Resource Allocation for Green Cognitive Radios: Energy Efficiency Maximization

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.

Cloud Radio Access Network (C-RAN) is a key architecture for 5G cellular wireless network that aims at improving spectral and energy efficiency of the network by merging RAN and cloud computing together. In this paper, a novel resource allocation scheme that optimizes the network energy efficiency of a C-RAN is designed. First, an energy consumption model that characterizes the computation energy of the BaseBand Unit (BBU) is introduced based on empirical results collected from a programmable C-RAN testbed. Then, an optimization problem is formulated to maximize the energy efficiency of the network, subject to practical constraints including Quality of Service (QoS) requirement, radio remote head transmit power, and fronthaul capacity limits. The introduced Network Energy Efficiency Maximization (NEEM) problem jointly considers the tradeoff among the network accumulated data rate, BBU power consumption, fronthaul cost, and beamforming design. To deal with the non-convexity and mixed-integer nature of the problem, we utilize successive convex approximation methods to transform the original problem into the equivalent Weighted Sum-Rate (WSR) maximization problem. We then propose a provably-convergent iterative method to solve the resulting WSR problem. Extensive simulation results coupled with real-time experiments on a small-scale C-RAN testbed show the effectiveness of our proposed resource allocation scheme and its advantages over existing approaches.

Cloud Radio Access Network (C-RAN) is a key architecture for 5G cellular wireless network that aims at improving spectral and energy efficiency of the network by uniting traditional RAN with cloud computing. In this paper, a novel resource allocation scheme that optimizes the network energy efficiency of a C-RAN is designed. First, an energy consumption model that characterizes the computation energy of the BaseBand Unit (BBU) is introduced based on empirical results collected from a programmable C-RAN testbed. Then, an optimization problem is formulated to maximize the energy efficiency of the network, subject to practical constraints including Quality of Service (QoS) requirement, radio remote head transmit power, and fronthaul capacity limits. The formulated Network Energy Efficiency Maximization (NEEM) problem jointly considers the tradeoff among the network accumulated data rate, BBU power consumption, fronthaul cost, and beamforming design. To deal with the non-convexity and mixed-integer nature of the problem, we utilize successive convex approximation methods to transform the original problem into the equivalent Weighted Sum-Rate (WSR) maximization problem. We then propose a provably-convergent iterative method to solve the resulting WSR problem. Extensive simulation results coupled with real-time experiments on a small-scale C-RAN testbed show the effectiveness of our proposed resource allocation scheme and its advantages over existing approaches.

This paper aims to design beamforming vectors and cluster formation scheme by formulating an energy efficiency (EE) maximization problem in the user-centric networks, where several multi-antenna access points (APs) form a virtual cell to serve each single-antenna user equipment (UE). Considering both rate requirements and power budgets, we study the feasibility of the original EE maximization problem. To verify the feasibility, we design a max-min rate problem. Then, instead of solving the original problem directly, we deal with two subproblems: 1) relaxed EE maximization subproblem and 2) total power minimization subproblem. Through applying the weighted minimum mean square error (WMMSE) approach, we first transform the nonlinear data rate expression into a quadratic form and then solve each subproblem via using the Lagrangian dual method, which fortunately has closed-form solutions. Moreover, a novel threshold forming scheme is proposed for AP cluster formation and a simple but effective method called K-dimension grid search is devised to obtain the near-optimal threshold. The extensive simulation results indicate that our proposed algorithm converges fast and is robust to the simulation environment. In addition, the proposed algorithm outperforms the benchmark algorithms greatly in terms of EE and data rate.

In this paper, energy-efficient power allocation (PA) is investigated for a multiple-input multiple-output non-orthogonal multiple access system with multiple users in a cluster. To ensure the quality of service (QoS) for the users, a minimum rate requirement is pre-defined for each user. Because of the QoS requirement, it is first necessary to determine whether the considered energy-efficiency (EE) maximization problem is feasible or not, by comparing the total transmit power with the required power for satisfying the QoS of the users. If feasible, a closed-form solution is provided for the corresponding sum rate maximization problem, and on this basis, the EE maximization problem is solved by applying non-convex fractional programming. Otherwise, a low-complexity user admission scheme is proposed, which admits users one by one following the ascending order of the required power for satisfying the QoS. Numerical results are presented to validate the effectiveness of the proposed energy-efficient PA strategy and user admission scheme.

This paper investigates reconfigurable intelligent surface (RIS)-assisted simultaneous wireless information and power transfer (SWIPT) networks with rate splitting multiple access (RSMA). An energy efficiency (EE) maximization problem is formulated subject to the power budget at the transmitter and the quality of service (QoS) requirements of both information communication and energy harvesting, where the beamforming vectors, the power splitting (PS) ratios, the common message rates, and the discrete phase shifts are jointly optimized. To tackle the non-convex problem with both discrete and continuous variables, a deep reinforcement learning-based approach is proposed with the proximal policy optimization (PPO) framework. Different from traditional optimization approaches which optimizes the beamforming vectors and phase shifts separately and alternatively, our proposed PPO-based approach optimizes all the variables in unison. Besides, to perform beamforming design in action space, the beamforming vectors for the common stream and the private stream are respectively designed based on the maximum-ratio transmission and the zero forcing to enhance both energy and information transmission. To evaluate the performance of the PPO-based approach, a successive convex approximation (SCA) and Dinkelbach’s method based solution scheme (named SCA-D scheme) is also presented. Simulation results show that the system EE obtained by the proposed PPO-based approach is close to that obtained by the SCA-D scheme while outperforming various benchmarks. The RSMA contributes to the EE of the system greatly compared with traditional scheme. As for the case of time-varying channels, the proposed PPO-based approach is with much smaller running time by only sacrificing a slight EE performance compared with the SCA-D scheme.

This paper considers the problem of energy efficiency maximization in the uplink of a cluster of multiple-antenna coordinated access points. A framework for energy efficiency optimization is developed in which the signal-to-interference-plus-noise ratio takes a more general expression than existing alternatives so as to encompass most 5G candidate technologies. Two energy efficiency optimization problems are formulated, also considering quality-of-service (QoS) constraints: 1) network global energy efficiency maximization; 2) worst-case energy-efficient design. These fractional, non-convex problems are tackled by means of fractional programming coupled with sequential convex optimization, and two low-complexity resource allocation algorithms are designed, which are guaranteed to converge to local optima of the non-convex problems. Numerical results show that the proposed algorithm can efficiently balance between the goals of maximizing the energy efficiency and meeting the QoS constraints. Moreover, it is shown that a small sum-rate reduction allows large energy savings.

In this paper, we design a resource allocation (RA) scheme for the downlink of relay-assisted orthogonal frequency division multiple access cellular networks. Our design goal is to maximize the energy efficiency (EE) with minimal signaling overhead and low computational complexity. To alleviate the signaling overhead of channel information, the proposed scheme allocates the resources in a distributed manner, where each relay station (RS) or base station (BS) individually performs a joint subchannel and power allocation for its subordinate users. To increase the cell throughput, the proposed scheme allows the subchannel reuse among RSs and this enhances the EE performance. The proposed scheme is composed of the resource allocation at RS (RS-RA) and the resource allocation at BS (BS-RA). The RS-RA is formulated as an optimization problem that minimizes the consumed power of RS while reusing as many subchannels as possible among RSs. The optimization objective of BS-RA is to maximize the EE of the cell. Simulation results show that the proposed distributed RA scheme achieves remarkably excellent EE performance with small signaling overhead, comparable with the centralized optimal RA scheme aiming at the EE maximization based on full channel information.

Terahertz (THz) communication is expected to be a key technology of next-generation wireless communication. In this paper, we study the energy efficiency of an indoor THz communication system employing array-of-subarray structure. In the system, each radio frequency (RF) chain is connected with one antenna subarray and finite-bit digital-to-analog converters (DACs) at the access point (AP) side or analog-to-digital converters (ADCs) at user side. We assume minimum achievable rate constraint to satisfy the communication requirements and propose a joint power and subarray optimization policy to maximize the energy efficiency, which is the division of the sum achievable rate and total power consumption including the transmission power and circuit power given by the up-to-date studies. Specifically, the energy efficiency maximization problem is decomposed into subproblems of finding the optimal power allocation and optimal subarray number. An algorithm is proposed to find the optimal settings. Through numerical results, the proposed scheme outperforms other benchmark schemes in terms of energy efficiency. Also, it is shown that the optimal configuration of the number of subarrays is essential for energy efficiency maximization in the considered system.

Unmanned aerial vehicles (UAVs) are developing rapidly owing to flexible deployment and access services as air base stations. However, the energy efficiency of the UAVs cells using non-orthogonal multiple access (NOMA) with imperfect channel state information (CSI) hasnt been well studied yet. Therefore, we maximize energy efficiency in the downlink NOMA UAV network considering imperfect CSI between the UAV and users. Resource allocation schemes including user scheduling as well as power allocation are designed for system energy efficiency optimization. Because of the non-convexity of optimization function with an probability constraint for imperfect CSI, the original problem is converted into a nonprobability problem and then decoupled into two convex subproblems by successive convex approximation method. First, a user scheduling method is applied in the two-side matching of users and subchannels by the difference of convex programming. Then based on user scheduling, the energy efficiency in UAV cells is optimized through a suboptimal power allocation algorithm. The simulation results prove that our proposed algorithm is more effective compared with existing resource allocation schemes.

This article studies the energy efficiency (EE) of a three-node decode-and-forward (DF) relay channel, where the relay operates in full-duplex (FD) or half-duplex (HD) mode. In particular, for the FD mode, both the residual self-interference (RSI) and the extra circuit power consumption introduced by the self-interference cancellation (SIC) at the relay node are considered and modeled as linear functions over the relay transmission power. Under this setup, the EE maximization problems for the considered FD and HD modes subject to the total transmission power and spectral efficiency (SE) constraints are formulated as max–min problems, whose optimal power allocation is computed by fractional programming. Next, the EEs for the FD, HD, and direct transmission (DT) modes are compared under different channel conditions, and a hybrid mode selection scheme, i.e., selecting the one with the maximum EE among the above three modes, is proposed. Besides, we also prove that both the EE and the SE monotonically increase with the total transmission power under a given condition. Finally, numerical results show that the maximum EE of the FD relaying exceeds those of the HD and DT ones when the required SE is relatively high. Furthermore, the proposed hybrid mode selection scheme can provide up to 192.4% higher EE compared with the DT mode.

This paper proposes a dynamic resource allocation scheme to maximize the energy efficiency (EE) for Massive MIMO Systems. The imperfect channel estimation (CE) and feedback are explicitly considered in the EE maximization problem, which aim to optimize the power allocation, the antenna subset selection for transmission, and the pilot assignment. Assuming CE error to be bounded for the complex-constrained Cramer–Rao Bound (CRB), theoretical results show that the lower bound is directly proportional to its number of unconstrained parameters. Utilizing this perspective, a separated and bi-directional estimation is developed to achieve both low CRB and low complexity by exploiting channel and noise spatial separation. Exploiting global optimization procedure, the optimal resource allocation can be transformed into a standard convex optimization problem. This allows us to derive an efficient iterative algorithm for obtaining the optimal solution. Numerical results are provided to demonstrate that the outperformance of the proposed algorithms are superior to existing schemes.

With growing concern about environmental issues and an emerging green communications paradigm, cognitive radio (CR) networks (CRNs) have to be considered from the energy efficiency perspective. In this paper, we focus on scheduling in CRNs, in which a cognitive base station (CBS) makes frequency allocations to the CRs at the beginning of each frame. A cognitive scheduler must consider the diversity among CRs' queues and channel capacities in terms of number of bits and the channel switching cost from one frequency to another. Taking all these into account, we formulate the scheduling problem as an energy efficiency maximization problem, which is a nonlinear integer programming (NLP) problem and is thereby hard to solve. We seek alternate computationally easier solutions. To this aim, we propose a polynomial-time heuristic algorithm, i.e., the energy-efficient heuristic scheduler (EEHS), which allocates each idle frequency to the CR that attains the highest energy efficiency at this frequency. Next, we reformulate the original problem first as a throughput maximization problem subject to energy consumption restrictions and then as an energy consumption minimization problem subject to minimum throughput guarantees. These two schedulers also have the power to provide fairness in resource allocation. We analyze the energy efficiency and successful transmission probability of the proposed schedulers under both contiguous and fragmented spectrum scenarios. Performance studies show that, compared with a pure opportunistic scheduler with a throughput maximization objective, proposed schedulers can attain almost the same throughput performance with better energy efficiency.

Reducing the interference to the licensed mobile users and obtaining the energy efficiency are key issues in cognitive heterogeneous networks. A corresponding rate loss constraint is proposed to be used for the sensing-based spectrum sharing (SBSS) model in cognitive heterogeneous networks in this paper. Resource allocation optimization strategy is designed for the maximum energy efficiency under the proposed interference constraint together with average transmission power constraint. An efficiency algorithm is studied to maximize energy efficiency due to the nonconvex optimal problem. Furthermore, the relationship between the proposed protection criterion and the conventional interference constraint strategy under imperfect sensing condition for the SBSS model is also investigated, and we found that the conventional interference threshold can be regarded as the upper bound of the maximum rate loss that the primary user could tolerate. Simulation results have shown the effectiveness of the proposed protection criterion overcome the conventional interference power constraint.

Full-duplex (FD) transmission holds a great potential of improving the sum data rate of wireless communication systems. However, the self-interference introduced by the full-duplex transmitter brings a big challenge to enhance the energy efficiency. This paper investigates the power allocation problem in a full-duplex two-way (FDTW) communication network over an OFDM channel, aiming at improving the sum data rate and energy efficiency. We first characterize the sum rate and energy efficiency achieved in a single-carrier FDTW system. The optimal transmit power that achieves the maximal sum data rate is presented. The energy efficiency maximization problem is solved by using fractional programming. Then we further formulate sum rate and energy efficiency maximization problem in a multi-subcarrier FDTW system. In particular, the sub-optimal transmit power allocation which achieves a decent sum rate improvement is found by using a proposed iterative algorithm. By combining the iterative algorithm and fractional programming, we further maximize the energy efficiency of the multi-subcarrier system. With our proposed algorithm, we can easily obtain an optimal transmit power that approximates the global optimal solution. Simulation results show that using the obtained optimal transmit power allocation algorithm can significantly improve the sum rate and energy efficiency in both single-carrier and multi-subcarrier systems.