, the original problem is transformed into an equivalent concave problem and a Dinkelbach's method based iterative algorithm is used to solve the problem. As a special case, the throughput maximization problem is also investigated. Through simulation results, the impact of transmit power constraint and PU interference constraint on the EE and throughput of the SU are analyzed.

A cognitive radio (CR) network refers to a secondary network operating in a frequency band originally licensed/allocated to a primary network consisting of one or multiple primary users (PUs). A fundamental challenge for realizing such a system is to ensure the quality of service (QoS) of the PUs as well as to maximize the throughput or ensure the QoS, such as signal-to-interference-plus-noise ratios (SINRs), of the secondary users (SUs). In this paper, we study single-input multiple output multiple access channels (SIMO-MAC) for the CR network. Subject to interference constraints for the PUs as well as peak power constraints for the SUs, two optimization problems involving a joint beamforming and power allocation for the CR network are considered: the sum-rate maximization problem and the SINR balancing problem. For the sum-rate maximization problem, zero-forcing based decision feedback equalizers are used to decouple the SIMO-MAC, and a capped multi-level (CML) water-filling algorithm is proposed to maximize the achievable sum-rate of the SUs for the single PU case. When multiple PUs exist, a recursive decoupled power allocation algorithm is proposed to derive the optimal power allocation solution. For the SINR balancing problem, it is shown that, using linear minimum mean-square-error receivers, each of the interference constraints and peak power constraints can be completely decoupled, and thus the multi-constraint optimization problem can be solved through multiple single-constraint sub-problems. Theoretical analysis for the proposed algorithms is presented, together with numerical simulations which compare the performances of different power allocation schemes.

We investigate energy efficient resource allocation in a downlink energy harvesting cognitive radio (CR) network, where multiple secondary users (SUs) with energy harvesting capability are assumed to share multiple channels with the primary user (PU) under the transmit power constraint and the interference power constraint. Specifically, one channel is assumed to be allocated to only one SU for information transmission and the rest SUs can do energy harvesting on the channel. The problem of joint channel and power allocation to maximize the energy efficiency of the CR network is investigated, and an efficient iterative algorithm based on Dinkelbach method and convex optimization is proposed. It is shown that the proposed algorithm significantly improves the energy efficiency of the CR network compared to reference algorithms.

Energy efficiency (EE) optimization is investigated for a multi-user cognitive radio network (CRN) over multiple-input-multiple-output (MIMO) interference channels (ICs). To reduce the system overhead due to information exchange among the secondary CR uses (SUs), the EE optimization problem is formulated as a non-cooperative game, where each SU transmitter competes against the other SU pairs by optimizing its transmit covariance matrix. Specifically, each multi-antenna SU maximizes locally its energy efficiency in terms of the number of bits transmitted per unit energy consumption, subject to the per-SU transmit power constraint and the primary user (PU) perceived total interference constraint. It is proved that the formulated non-cooperative game admits at least one Nash equilibrium (NE), and the sufficient condition for a unique NE is derived subsequently. Primal decomposition is employed in the local EE optimization problem to relax the coupling PU perceived interference constraint such that fully distributed operation is allowed. A distributed iterative EE optimization algorithm (DIEEOA) is then proposed to obtain the unique NE, which is shown to converge to the global optimum. Linear precoding techniques are employed to mitigate the impacts of multi-user interference and imperfect channel state information (CSI). Through numerical simulations, effectiveness of the proposed scheme is validated and the system setting parameters' impacts on the performance are studied.

A cognitive network is considered in which a primary user and a secondary user are transmitting to a common receiver. Successive interference cancellation is performed at the common receiver to guarantee that no interference is experienced by the primary user. At the beginning of each time slot, the secondary user senses the channel of the primary user to determine if the primary user is active or idle. The sensing scheme is not perfect and thus there are nonzero probabilities of miss detection and false alarm. The secondary user transmits a variable number of packets in each time slot and the number of transmitted packets is determined via a probabilistic cross-layer algorithm which minimizes the average packet delay of the secondary user under an average power constraint. A constraint on the maximum collision probability is imposed to guarantee an acceptable quality of service for the primary user. The problem is shown to be quasi-convex, and thus, can be solved efficiently. Our results indicate that even with sensing errors, the throughput of the network increases significantly with increasing the number of transmitted packets. However this comes at the expense of increasing the average delay of the secondary user.

With the rapid development of wireless services and applications, the currently radio spectrum is becoming more crowded. How to accommodate more wireless services and applications within the limited radio spectrum becomes a big challenge faced by modern society. Cognitive radio (CR) is proposed as a promising technology to tackle this challenge by introducing secondary users (SUs) to opportunistically or concurrently access the spectrum allocated to primary users (PUs). Currently, there are two prevalent CR models: the spectrum sharing model and the opportunistic spectrum access model. In the spectrum sharing model, the SUs are allowed to coexist with the PUs as long as the interferences from SUs do not degrade the quality of service (QoS) of PUs to an unacceptable level. In the opportunistic spectrum access model, SUs are allowed to access the spectrum only if the PUs are detected to be inactive. These two models known as underlay and overlay schemes, respectively. This thesis studies a number of topics in CR networks under the framework of these two schemes. First, studied cognitive radio transmissions under QoS delay constraints. Initially, we focused on the concept: effective capacity for cognitive radio channels in order to identify the performance in the presence of QoS constraints. Both underlay and overlay schemes are studied taking into consideration the activity of primary users, and assuming the general case of channel fading as Gamma distribution. For this setting, we further proposed a selection criterion by which the cognitive radio network can choose the adequate mode of operation. Then, we studied the cognitive radio transmissions focusing on Rayleigh fading channel and assumed that no prior channel knowledge is available at the transmitter and the receiver. We investigated the performance of pilot-assisted transmission strategies. In particular, we analyzed the channel estimation using minimum mean-square-error (MMSE) estimation, and analyzed efficient resource allocation strategies. In both cases, power allocations and effective capacity optimization were obtained. Effective capacity and interference constraint were analyzed in both single-band and multi-band spectrum sensing settings. Finally, we studied optimal access probabilities for cognitive radio network using Markov model to achieve maximum throughput for both CR schemes.

Cognitive radio and heterogeneous wireless networks are important candidate techniques for the fifth generation (5G) communication systems. On the other hand, propagation properties of radio channels in a physical layer are exploited to design efficient secure transmission schemes for 5G wireless networks. In this work, a security-aware joint power and subchannel allocation problem based on the inter-network cooperation is investigated at cognitive heterogeneous networks via imperfect spectral sensing. The security-aware joint power and subchannel allocation is subject to constraints in available subchannels, proportional fairness secrecy transmission rates among secondary mobile terminals (MTs), total interference power threshold, and total power for each secondary MT. In order to determine the impact of imperfect spectral sensing to resource allocation, we first represent the interference power at primary base station in terms of the misdetection probability and false alarm probability. Then, the security-aware power and subchannel allocation problem with inter-network cooperation is formulated as a biconvex optimization problem, and an optimal security-aware power and subchannel allocation algorithm is proposed utilizing the dual decomposition method. Compared to the heuristic power and subchannel allocation algorithms, numerical simulation results show that the proposed algorithms improve the total secrecy throughput and guarantee the proportional fairness among different secondary MTs.

Cognitive Radio Networks (CRNs) is a formidable evolution in wireless technology owing to its potentiality to resolve spectrum scarcity crisis. In CRNs, cognitive radio user’s sense idle spectrum as well as opportunistically entrée sensed spectrum devoid of approved users. The best spectrum is said to be selected opportunistically, shared with other users with no severity to licensed users. Many CRN devices that are fueled by battery possessing constrained energy capacity. Prevailing battery mechanisms yet couldn’t contribute CRN devices works for a long time. Therefore, conservation of energy is mandatory as far as routing in CRN is concerned. In this work a machine learning based method called, Regression Cooperative Spectrum Sensing and Gradient Flying Fox Route Optimization (RCSS-GFFRO) for energy and bandwidth aware data routing with spectrum utilization in CRNs is proposed. Protection of primary users (PUs) as well as optimization of secondary user (SU) transmission included in proposed method. In the proposed method a cooperative spectrum sensing using machine learning (ML) model called, Median Regression function is applied. The Median Regression Cooperative Spectrum Sensing in our work with the bandwidth and energy efficiency ensures acceptable probability of interference to primary users due to secondary user’s access by measuring the relationship between bandwidth and energy efficiency simultaneously. Next, optimal route identification is performed using Gradient Flying Fox Route Optimization algorithm. By applying gradient optimization function minimizes the interference with primary users and increases secondary user performance via sensing time and transmission time parameters with higher routing performance. Simulation estimated with parameters.