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Robust online energy efficiency optimization for distributed multi-cell Massive MIMO networks

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Science China(Information Sciences) 2023年第3期66卷 239-252页

作者： Li YOU Yufei HUANG Wen ZHONG Wenjin WANG Xiqi GAO National Mobile Communications Research Laboratory Southeast University Purple Mountain Laboratories

This paper studies the energy efficiency(EE) oriented precoding design in multi-cell massive multiple-input multiple-output(mimo) systems, with only statistical channel state information(CSI) at the transmitter. During the transmission, as the channel varies dynamically with time and the previously obtained CSI becomes outdated, the base stations must adjust their transmit policies accordingly. To tackle this issue, we propose an online EE maximization algorithm that can achieve a no-regret transmission; i.e.,the performance of this online method gradually approaches that of the fixed offline method which has full knowledge of the future CSI. Specifically, we first construct the online EE optimization problem in a distributed way to reduce the information required to be exchanged between cells. Then, we apply the largedimensional random matrix theory to lower the calculation complexity, and the Charnes-Cooper transform to address the nonconvexity of the problem, respectively. The online gradient ascent method is utilized to perform this no-regret power allocation strategy based on all past CSI. We also assess the robustness of the algorithm to estimation error of statistical CSI under some mild conditions which can usually be satisfied in practice. Numerical results demonstrate the no-regret property and the robustness of the proposed online algorithm for energy efficient multi-cell Massive MIMO transmission.

关键词： energy efficiency statistical CSI multi-cell mimo Massive MIMO online gradient ascent distributed processing robust transmission

An energy-efficient resource allocation strategy in Massive MIMO-enabled vehicular edge computing networks

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高置信计算(英文) 2023年第3期3卷 40-49页

作者： Yibin Xie Lei Shi Zhenchun Wei Juan Xu Yang Zhang School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of EducationHefei 230009China Hefei Origin IoT Technology Co. LtdHefei 230088China

The vehicular edge computing(VEC)is a new paradigm that allows vehicles to offload computational tasks to base stations(BSs)with edge servers for computing.In general,the VEC paradigm uses the 5G for wireless communications,where the massive multi-input multi-output(mimo)technique will be used.However,considering in the VEC environment with many vehicles,the energy consumption of BS may be very large.In this paper,we study the energy optimization problem for the Massive MIMO-based VEC network.Aiming at reducing the relevant BS energy consumption,we first propose a joint optimization problem of computation resource allocation,beam allocation and vehicle grouping scheme.Since the original problem is hard to be solved directly,we try to split the original problem into two subproblems and then design a heuristic algorithm to solve them.Simulation results show that our proposed algorithm efficiently reduces the BS energy consumption compared to other schemes.

关键词： Vehicular edge computing Massive MIMO Resource allocation Energy-efficient

Coordinated multicast and unicast transmission in V2V underlay Massive MIMO

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Science China(Information Sciences) 2022年第3期65卷 235-245页

作者： Xinxin NIU Li YOU Xiqi GAO National Mobile Communications Research Laboratory Southeast University Purple Mountain Laboratories

This paper investigates coordinated multicast and unicast transmission for vehicle-to-vehicle(V2 V) underlay massive multiple-input multiple-output(mimo). First, the achievable ergodic multicast rate for the cellular link and the achievable ergodic unicast rate for all the V2 V links are summed with weight to formulate the rate optimization problem, with the assumption that the statistical channel state information(CSI) is known at the base station and the transmitters of the V2 V communication pairs. We then derive the optimal transmitting directions in closed-form for the cellular link and all the V2 V links,respectively, which converts the original optimal problem to a simpler power allocation problem in the beam domain. Via invoking the concave-convex procedure, an efficient iterative algorithm with guaranteed convergence is proposed for the power allocation problem. Furthermore, we replace the objectives which contain high-complex expectation operations with their deterministic equivalents in each iteration of the proposed algorithm to achieve lower algorithm complexity. Simulation results show the fast convergence speed of the proposed power allocation algorithm and the significant performance gains of the proposed transmission design for V2 V underlay Massive MIMO.

关键词： coordinated multicast and unicast transmission V2V underlay Massive MIMO statistical CSI

DQL-Based Intelligent Scheduling Algorithm for Automatic Driving in Massive MIMO V2I Scenarios

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China Communications 2023年第3期20卷 18-26页

作者： Yong Liao Zisong Yin Zhijing Yang Xuanfan Shen School of Microelectronics and Communication Engineering Chongqing UniversityChongqing 400044China

Connected and autonomous vehicle(CAV)vehicle to infrastructure(V2I)scenarios have more stringent requirements on the communication rate,delay,and reliability of the Internet of vehicles(Io V).New radio vehicle to everything(NR-V2X)adopts link adaptation(LA)to improve the efficiency and reliability of road safety information transmission.In order to solve the problem that the existing LA scheduling algorithms cannot adapt to the Doppler shift and complex fast time-varying channel in V2I scenario,resulting in low reliability of information transmission,this paper proposes a deep Q-learning(DQL)-based massive multiple-input multiple-output(mimo)LA scheduling algorithm for autonomous driving V2I scenario.The algorithm combines deep neural network(DNN)with Q-learning(QL)algorithm,which is used for joint scheduling of modulation and coding scheme(MCS)and space division multiplexing(SDM).The system simulation results show that the algorithm proposed in this paper can fully adapt to the different channel environment in the V2I scenario,and select the optimal MCS and SDM for the transmission of road safety information,thereby the accuracy of road safety information transmission is improved,collision accidents can be avoided,and bring a good autonomous driving experience.

关键词： NR autonomous driving V2I link adap-tation Massive MIMO deep Q-learning

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Energy-Efficient Resource Optimization for Massive MIMO Networks Considering Network Load

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Computers, Materials & Continua 2022年第4期71卷 871-888页

作者： Samira Mujkic Suad Kasapovic Mohammed Abuibaid Department of Telecommunications Faculty of Electrical EngineeringUniversity of TuzlaTuzla75000Bosnia Herzegovina 2Department of Systems and Computer Engineering Faculty of Engineering and DesignCarleton UniversityOttawaK1S 5B6Canada

This paper investigates the resource optimization problem for a multi-cell massive multiple-input multiple-output(mimo)network in which each base station(BS)is equipped with a large number of antennas and each base station(BS)adapts the number of antennas to the daily load profile(DLP).This paper takes into consideration user location distribution(ULD)variation and evaluates its impact on the energy efficiency of load adaptive Massive MIMO system.ULD variation is modeled by dividing the cell into two coverage areas with different user densities:boundary focused(BF)and center focused(CF)ULD.All cells are assumed identical in terms of BS configurations,cell loading,and ULD variation and each BS is modeled as an M/G/m/m state dependent queue that can serve a maximum number of users at the peak load.Together with energy efficiency(EE)we analyzed deployment and spectrum efficiency in our adaptive Massive MIMO system by evaluating the impact of cell size,available bandwidth,output power level of the BS,and maximum output power of the power amplifier(PA)at different cell loading.We also analyzed average energy consumption on an hourly basis per BS for the model proposed for data traffic in Europe and also the model proposed for business,residential,street,and highway areas.

关键词： Massive MIMO traffic load energy efficiency user location distribution optimization

Low Complexity Detection Algorithms Based on ADMIN for Massive MIMO

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China Communications 2023年第11期20卷 67-77页

作者： Shuchao Mi Jianyong Zhang Fengju Fan Baorui Yan Muguang Wang Institute of Lightwave Technology Beijing Jiaotong UniversityBeijing 100044China Key Lab of All Optical Network and Advanced Telecommunication of EMC Beijing Jiaotong UniversityBeijing 100044China

This paper proposes the alternating direction method of multipliers-based infinity-norm(ADMIN) with threshold(ADMIN-T) and with percentage(ADMIN-P) detection algorithms,which make full use of the distribution of the signal to interference plus noise ratio(SINR) for an uplink Massive MIMO system.The ADMIN-T and ADMIN-P detection algorithms are improved visions of the ADMIN detection algorithm,in which an appropriate SINR threshold in the ADMIN-T detection algorithm and a certain percentage in the ADMIN-P detection algorithm are designed to reduce the overall computational complexity.The detected symbols are divided into two parts by the SINR threshold which is based on the cumulative probability density function(CDF) of SINR and a percentage,respectively.The symbols in higher SINR part are detected by MMSE.The interference of these symbols is then cancelled by successive interference cancellation(SIC).Afterwards the remaining symbols with low SINR are iteratively detected by ADMIN.The simulation results show that the ADMIIN-T and the ADMIN-P detection algorithms provide a significant performance gain compared with some recently proposed detection algorithms.In addition,the computational complexity of ADMIN-T and ADMIN-P are significantly reduced.Furthermore,in the case of same number of transceiver antennas,the proposed algorithms have a higher performance compared with the case of asymmetric transceiver antennas.

关键词： ADMIN low complexity detection algo-rithm Massive MIMO MMSE SINR

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A Sparse Optimization Approach for Beyond 5G mmWave Massive MIMO Networks

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Computers, Materials & Continua 2022年第8期72卷 2797-2810页

作者： Waleed Shahjehan Abid Ullah Syed Waqar Shah Imran Khan Nor Samsiah Sani Ki-Il Kim Department of Electrical Engineering University of Engineering and Technology PeshawarPakistan Center for Artificial Intelligence Technology Faculty of Information Science&TechnologyUniversiti KebangsaanKajang43000Malaysia Department of Computer Science and Engineering Chungnam National UniversityDaejeon34134Korea

Millimeter-Wave(mmWave)Massive MIMO is one of the most effective technology for the fifth-generation(5G)wireless networks.It improves both the spectral and energy efficiency by utilizing the 30–300 GHz millimeter-wave bandwidth and a large number of antennas at the base station.However,increasing the number of antennas requires a large number of radio frequency(RF)chains which results in high power consumption.In order to reduce the RF chain’s energy,cost and provide desirable quality-ofservice(QoS)to the subscribers,this paper proposes an energy-efficient hybrid precoding algorithm formm Wave Massive MIMO networks based on the idea of RF chains selection.The sparse digital precoding problem is generated by utilizing the analog precoding codebook.Then,it is jointly solved through iterative fractional programming and successive convex optimization(SCA)techniques.Simulation results show that the proposed scheme outperforms the existing schemes and effectively improves the system performance under different operating conditions.

关键词： 5G mmwave precoding Massive MIMO complexity

QBFO-BOMP Based Channel Estimation Algorithm for mmWave Massive MIMO Systems

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Computer Modeling in Engineering & Sciences 2023年第11期137卷 1789-1804页

作者： Xiaoli Jing Xianpeng Wang Xiang Lan Ting Su State Key Laboratory of Marine Resource Utilization in South China Sea and School of Information and Communication Engineering Hainan UniversityHaikou570228China

At present,the traditional channel estimation algorithms have the disadvantages of over-reliance on initial conditions and high complexity.The bacterial foraging optimization(BFO)-based algorithm has been applied in wireless communication and signal processing because of its simple operation and strong self-organization ability.But the BFO-based algorithm is easy to fall into local optimum.Therefore,this paper proposes the quantum bacterial foraging optimization(QBFO)-binary orthogonal matching pursuit(BOMP)channel estimation algorithm to the problem of local optimization.Firstly,the binary matrix is constructed according to whether atoms are selected or not.And the support set of the sparse signal is recovered according to the BOMP-based algorithm.Then,the QBFO-based algorithm is used to obtain the estimated channel matrix.The optimization function of the least squares method is taken as the fitness function.Based on the communication between the quantum bacteria and the fitness function value,chemotaxis,reproduction and dispersion operations are carried out to update the bacteria position.Simulation results showthat compared with other algorithms,the estimationmechanism based onQBFOBOMP algorithm can effectively improve the channel estimation performance of millimeter wave(mmWave)massive multiple input multiple output(mimo)systems.Meanwhile,the analysis of the time ratio shows that the quantization of the bacteria does not significantly increase the complexity.

关键词： Channel estimation bacterial foraging optimization quantum bacterial foraging optimization binary orthogonal matching pursuit Massive MIMO

Coexistence Between Massive MIMO and Radar Communications:Performance Analysis

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Journal of Communications and Information Networks 2023年第1期8卷 37-47页

作者： Mohamed Elfiatoure Hien Quoc Ngo Michail Matthaiou Centre for Wireless Innovation(CWI) Queen’s University BelfastBelfast BT39DTUK

Frequency spectrum sharing between radar and communication systems has recently attracted substantial attention.We consider the coexistence between a massive multiple-input multiple-output(mimo)downlink system and mimo radar to enable the operation of these two systems with minimal mutual interference.Through an asymptotic analysis,we show that by using more antennas at the base station(BS),we can improve the performance of Massive MIMO,while keeping the interference to the radar system unchanged.Additionally,if we use a large number of antennas at the BS and make the transmit power inversely proportional to the number of antennas,we can avoid the interference from the Massive MIMO system to the radar system,with no compromise in the performance of the Massive MIMO system.Closed-form expressions for the probability of detection of the radar system and the downlink spectral efficiency of the Massive MIMO system,are derived.Furthermore,we propose a power allocation scheme which selects the transmit powers at the mimo radar and BS to maximize the probability of detection for the mimo radar.Interestingly,the optimal power allocation can be determined in closed-form.These results provide valuable insights into the practical coexistence between Massive MIMO and radar systems.

关键词： Massive MIMO radar-communication coexistence spectrum sharing