Base Station Power Constraints Research Articles

RETRACTED: Energy Efficiency Maximization for Hybrid-Powered 5G Networks with Energy Cooperation

The extensive deployment of 5G cellular networks causes increased energy consumption and interference in systems, and to address this problem, this paper investigates the optimization problem of joint energy harvesting and energy cooperation to maximize energy efficiency (EE). First, considering user equipment (UE) quality of service (QoS) constraints, cellular base station power constraints, and renewable energy harvesting constraints, we construct a mixed-integer nonlinear programming problem for joint resource allocation. This problem is difficult to solve directly, thus we combine the fixed-variable method to solve the complex original problem in three less difficult subproblems of user association, power allocation, and energy cooperation by solving them separately using Lagrangian method, improved particle swarm optimization algorithm, and matching theory, respectively. Finally, the final solution to the original problem is obtained by combining the above three algorithms through convergent iterative algorithms. The simulation results show that the joint algorithm proposed in this paper has a better performance in throughput and energy efficiency compared with the comparison algorithms.

User Scheduling Based on Multi-Agent Deep Q-Learning for Robust Beamforming in Multicell MISO Systems

Maximizing the rate in multiple input single output (MISO) systems using distributed algorithms is an important task that typically incurs high computational cost. In this work, we propose two deep Q-learning-based user scheduling schemes to solve the beamforming problem of sum-rate maximization with per base station power constraints in multicell MISO scenarios. The two key features of the proposed algorithms are that they are executed in a distributed fashion and are robust with respect to channel state information (CSI) errors. Simulation results show that in the presence of CSI errors the proposed schemes outperform state-of-the-art algorithms both in terms of average spectral efficiency and execution time.

Coordinated Multicell Multicast Beamforming Based on Manifold Optimization

Multicast beamforming is a key technology for next-generation wireless cellular networks to support high-rate content distribution services. In this paper, the coordinated downlink multicast beamforming design in multicell networks is considered. The goal is to maximize the minimum signal-to-interference-plus-noise ratio of all users under individual base station power constraints. We exploit the fractional form of the objective function and geometric properties of the con-straints to reformulate the problem as a parametric manifold optimization program. Afterwards we propose a low-complexity Dinkelbach-type algorithm combined with adaptive exponential smoothing and Riemannian conjugate gradient iteration, which is guaranteed to converge. Numerical experiments show that the proposed algorithm outperforms the existing SDP-based method and DC-programming-based method and achieves near-optimal performance.

MVDR-Based Multicell Cooperative Beamforming Techniques for Unicast/Multicast MIMO Networks With Perfect/Imperfect CSI

In this paper, we consider unicast/multicast multicell cooperative transmission with interference among users/user groups. In this context, we derive the optimal minimum variance distortionless response (MVDR)-based successive minimum variance beamforming (SMVB) scheme that achieves a superior sum-rate performance compared with the block diagonalization scheme. Subsequently, we derive an optimal joint power allocation and broadcast beamforming scheme for multicell cooperative broadcast transmission for a pooled power constraint at the base stations. This scheme is extended to include per base station power constraints by formulating it as a relaxed semi-definite program (SDP). We also consider the multicell cooperative uplink scenario and derive a successive multiuser (MU) uplink beamforming (SMUB) scheme that maximizes the signal-to-interference-plus-noise ratio (SINR) of each user. Next, we consider imperfect channel state information (CSI) and derive a robust beamforming scheme for the downlink, based on minimizing the worst-case interference by formulating it as a second-order cone program. It is demonstrated that the corresponding robust beamformer for the uplink transmission can be derived by employing the multidimensional covariance fitting approach. Simulation results are presented to demonstrate the superior sum-rate performance of the proposed multicell transmission schemes. The performance is also compared with several bounds for cooperative multicell scenarios.

Base station beamforming technique using multiple signal‐to‐interference plus noise ratio balancing criteria

The authors propose a coordinated multi-cell beamforming technique for signal-to-interference plus noise ratio (SINR) balancing under multiple base station power constraints. Instead of balancing SINR of all users in all cells to the same level, the authors’ propose a new approach to balance SINR of users in various cells to different maximum possible values. This has the ability to allow users in cells with relatively more transmit power or better channel condition to achieve a higher balanced SINR than that achieved by users in the worst-case cells. This multi-level SINR balancing problem is solved using SINR constraints based SINR balancing criterion and subgradient method. The simulation results support the optimality of the results through comparison with semi-definite programming-based optimisation.

On Robust Weighted Sum Rate Maximization for MIMO Interfering Broadcast Channels with Imperfect Channel Knowledge

In this letter, we propose robust transceiver designs for MIMO interfering broadcast channels while taking imperfect channel knowledge into consideration. By exploiting the relationship between the weighted sum rate (WSR) and the weighted sum mean square error, we design a transceiver that maximizes WSR subject to per base station power constraint. We also develop a transceiver design with lower complexity than the WSR maximization transceiver by adopting the power constraint per user. From simulation results, we confirm the resilience of both the proposed designs against imperfect channel knowledge.

Linear and Nonlinear Precoding Schemes for Centralized Multicell MIMO-OFDM Systems

The aim of this article is to propose and compare linear and nonlinear precoding schemes for multicell multiuser MIMO-OFDM based systems. The considered linear precoder is designed in two phases: first the intercell interference is removed by applying a linear zero-forcing algorithm. Then the system is further optimized by proposing three power allocation algorithms with per base station power constraint and different complexity tradeoffs: one optimal to minimize the average bit-error-rate and two suboptimal. The proposed nonlinear precoding is designed to minimize average bit-error-rate, over the users, conditioned to a channel realization and the transmitted data. In the high SNR regime, this problem simplifies from a constrained quadratic nonlinear optimization to a single quadratic problem with a scaling, allowing to reduce the complexity. The performance of the proposed schemes is evaluated, considering typical pedestrian scenarios based on LTE specifications. Numerical results show that the performance of the nonlinear scheme outperforms the linear ones. The nonlinear algorithm selects and inverts part of the correlation matrix unlike the linear zero-forcing where full inversion is required. This leads to a better performance as the selection allows to get a better conditioned matrix. Also, it is shown that the complexity of the nonlinear scheme is similar to the linear suboptimal closed-form one.

Constrained power allocation schemes for coordinated base station transmission using block diagonalization

Abstract In this study, we propose several power allocation schemes in a coordinated base station downlink transmission with per antenna and per base station power constraints. Block Diagonalization is employed to remove interference among users. For each set of power constraints, two schemes based on the waterfilling distribution are proposed and compared to the optimal solution, which can only be obtained numerically by using convex optimization. We show that the proposed schemes achieve a performance, in terms of weighted sum rate, very close to the optimal, without the heavy computational complexity required by the latter. The sum rates are compared first in a simplified two-user two-cell case where we also compare our approach to the previous solutions available in the literature. Then, we examine the performance in a multi-cell scenario where we also evaluate the degradation of the performance caused by imperfect channel state information.

Cooperative base station beamforming in WiMAX systems

Cooperative base station for downlink multiple-input–multiple-output (MIMO) system is known as a critical radio technology for worldwide interoperability for microwave access (WiMAX) communications. This study proposes to use zero-forcing beamforming for cooperative base stations in a downlink multicell MIMO system. It is well known that beamforming requires perfect knowledge of channel state information (CSI) at the transmitter, and in practice the perfect CSI may not be available because of channel estimation errors. In this study, using approximate capacity loss analysis the authors are able to analyse the effect of channel estimation errors on system capacity in a cooperative base station system with zero-forcing beamforming. A power allocation policy is proposed to reduce the capacity loss under per base station power constraints. Numerical results show that the approximate capacity loss is very close to the real capacity loss, which can be reduced with the help of the power allocation policy.

Spectrally efficient wireless systems with cooperative precoding and beamforming

Interference among multiple base stations that coexist in the same location limits the capacity of wireless networks. In this paper, we propose a method to design a spectrally efficient cooperative downlink transmission scheme employing precoding and beamforming. The algorithm eliminates interference and achieves symbol error rate (SER) fairness among different users. To eliminate the interference, Tomlinson Harashima precoding (THP) is used to cancel part of the interference while the transmit-receive antenna weights are chosen to cancel the remaining interference. A novel iterative method is applied to generate the transmit-receive antenna weights. The convergence behaviour of the iterative process is investigated. To achieve SER fairness among different users and improve the performance of the system, we develop algorithms that provide equal signal to-interference-plus-noise-ratios (SINR) across all users under both per base station (BS) and total BSs power constraints. Per BS and total BSs power constraints are constraints where the power for each BS and all BSs is limited to a particular value. The simulation results show that the proposed scheme outperforms existing cooperative transmission schemes in terms of the SER performance and complexity and closely approaches an interference free performance under the same configuration.

Multi-User Cooperative Base Station Systems With Joint Precoding and Beamforming

Interference among multiple base stations that coexist in the same location limits the capacity of wireless networks. In this paper, we propose a method to design a single/multi-stream multi-user multiple-input multiple-output (MIMO) cooperative downlink transmission scheme employing precoding and beamforming under both the per base station (BS) and total BS power constraints. The per BS and total BS power constraints are the constraints where the power for each BS and all BSs is limited to a particular value, respectively. The algorithm eliminates the interference and achieves symbol-error rate (SER) fairness among different users. To eliminate the interference, Tomlinson-Harashima precoding (THP) is used to cancel part of the interference while the transmit-receive antenna weights cancel the remaining part. An iterative method based on the uplink-downlink duality principle is used to generate the transmit-receive antenna weights. The algorithm provides an equal signal-to-interference-plus-noise-ratio (SINR) across all users and reduces its computational complexity by trading off the complexity for a slight performance degradation. The proposed methods are extended to the scenario when the receiver does not have complete channel state informations (CSIs). Lastly, we propose a new method of selecting the user precoding order, which has a much lower complexity than VBLAST ordering but with almost the same performance. The simulation results show that the proposed schemes considerably outperform existing nonlinear and linear cooperative transmission schemes in terms of SER performance and approach an interference free performance.

PRICE-BASED RESOURCE ALLOCATION IN CDMA NETWORKS AND STABILITY ANALYSIS

We present an optimal model based on utility functions and pricing for downlink radio resource allocation in code division multiple access (CDMA) systems. The aim of this model is to achieve the throughput maximization via dynamic price adjustment. Due to the existence of action delays, we give globally asymptotical stability results using Lyapunov-Razumikhin arguments for rate and price evolvement closed-loop systems. Considering total power constraint for base station, we introduce a power allocation policy, which need only users’ local information. Simulation results validate the effectiveness of the joint rate and power control scheme.