Base Station Power Research Articles

QoS-Aware Energy-Efficient MicroBase Station Deployment for 5G-Enabled HetNets

The increasing energy consumption is a legacy of the fast improvement of ICT (Information and Communication Technology). It is also contrary to the current energy conservation and emission reduction concept. There are several reasons for high energy consumption. Among them, we find that the increase in base station density of the 5G heterogeneous network (5G HetNets) is prominent. We present a micro base station deployment strategy in 5G HetNets for obtaining high energy efficiency. It optimizes target values as are trade-offs at different user distribution probabilities to improve adaptation to different user distribution scenarios. An energy deployment algorithm based on high efficiency for micro base stations is considered as jointly optimizing micro base station’s number, deployment location, and power configuration. Simulation results show that the proposed combinatorial optimization strategy effectively improves the system energy efficiency compared to the deployment method, regardless of the power factor. Moreover, it verifies that the micro base station power affects the energy efficiency in HetNets.

CSI-Impaired Beamforming Optimization for Dense MIMO Communication Networks

This paper studied the robust beamforming for dense transmission systems with imperfect channel state information (CSI). The objective was to maximize the minimum signal to interference plus noise ratio (SINR) in the constraint of the per base station (BS) power. By performing the uplink–downlink duality theory, the referred non-convex optimization problem can be changed into the equivalent uplink decoupling optimization problem. Then, we proposed the instantaneous updating uplink–downlink power algorithm, which relies on instantaneous CSI for the finite system. For the massive MIMO system, to obtain the solution to the problem required for power to instantaneously update, we proposed the updating uplink–downlink power algorithm, which only requires statistical CSI, by applying the random matrix theory. The simulation results show the feasibility and effectiveness of our proposed algorithms.

On reducing energy cost consumption in heterogeneous cellular networks using optimal time constraint algorithm

The increasing data demand in recent years has resulted in a considerable rise in heterogeneous cellular network energy usage. Advances in heterogeneous cellular networks with renewable energy supplied from base stations offer the cellular communications sector interesting options. Rising energy consumption, fuelled by huge growth in user count as well as usage of data, has emerged as the most pressing challenge for operators in fulfilling cost-cutting and environmental-impact objectives. The use of minimum power relay stations or base stations in conventional microcells is intended to lower the cellular network's total energy usage. This paper examines the reasons, difficulties, and techniques for addressing renewable heterogeneous networks' energy cost reduction issue. Because of the variety of renewable energy and mobile traffic, the issue of reducing energy costs necessitates both spatial and temporal resource allotment optimization. In this paper, we proposed a new technique for reducing the energy consumption cost using the optimal time constraint algorithmic approach. We demonstrate that the proposed method has time as well as space complexity. Experimental simulations on actual databases with synthetic costs are used to confirm the usefulness and efficacy of our method.

α-Fairness-Maximizing User Association in Energy-Constrained Small Cell Networks

Renewable energy source (RES)-powered base stations have received tremendous research interest in recent years because they can expand network coverage without building a power grid. This paper proposes a novel user association (UA), resource allocation (RA), and dynamic power control (PC) scheme to maximize the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> -fairness in RES-assisted small cell networks. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> -fairness is a general notion that flexibly adjusts the balance between the throughput, proportional fairness, and max-min fairness according to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> . Nevertheless, none of the existing studies has proposed UA, RA, and PC to maximize the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> -fairness due to its NP-hardness. Furthermore, fixed-policy-based PC designs cannot consider time-varying environments (e.g., energy harvesting models and wireless channels) of the RES-assisted networks. We first provide a Lagrangian duality-based algorithm to solve the UA and RA problem for a fixed PC. Next, we propose a dynamic PC scheme based on deep reinforcement learning (DRL) that chooses the best PC considering the time-varying environments. However, because the UA and RA algorithm executed in each step of the dynamic PC requires a long computation time, we aim to accelerate the computation of the UA and RA with DRL. Inspired by the Lagrangian duality, we design a DRL-based UA and RA with a low-dimensional continuous variable by relaxing the UA variable, the cardinality of which increases exponentially with the number of base stations and users. The simulation results show that the proposed scheme achieves a 100 times shorter computation time than the optimization-based schemes by computing only two neural networks. In particular, although there have been numerous studies on the proportional fairness maximization, the proposed scheme outperforms the optimization-based schemes in the throughput, proportional fairness, and max-min fairness metrics.

Reinforcement Learning Based Network Coding for Drone-Aided Secure Wireless Communications

Active eavesdropper sends jamming signals to raise the transmit power of base stations and steal more information from cellular systems. Network coding resists the active eavesdroppers that cannot obtain all the data flows, but highly relies on the wiretap channel states that are rarely known in wireless networks. In this paper, we present a reinforcement learning (RL) based random linear network coding scheme for drone-aided cellular systems to address eavesdropping. In this scheme, the network coding policy, including the encoded packet number, the packet and power allocation, is chosen based on the measured jamming power, previous transmission performance and BS channel states. A virtual model generates simulated experiences to update Q-values besides real experiences for faster policy optimization. We also propose a deep RL version and design a hierarchical architecture to further accelerate the policy exploration and improve the anti-eavesdropping performance, in terms of the intercept probability, the latency, the outage probability and the energy consumption. We analyze the computational complexity, drone deployment, secure coverage area and the performance bound of the proposed schemes, which are verified via simulation results.

Joint optimization algorithm of user association and power control based on spatial traffic distribution

Abstract5G networks must reduce network overhead while meeting user needs. Based on the flow model, this paper jointly optimizes the user association and power control in cellular networks, aiming at achieving a reasonable compromise between user delay and network energy consumption. Unlike previous snapshot models, flow models focus on the spatial distribution of traffic demand instead of the specific location of users. We consider a nonuniformity of spatial traffic distribution and load coupling among base stations (BSs). Firstly, an iterative algorithm is proposed to solve the optimal user association strategy under certain power of BSs. Then, a greedy power adjustment and user association algorithm (GPA‐UA) is proposed to further optimize the objective function. The simulation results show that the proposed algorithm can reduce both user delay and network energy consumption in most cases, and the performance of the proposed algorithm is approximate to the Optimal scheme while GPA‐UA has lower complexity.

Energy Efficient Resource Allocation for IRS Assisted CoMP Systems

A novel intelligent reconfigurable surface (IRS) assisted coordinated multi-point (CoMP) system is proposed. Our objective is to maximize the energy efficiency (EE) of this system by jointly optimizing base station (BS) clustering, user association, sub-carrier assignment, power allocation, and optimal design of the IRS, while satisfying the users’ quality of service requirements. Considering the amplitude and phase shift characteristics, both ideal and non-ideal IRS are investigated. The formulated problem is proved to be NP-hard. By analyzing its structure, we decouple it into the power allocation sub-problem, the BS clustering, UE association, and sub-carrier assignment sub-problem, and the reflection coefficients design sub-problem. For the power allocation sub-problem, we invoke the fractional programming to find the optimal solution. For the reflection coefficients design sub-problem of ideal IRS, the optimal solution is derived with the Lagrangian dual method. Whereas quantization-based method is employed to find the discrete phase shifts for non-ideal IRS. We finally propose a genetic algorithm (GA) to represent the potential solutions of sub-carrier assignment, and combine the other two optimization algorithms as fitness estimator in GA. Numerical results validate the feasibility, fast convergence, and the flexibility of the proposed algorithm. It shows that the proposed scheme outperform the system without IRS and that with a random initialized IRS.

Weighted Sum-Rate of Intelligent Reflecting Surface Aided Multiuser Downlink Transmission With Statistical CSI

Intelligent reflecting surface (IRS) is a newly emerged technology that can increase the energy and spectral efficiency of wireless communication systems. This paper considers an IRS-aided multi-user multiple-input single-output (MISO) communication system, and presents a detailed analysis and optimization framework for the weighted sum-rate (WSR) of the downlink transmission over Rician fading channels. Unlike most of the prior works where the active beamformer at the base station (BS) and passive beamformer at the IRS are jointly designed based on the instantaneous channel state information (CSI), this paper proposes a low-complexity transmission protocol where the IRS passive beamforming and BS power allocation coefficient vector are optimized in the large timescale based on the statistical CSI, and the BS transmit beamforming is designed in the small timescale based on only the instantaneous CSI of the effective BS-user channels. Therefore, the channel training overhead in each channel coherence interval under our proposed protocol is independent of the number of IRS reflecting elements, which is in sharp contrast to most of the prior works. By considering maximum-ratio transmit beamforming at the BS, we derive a lower bound of the ergodic WSR in closed-form. Then, we propose an efficient algorithm to jointly optimize the IRS passive beamforming and BS power allocation coefficient vector for maximizing the ergodic WSR lower bound. Numerical results validate the tightness of our derived WSR bound and show that the proposed scheme outperforms various existing schemes in terms of complexity or capacity performance.

A Joint Positioning Algorithm in Industrial IoT Environments with mm-Wave Communications

With the real restriction of packet size and transmission power, a joint UL-AoA (Uplink Angle of Arrival) and UL-TDOA (Uplink Time Difference of Arrival) positioning method is discussed for the Industrial Internet of Things (IIoT), which demands high accuracy of position information. One motivation of the UL positioning method of IIoT is an asymmetric network and uplink traffic is dominant in most identified scenarios. Further, IIoT sensors are power limited for low cost, compared to the transmission power of base stations and other normal user equipment. This paper considers an environment of a 5G-enabled IIoT network with massive Multiple input multiple output (MIMO) antennas, beamforming and millimeter operating bands. After reviewing downlink positioning reference signal (PRS) and uplink sounding reference signal for positioning (SRS-Pos), a general system model and a theoretical localization problem is derived under the joint positioning method. The provided simulation results help to investigate the impact of system configurations on positioning measurements. Further, through the test in this paper, it is proved that joint positioning outperforms that of the time only or angle only method.

Carbon-Neutral Cellular Network Operation Based on Deep Reinforcement Learning

With the exponential growth of traffic demand, ultra-dense networks have been proposed to cope with such demand. However, the increase of the network density causes more power use, and carbon neutrality becomes an important concept to decrease the emission and production of carbon. In cellular networks, emission and production can be directly related to power consumption. In this paper, we aim to achieve carbon neutrality, as well as maximize network capacity with given power constraints. We assume that base stations have their own renewable energy sources to generate power. For carbon neutrality, we control the power consumption for base stations by adjusting the transmission power and switching off base stations to balance the generated power. Given such power constraints, our goal is to maximize the network capacity or the rate achievable for the users. To this end, we carefully design the objective function and then propose an efficient Deep Deterministic Policy Gradient (DDPG) algorithm to maximize the objective. A simulation is conducted to validate the benefits of the proposed method. Extensive simulations show that the proposed method can achieve carbon neutrality and provide a better rate than other baseline schemes. Specifically, up to a 63% gain in the reward value was observed in the DDPG algorithm compared to other baseline schemes.

Dual-Population Co-Evolution Multi-Objective Optimization Algorithm and Its Application

In the multi-objective optimization algorithm, the parameter strategy has a huge impact on the performance of the algorithm, and it is difficult to set a set of parameters with excellent distribution and convergence performance in the actual optimization process. Based on the MOEA/D algorithm framework, this paper construct an improved dual-population co-evolution MOEA/D algorithm by adopt the idea of dual-population co-evolution. The simulation test of the benchmark functions shows that the proposed dual-population co-evolution MOEA/D algorithm have significant improvements in IGD and HV indicators compare with three other comparison algorithms. Finally, the application of the LTE base station power allocation model also verifies the effectiveness of the proposed algorithm.

Beamforming Optimization for Information Enhancement Transmission in MISO SWIPT with NOMA System

This paper mainly considers a communication network with multiple-input single-output (MISO), in which nonorthogonal multiple access (NOMA) decoding technology is adopted, and wireless power-carrying technology is used to enhance the transmission quality of user information in the communication network. Through joint optimization of power splitting ratio and beamforming vector, we aim at minimizing transmission power (base station transmit power and secondary transmit power) while satisfying the quality-of-service requirement of all users and minimum power for secondary transmission. The problem is a nonconvex optimization program, and it is difficult to get the optimal value directly. First, in order to solve the problem for two-user case, the original problem is transformed into a semidefinite programming (SDP) problem, and then, the iterative updating algorithm is used to approximate the optimal value. When the number of users is greater than two, the original problem is transformed into a second-order cone problem, in which we deal with a sequence of second-order cone programming (SOCP). Results verify that the optimal value of the sequence of SOCPs is not increasing and converges to a local optimal value. Detailed simulation experiments indicate that the algorithm improves the performance of NOMA downlink beamforming under the condition of enhancing information and eliminating network interference.

Impacts of User Association and Power Control on AoI in Multi-Tier Cellular-Based IoT Networks

In this letter, we study the age of information (AoI) of a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> -tier cellular Internet-of-Things (IoTs) network by taking into account both weighted path loss association policy and fractional power control strategy. By adopting tools from stochastic geometry, we first obtain the conditional per-tier AoI and conditional success probability, and then derive the tractable expression for average AoI. The proposed analytical framework can be used to evaluate the impacts of weight association ratio of different base station (BS) tiers and power control factor on average AoI. Numerical results verify the correctness of our analysis and reveal the following two conclusions: i) the nearest association policy (i.e., with weight association ratio being 0dB) is optimal to both average AoI and overall success probability; ii) the power control factor has significant impact on average AoI, where the optimal power control factor for average AoI is larger than that for overall success probability.

Optimal SIC Ordering and Power Allocation in Downlink Multi-Cell NOMA Systems

In this work, we propose a globally optimal joint successive interference cancellation (SIC) ordering and power allocation (JSPA) algorithm for the sum-rate maximization problem in downlink multi-cell non-orthogonal multiple access (NOMA) systems. The proposed algorithm is based on the exploration of base stations (BSs) power consumption, and closed-form of optimal powers obtained for each cell. Although the optimal JSPA algorithm scales well with larger number of users, it is still exponential in the number of cells. For any suboptimal decoding order, we propose a low-complexity near-optimal joint rate and power allocation (JRPA) strategy in which the complete rate region of users is exploited. Furthermore, we design a near-optimal semi-centralized JSPA framework for a two-tier heterogeneous network such that it scales well with larger number of small-BSs and users. Numerical results show that JRPA highly outperforms the case that the users are enforced to achieve their channel capacity by imposing the well-known SIC necessary condition on power allocation. Moreover, the proposed semi-centralized JSPA framework significantly outperforms the fully distributed framework, where all the BSs operate in their maximum power budget. Therefore, the centralized JRPA and semi-centralized JSPA algorithms with near-optimal performances are good choices for larger number of cells and users.

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.

Energy-efficient deep-predictive airborne base station selection and power allocation for UAV-assisted wireless networks

Wireless networks with unmanned aerial vehicles (UAVs) as the airborne base station (ABS) become a promising technology to enhance terrestrial users’ coverage area. Moving at high speed over the wireless network covered by UAV requires an effective ABS selection scheme during the handover process to maintain the quality of service (QoS). On the other hand, the network load must be distributed fairly between the ABSs to prevent overloading at some base stations. Due to the limited power of ABSs, power saving is an essential task. Energy efficiency is a measure that balances data rate and power consumption. This study proposes a deep-predictive target ABS selection, resource block (RB), and power allocation algorithm that jointly maximizes energy efficiency and load balancing. The proposed method constructs the neighbor lists based on the reference signal received power (RSRP), signal to interference plus noise ratio (SINR), and the sojourn time estimated by the deep neural network model to reduce the unnecessary handovers. The simulation results show that the proposed method reduces the ping-pong rate and the outage probability and increases load balancing, spectral, and energy efficiencies compared to previous works.

Hybrid Beamforming for Millimeter Wave MIMO Integrated Sensing and Communications

In this letter, we consider hybrid beamforming for millimeter wave (mmWave) MIMO integrated sensing and communications (ISAC). We design the transmit beam of a dual-functional radar-communication (DFRC) base station (BS), aiming at approaching the objective radar beam pattern, subject to the constraints of the signal to interference-plus-noise ratio (SINR) of communication users and total transmission power of the DFRC BS. To provide additional degree of freedom for the beam design problem, we introduce a phase vector to the objective beam pattern and propose an alternating minimization method to iteratively optimize the transmit beam and the phase vector, which involves second-order cone programming and constrained least squared estimation, respectively. Then based on the designed transmit beam, we determine the analog beamformer and digital beamformer subject to the constant envelop constraint of phase shifter network in mmWave MIMO, still using the alternating minimization method. Simulation results show that under the same SINR constraint of communication users, larger antenna array can achieve better radar beam quality.

Energy-Efficient Massive MIMO SWIPT-Enabled Systems

This study focuses on the downlink (DL) transmission of massive multiple-input-multiple-output (MIMO) with the support of simultaneous wireless information and power transfer (SWIPT) system based on a power-splitting (PS) scheme. To reach the green design target in the concept of wireless communication networks, this paper maximizes the system’s energy efficiency (EE) using joint system-level optimization. First, the closed-form expressions of the harvested energy and achievable data rate are first derived. Then, we formulated the EE maximization problem by jointly optimizing user equipment (UE) pilot transmission time allocation, the received PS ratios, a base station (BS) transmit power allocation, and a number of BS antennas while considering the minimum data rate as user’s quality-of-service (QoS) requirement and the maximum BS power transmission constraint. However, the formulated EE maximization problem is non-convex and non-linear which is difficult to solve. Hence, we propose an efficient low-complex alternative optimization (LCAO) algorithm to solve the non-convex problem iteratively with an acceptable computational complexity level. Simulation results are provided to evaluate the derived closed-form expressions and validate the proposed LCAO algorithm’s efficiency. The optimality, convergence, and complexity of the LCAO algorithm are analytically investigated. Results indicate that the proposed LCAO algorithm outperforms the equal power allocation (EPA) scheme by 15% better EE gain and 4% better than the max-min fairness scheme when the BS power transmission capacity is higher than 3 (Watt).

Intelligent Reflecting Surface for Multi-Path Beam Routing With Active/Passive Beam Splitting and Combining

Intelligent reflecting surface (IRS) can be densely deployed in wireless networks to significantly enhance the communication channels. In this letter, we consider the downlink transmission from a multi-antenna base station (BS) to a single-antenna user, by exploiting the cooperative passive beamforming (CPB) and line-of-sight (LoS) path diversity gains of multi-IRS signal reflection. Unlike existing works where only one single multi-IRS reflection path from the BS to user is selected, we propose a new and more general <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-path beam routing</i> scheme. Specifically, the BS sends the user’s information signal via multiple orthogonal active beams (termed as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">active beam splitting</i> ), which point towards different IRSs. Then, these beamed signals are subsequently reflected by selected IRSs via their CPB in different paths, and finally coherently combined at the user’s receiver (thus named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">passive beam combining</i> ). For this scheme, we formulate a new multi-path beam routing design problem to jointly optimize the number of IRS reflection paths, the selected IRSs for each of the reflection paths, the active/passive beamforming at the BS/each selected IRS, as well as the BS’s power allocation over different active beams, so as to maximize the received signal power at the user. To solve this challenging problem, we first derive the optimal BS/IRS beamforming and BS power allocation for a given set of reflection paths. The clique-based approach in graph theory is then applied to solve the remaining multi-path selection problem efficiently. Simulation results show that our proposed multi-path beam routing scheme significantly outperforms its conventional single-path beam routing special case.

Enhanced Handover Mechanism in Long Term Evolution (LTE) Networks

Femtocell is a low power base station, wireless access point designed especially for homes and small organizations. It is promising technology for operators to improve their capacity and for users to give indoor coverage. As mobile users are increasing day by day so the legacy system is unable to provide such a high data rates to all these users. In this case femtocells play a key role to offload the data traffic from macro base station. The implementation of femtocell has posed so many challenges like interference, localization, access control and mobility management. The aim of this paper is to present an enhanced algorithm for handover in Hand-In scenario. In already existing algorithms handover is decided on the basis of a single parameter but here we have simulated an algorithm that considers multiple parameters instead of a single parameter for handover. Through this algorithm, the most suitable femtocell will be selected for handover, hence number of handovers will be decreased. Simulation results show that the system performance has been improved.