Power Consumption Of Base Stations Research Articles

Energy-Efficient Base Station Switching-Off With Guaranteed Cooperative Profit Gain of Mobile Network Operators

This paper studies the energy-efficient cooperative base station (BS) switching-off in multi-input single-output (MISO) cellular networks, where the roaming-cost-based cooperation and the cooperative beamforming among multiple mobile network operators (MNOs) are jointly designed. To make the cooperation among MNOs practical, we introduce a new constraint to make sure the cooperative profit gain of MNOs is non-negative and also formulate an optimization problem to maximize the system energy efficiency (EE) by jointly considering multiple factors, including the operation modes of BSs, the connection states between users and BSs, the beamforming vectors of multi-antenna BSs under constraints of users’ quality of service, BSs’s power budget, and the non-negative profit gain of MNOs, etc. To solve the non-convex problem, we propose an energy-efficient beamforming-aware BS switching off method (EBBSOM), in which we first design a roaming-cost-based BS switching-off algorithm (EE-BSOA) to search the feasible inactive BSs and then propose a successive convex approximation (SCA) and Dinkelbach’s method-based EE maximization beamforming algorithm (EE-BFA) to optimize beamforming vectors. In order to keep the cooperative profit gain non-negative for MNOs and also to balance the load and power consumption of BS from the profit perspective, we present a profit-wise BS switching-off priority method. We theoretically prove that the proposed EBBSOM is able to resolve the inconsistency between BS switching-off and EE maximization. Simulations show that the proposed EBBSOM enhances the system EE without degrading the economic profit gain of MNOs. Also, the proposed profit-wise BS switching-off priority enables the EBBSOM to achieve higher EE than traditional load-wise one. Furthermore, it is also observed that there exists a feasible rate price region, which is able to clearly characterize network EE and the average profit gain of MNOs simultaneously.

Analysis of multiple sleeps and N-policy on a M/G/1/K user request queue in 5g networks base station

The primary purpose of green communication is to reduce energy use. The base station (BS) is a radio receiver/transmitter that acts as the wireless network’s hub. It serves as a link between a wired and wireless network. To receive and transmit messages, BS uses a lot of energy. The use of effective sleep and wake-up/setup activities with an acceptable delay helps reduce base station power consumption. In this paper, the BS’s service process is modelled as a finite buffer queue with close down, sleep, and setup. After a certain number of user requests (URs) have accumulated in the system, to awaken the BS from multiple sleeps (MS) the -Policy is implemented. To produce probability generating functions, the supplementary variable approach is applied. The UR’s mean delay and the BS’s mean power consumption are calculated using simulation. According to computational studies, multiple sleeps with -policy consume less power than multiple sleeps without -policy.

A Low-Complexity Beam Selection Algorithm for Two-Dimensional Beamspace Massive MIMO

To mitigate the huge investment in radio-frequency devices and associated high power consumption of modern base stations, the beamspace multiple-input multiple-output (B-MIMO) has become a promising solution at an expense of acceptable performance degradation. Facing the increasingly high data throughput demand, two-dimensional (2D) massive B-MIMO systems with beams steering over both elevation and azimuth angles have been developed recently. However, with such a large number of beams available for wireless communication, effective beam selection for multiple users in three-dimensional (3D) space remains technically challenging. In this letter, low-complexity suboptimal beam selection schemes are investigated to overcome this difficulty. By avoiding massive and repetitive matrix-vector product operations in the beam selection procedure through specifically designed linear algebraic calculation strategy, a lower complexity is achieved. Numerical simulations and theoretical analyses demonstrate the proposed methods have around <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> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K^{2}$ </tex-math></inline-formula> times speedup compared to state-of-the-art beam selection approaches, where <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> is the number of users.

QoS Guaranteed Power Minimization and Beamforming for IRS-Assisted NOMA Systems

In this letter, to solve the problem of large interference among users and reduce the transmit power consumption of base station while guaranteeing target quality of service (QoS) in downlink intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) systems, we present a joint optimization algorithm to design active beamforming for base station and passive beamforming (phase shift matrix) for IRS based on semidefinite relaxation (SDR). The non-convex constraint of signal-to-interference-plus-noise ratio (SINR) is transformed into affine constraint by increasing the dimension of optimization variables. The two subproblems of transmit power minimization and phase shift feasibility are solved iteratively using alternating optimization. To solve the computing performance degradation of SDR in largescale problems, we further propose a low-complexity algorithm based on successive convex approximation (SCA) and the original problem is then determined iteratively by relaxing the constant modulus constraint of IRS. Simulation results show that both algorithms have lower power consumption than the existing algorithms with different number of IRS reflecting elements or transmit antennas, and the SCA algorithm can approach the performance of SDR with lower complexity.

Robust Transmission Design for RIS-Aided Wireless Communication With Both Imperfect CSI and Transceiver Hardware Impairments

Reconfigurable intelligent surface (RIS) has recently been regarded as a potential technique to enhance the performance of wireless communication systems by creating additional communication links. However, it is almost impossible to get the perfect channel state information (CSI) from the base station (BS) to the Internet of Things Devices (IoTDs) and the RIS-related channels. Furthermore, residual transceiver hardware impairments inevitably affect the performance of wireless communication systems. Hence, we study the robust design for an RIS-aided wireless communication system based on the imperfect CSI and hardware impairments. Minimizing the power consumption of BS is formulated by ensuring the minimum signal-to-interference-plus-noise ratio (SINR) demands of the IoTDs and the unit-modulus constraints of the RIS. Specifically, after approximating the nonconvex constraints by using the S-procedure and the successive convex approximation (SCA) methods, we adopt the block coordinate descent (BCD) technique to iteratively optimize one set of variables while keeping the other variables fixed in various channel uncertainty scenarios. Simulation results demonstrate that the influence of transceiver hardware impairments can be effectively decreased by deploying RIS even with channel uncertainty, which is more advantageous than increasing the number of BS’s antennas.

Channel Estimation for mmWave Massive MIMO Systems With Mixed-ADC Architecture

Millimeter wave (mmWave) communications are widely preferred due to the rich bandwidth and potentially huge spectrum resources. Nowadays, mixed-ADC architecture combined with mmWave massive MIMO has become a communication mainstream, which can effectively solve the issue of high total power consumption and cost of base station (BS) circuits. However, the channel estimation problem for mmWave massive MIMO systems with mixed-ADC architecture has not been studied yet. In this paper, we develop the sparse channel estimation method on this framework. Specifically, by exploiting the sparsity of mmWave channels, the beamspace channel estimation problem can be transformed into a sparse matrix recovery problem, the channel parameters are recovered using compressive sensing (CS) techniques. Simulation results show that the algorithms quantized by the mixed-ADC outperforms the low-resolution ADC, and the best performance can be achieved when the low-resolution ADC in the mixed-ADC architecture reaches five-bit.

Two-Timescale Mobile User Association and Hybrid Generator On/Off Control for Green Cellular Networks With Energy Storage

In this paper, we study the energy-efficient (EE) cost minimization problem of green cellular networks, where base stations (BSs) are jointly powered by energy storages, i.e., batteries, external power grid with random electricity price, and local hybrid generators considering independent on/off strategies in long period and turn-on/off cost. The power consumption of each BS is correlated with the number of associated mobile users (MUs) and their random mobility. We formulate a stochastic optimization problem and decompose it into two subproblems with limited optimality gap. We solve the first MU association subproblem by minimizing the price-weighted power consumption of BSs under low-complexity matching algorithm. For the second energy control subproblem, we propose a novel two-timescale approach based on stochastic network optimization to tackle with the time-coupling turn-on/off cost and battery dynamic constraints. We derive the long-term time-averaged turn-on/off cost minimized by our approach including its upper bound and approximate expression. Lastly, extensive simulation results verify our theoretical performance analyses and show that our proposed algorithm can outperform the other three online benchmark algorithms.

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.

Electric Load Profile of 5G Base Station in Distribution Systems Based on Data Flow Analysis

This paper proposes an electric load demand model of the 5th generation (5G) base station (BS) in a distribution system based on data flow analysis. First, the electric load model of a 5G BS is developed according to its components and their characteristics. Second, critical factors of the power consumption of 5G BS, including area, data flow with uncertainty number of activated terminals and data usage of the terminal, are discussed. A statistical model of the number of activated terminals and data usage of the terminal are established using a probability distribution, and Monte Carlo sampling is used to handle the uncertainties. Then, the model is extended for multiple terminal connections. A case study is conducted to analyze the impact of the critical factors on the load of 5G BS and the influence of 5G BSs load on the other loads in three typical areas. Numerical results demonstrate that the proposed model is effective and can be employed as an accurate representation of the 5G BS load profile for the analysis of load characteristics. Case studies also show that the 5G BS loads have diverse impact on different typical areas in a distribution system.

Low-Latency QC-LDPC Encoder Design for 5G NR.

In order to meet the low latency and high throughput requirements of data transmission in 5th generation (5G) New Radio (NR), it is necessary to minimize the low power encoding hardware latency on transmitter and achieve lower base station power consumption within a fixed transmission time interval (TTI). This paper investigates parallel design and implementation of 5G quasi-cyclic low-density parity-check (QC-LDPC) codes encoder. The designed QC-LDPC encoder employs a multi-channel parallel structure to obtain multiple parity check bits and thus reduce encoding latency significantly. The proposed encoder maps high parallelism encoding algorithms to a configurable circuit architecture, achieving flexibility and support for all 5G NR code length and code rate. The experimental results show that under the 800 MHz system frequency, the achieved data throughput ranges from 62 to 257.9 Gbps, and the maximum code length encoding time under base graph 1 (BG1) is only 33.75 ns, which is the critical encoding time of our proposed encoder. Finally, our proposed encoder was synthesized on SMIC 28 nm CMOS technology; the result confirmed the effectiveness and feasibility of our design.

Fractional Frequency Reuse in Ultra Dense Networks

Ultra Dense Network (UDN) in which Base Stations (BSs) are deployed at an ultra high density is a promising network model of the future wireless generation. Due to ultra densification, reuse of frequency bands with an ultra high density is compulsory for this network. Conventionally, the research on frequency reuse technique such as Fractional Frequency Reuse (FFR) classifies the active users into only two groups. However, this approach is not suitable for UDNs where the signal experiences a huge amount of power loss over distances. Thus, this paper proposes a generalized model of FFR for UDNs where the active users are classified into more than two groups The paper introduces a simple approach to obtain the coverage probability of a typical user in the case of a general path loss model. In the case of stretched path loss model for UDNs, the closed-form expression of user coverage probability is derived. From the analytical and simulation results, it is stated that the proposed model can improve user performance without increasing BS power consumption. Furthermore, two additional interesting conclusions are found in this paper: (i) the user coverage probability increases to a peak before passing a decline when the density of BSs increases; (ii) an increase in BS transmission power may decrease the user performance.

Max–min fair robust beamforming design for underlay device‐to‐device communications in cellular networks

SummaryThis paper investigates the robust downlink beamforming designs based on max–min fairness and base station (BS) power consumption constraint for device‐to‐device (D2D) communications underlaying cellular network, under the assumption of imperfect channel state information (CSI) at the BS. Our objective is to maximize the minimum signal‐to‐interference‐plus‐noise ratio (SINR) or non‐outage probability of users while guaranteeing that the consumed power at the BS is less than a threshold. In particular, three max–min fairness scenarios are considered. In the first scenario, the worst‐case SINR of cellular users (CUs) is maximized where the D2D SINR is guaranteed to be above a specified predetermined threshold. In the second scenario, we extend the first scenario and maximized D2D SINR while maximizing CUs' SINR. In these two scenarios, it is assumed that the errors are upper bounded in their Frobenius norms. In the third scenario, the minimum non‐outage probability of users is maximized, while a probabilistic model is considered for the uncertainty of channel covariance matrices. Although such optimization problems are not convex, the semidefinite relaxation (SDR) approach is used to obtain the optimal beamforming matrix, which always complies the rank‐one constraint. Simulation results show that significant improvement in terms of minimum SINR of CUs, the minimum non‐outage probability of CUs, and the probability of feasibility and the fairness index can be achieved with our proposed algorithms in comparison with other beamforming schemes.

Study of DRX Sleep Mode Performance on Virtual Base Station Energy Saving in 5G Networks

No doubt that power consumption is one of the most important challenges which face the modern mobile networks. These modern networks do a nice job in multimedia services. Then, the physical downlink data transmission rate will change the Base Station (BS) power consumption. The Conventional BS (CBS) of the cellular legacy networks was consuming much energy due to complicated hardware connections and system levels. Now, the fifth generation of mobile networks (5G) is introducing the Virtual Base Station (VBS) based on the Cloud infrastructure. Therefore, it can reduce the traditional complexity and save more energy consumption. In this paper, the Discontinuous Reception (DRX) sleep mode is applied on the corresponding User Equipment (UE). This has an effective impact on the VBS power consumption. A series of MATLAB simulation experiments is conducted to assess the performance. Appropriate path loss models are considered in the experimental study for mmWave broadband spectrum of 5G.

Outage Aware Power Control for Vehicular Wireless Energy Transfer Over Dynamic Channels

Wireless powered devices suffer from dynamic energy arrival in case of fast fading channel conditions caused by high mobility of the energy base station (BS), which may lead to energy outage for practical applications executed at the wireless device (WD). We use effective bandwidth theory to investigate the tail distribution of the WD's energy queue, from which the energy outage probability (EOP) is derived. Then, a power control problem is proposed to minimize the mobile vehicular BS's transmit power while guaranteeing the EOP for the WD, which can be equivalently addressed by solving a single equation. The match between simulation results and theoretical analysis demonstrates the validity of the proposed EOP analysis. Furthermore, the proposed scheme can reduce the power consumption of the vehicular BS while guaranteeing EOP requirement of the WD.

Performance evaluation of UE-controlled intelligent handover algorithm for natural disaster

&lt;span&gt;This paper proposes a UE-controlled intelligent handover algorithm for natural disaster. In this handover algorithm, two variables known as modified Received Signal Strength (RSSm) and Left over Power (LoP) are identified. The RSSm is an improved formulation from RSS where distance fraction coefficient has been introduced. The fraction coefficient of 0.2 is used where the affected areas is reduced so that the users can receive good signal quality due to its location near to the base station. Meanwhile, the LoP also has been investigated to control power consumption of base station. In this research, 80% RSSm and 20% LoP has been chosen for the proposed handover algorithm as it can maintain good quality of service (QoS) for all users and also can prolong battery life. From the simulation results obtained, the average number of handovers for the proposed handover algorithm outperformed the conventional natural disaster handover algorithm.&lt;/span&gt;

Nonlinear One-Bit Precoding for Massive MIMO Downlink Systems With l₀-Norm Constraint

Using one-bit digital-to-analog converters can significantly reduce the power consumption of base station in the massive multiple-input multiple-output downlink system. In this letter, we introduce $l_{0}$ -norm constraint on quantized transmit signals in the nonlinear one-bit precoding to further reduce the number of radio-frequency chains and thus the power consumption. This precoding is first formulated as a non-convex optimization problem, then the alternative direction method of multipliers (ADMM) is employed to solve this problem. Specifically, an efficient two-step implementation of the non-convex projection operator in ADMM is presented. Finally, the performance of proposed scheme and the effectiveness of proposed algorithm are validated by computer simulations.

Stochastic Power Saving for Macrocell-Assisted Small Cell Networks

In this paper, a power saving scheme is investigated for macrocell-assisted small cell networks in the downlink, in which small cells are selected to stay in sleep or wake-up modes in order to mitigate excessive power consumption and the small cells operating in the sleep mode are eligible to off-load their served users to the macrocell to preserve communications. By taking the dynamics of user arrival into account, we attempt to minimize the average total power consumption of the network, which depends on several network parameters like power consumption of base stations, available bandwidth, user load in the cells, cell size, user rate requirement, rate outage probability and noise power density, while ensuring the average number of dropping off-loaded users to a satisfactory level. The design problem is then formulated as a constrained Markov decision process and solved via linear programming. A randomized strategy is proposed to accomplish the optimal sleep/wake-up policy for small cells. Simulation results confirm the effectiveness of the proposed scheme, as compared with the one without adopting sleep/wake-up mechanisms, and help us to capture the impact of the network parameters on the entire power saving.

Heterogeneous cellular network optimization for green access of IoT traffics

The Internet of Things(IoT) has become an essential supporting platform for the present and future cyber-enabled services. Cellular networks is considered as the main channel of the data access for IoT terminals distributed in the region of interest, and they have an irreplaceable value, especially in wide-area coverage. Thus, it has a significant application value to reduce the downlink transmit power consumption of base stations under the restrictions of the coverage requirements for the green communication in heterogeneous cellular networks. A gradient descent algorithm was proposed based on smooth approximation and root mean square propagation. The algorithm could minimize the total downlink power consumption of base stations while satisfying the IoT service coverage. First, the penalty function method was used to simplify such an optimization problem with complicated constraints to a new one with simple constraints. Then, the non-derivative objective function was transformed by an approximation method into a derivable form. We also presented the close-form of the gradient of the objective function with respect to both the azimuths of the antennas installed in the base stations and the downlink transmit power levels related to these antennas. Finally, the gradient descent algorithm with root mean square propagation was used to execute the optimization of the newly approximated but smoothed version of the original objective function. Simulation experiments were conducted, and the results show that the proposed algorithm can significantly reduce the total power consumption of the downlink radio frequency transmit under the restrictions of the coverage ratio requirements in the region of interest. Furthermore, not only is the convergence speed of the proposed algorithm very fast, but also the oscillation phenomenon that occurs during the iterative procedure steps of the optimization is greatly suppressed by the proposed algorithm compared with the meta-heuristic algorithms and ordinary gradient descent method.

Efficient Power Control Framework for Small-Cell Heterogeneous Networks.

Heterogeneous networks are rapidly emerging as one of the key enablers of beyond fifth-generation (5G) wireless networks. It is gradually becoming clear to the network operators that existing cellular networks may not be able to support the traffic demands of the future. Thus, there is an upsurge in the interest of efficiently deploying small-cell networks for accommodating a growing number of user equipment (UEs). This work further extends the state-of-the-art by proposing an optimization framework for reducing the power consumption of small-cell base stations (BSs). Specifically, a novel algorithm has been proposed which dynamically switches off the redundant small-cell BSs based on the traffic demands of the network. Due to the dynamicity of the formulated problem, a new UE admission control policy has been presented when the problem becomes infeasible to solve. To validate the effectiveness of the proposed solution, the simulation results are compared with conventional techniques. It is shown that the proposed power control solution outperforms the conventional approaches both in terms of accommodating more UEs and reducing power consumption.

Application Layer Energy-Efficient Scalable Video Cooperative Multicast in Cellular Networks

In conventional multicasting of cellular networks, due to channel diversity of receivers, data rates of the base stations (BS) are limited to ensure all mobile stations (MS) receive packets correctly. This method, besides the increased power consumption, deprives MSs from getting better video quality than they could get at higher bit-rates. This paper proposes two new application layer multicasting schemes named HMCM and H2CM that exploit cooperative multicast (CM) of two-layer scalable video coding to transmit each layer of video in two different paths in order to reduce power consumption. In both methods, the base layer of video is transmitted by the BS through cellular network (e.g., 4G or 5G) to all MSs in the cell area. This makes sure minimum video quality is guaranteed for all MSs. In the second stage, the enhancement layer of video is transmitted by CM in multi-stages to all MSs that helps to reduce power consumption. Using mathematical analysis and NS3 simulator, it is shown, compared to the conventional multicasting of single layer video, both HMCM and H2CM, at a given error rate, depending on the ratio of the base layer to total video bit-rate, can reduce power consumption of the BS by 70% and 40% respectively and 12% reduction in total power consumption by H2CM. Moreover, since the proposed approaches are entirely implemented in the application layer, they can be used in most wireless standards without any modifications to the network stack.