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DECCO: Deer-Learning Enabled coverage and capacity optimization for Massive MIMI Systems

IEEE ACCESS 2018年 6卷 23361-23371页

作者： Yang, Yang Li, Yang Li, Kai Zhao, Shuang Chen, Rui Wang, Jun Ci, Song Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Key Lab Wireless Sensor Network & Commun Shanghai 200050 Peoples R China ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Xidian Univ State Key Lab ISN Xian 710071 Shaanxi Peoples R China UCL Dept Comp Sci London WC1E 6BT England Univ Nebraska Dept Elect & Comp Engn Lincoln NE 68588 USA

System capacity and service coverage are the most critical performance metrics in cellular wireless communication networks. Usually, system capacity enhancements are at the expense of service coverage degradations, and vice versa. This capacity-coverage tradeoff and the associated joint optimization problem becomes very challenging in massive multiple-input multiple-output (MIMO) wireless systems, due to a large amount of antenna tilt values to be configured and very sophisticated inter-cell interference conditions, under massive antenna scenarios. This paper proposes a novel approach, namely group alignment of user signal strength (GAUSS), to efficiently support the user scheduling for the massive MIMO system, and thus serve as an effective parameter for the coverage and capacity optimization (CCO) problem. Together with a unified threshold of Quality of Service, i.e. the minimum signal-to-interference-plus-noise ratio (SINRmin) for user satisfaction, GAUSS can effectively control the variance of signal strengths of multiple users in the neighborhood. Moreover, an intelligent and efficient deep-learning enabled coverage and capacity optimization (DECCO) algorithm is proposed and evaluated, which adopts a pre-trained deep policy gradient-based neural network to dynamically derive GAUSS and SINRmin during CCO. Furthermore, an inter-cell interference coordination (ICIC) is proposed to enhance the CCO performance. Analytical and simulation results show that the proposed DECCO algorithm can effectively achieve a much better performance balance between system capacity and service coverage than traditional fixed optimization (FO) and proportional fair optimization (PFO) algorithms. Specifically, DECCO significantly increases the overall spectrum efficiency by 24% and 40%, respectively, than FO and PFO in a typical massive MIMO system.

关键词： coverage and capacity optimization deep reinforcement learning massive MIMO user scheduling inter-cell interference coordination

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coverage and capacity optimization Analysis in Heterogeneous Cellular Networks Based on Truncated Function

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WIRELESS PERSONAL COMMUNICATIONS 2016年第2期89卷 385-403页

作者： Wang, Xiaojuan Song, Mei Teng, Yinglei Xing, Anqi Yuan, Deyu Beijing Univ Posts & Telecommun Dept Elect Engn Beijing 100876 Peoples R China

This paper presents a framework for coverage and capacity optimization (CCO) in HCNs from stochastic geometry theory. Unlike conventional approaches, we propose a concept of effective capacity as the optimization objective, which involves implicit index of coverage in the form of truncation function. The main idea is to model the coverage probability, average rate and joint objective considering noise where the location of base stations are spatially distributed as a Poisson Point Process. Under an adaptive power allocation assumption, we improve iterative approach with percentile level to find an effective increasing direction of transmit power. The simulation shows that the theoretical analysis and optimization objective are feasible and the adjustment of percentile level serves as a flexible solution for CCO.

关键词： Heterogeneous cellular networks coverage and capacity optimization Effective capacity Poisson point process

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coverage and capacity optimization in Cellular Radio Networks with Advanced Antennas

Coverage and Capacity Optimization in Cellular Radio Network...

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WSA 2015;19th International ITG Workshop on Smart Antennas

作者： Maciej Soszka Sascha Berger Albrecht Fehske Meryem Simsek Bohdan Butkiewicz Gerhard Fettweis Vodafone Chair Mobile Communications Systems Airrays GmbH Institute of Electronic Systems Warsaw University of Technology

We are facing a highly complex optimization problem when using 2-dimensional antenna arrays for concurrent coverage and capacity optimization, because at each single array a multitude of phases needs to be set in order to achieve best performance. Considering an array consisting of 8 x 8 elements and 10 different possible phase values we already have 10(exp 64) possible settings. However, managing this complexity is desirable since the multitude of phase settings available leads to a high degree of freedom when shaping the antenna beam, which in turn, is expected to enhance coverage and capacity performance. We propose a solution technique that reduces the complexity of the problem while still achieving adequate performance gains. We achieve the complexity reduction by considering only a limited set of basic beams which we flexibly combine with each other in order to obtain the overall beam pattern of the actual transmission. We propose a Nelder-Mead and a Q-Learningbased approach in order to adjust the remaining parameters, i. e. vertical and horizontal angles of every basic beam. Based on system level simulations we can conclude that 2-dimensional antenna arrays can significantly outperform linear arrays when carrying out concurrent coverage and capacity optimization.

关键词： Self-Organizing Networks coverage and capacity optimization Advanced Antennas Active Antennas Nelder-Mead Q-Learning Reinforcement Learning coverage active antennas Self-organizing networks Antenna arrays Degree of freedom beam mode horizontal angles learning (artificial intelligence) Learning ANTENNA cellular radio linear arrays

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Joint Uplink - Downlink capacity and coverage optimization via Site-Specific Learning of Antenna Settings

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第5期23卷 4032-4048页

作者： Tekgul, Ezgi Novlan, Thomas Akoum, Salam Andrews, Jeffrey G. Univ Texas Austin Wireless Networking & Commun Grp 6GUT Austin TX 78712 USA Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA AT&T Labs Austin TX 78759 USA

We propose a novel framework for optimizing antenna parameter settings in a heterogeneous cellular network. We formulate an optimization problem for both coverage and capacity- in both the downlink (DL) and uplink (UL)- which configures the tilt angle, vertical half-power beamwidth (HPBW), and horizontal HPBW of each cell's antenna array across the network. The novel data-driven framework proposed for this nonconvex problem, inspired by Bayesian optimization (BO) and differential evolution algorithms, is sample-efficient and converges quickly, while being scalable to large networks. By jointly optimizing DL and UL performance, we take into account the different signal power and interference characteristics of these two links, allowing a graceful trade-off between coverage and capacity in each one. Our experiments on a state-of-the-art 5G NR cellular system-level simulator developed by AT&T Labs show that the proposed algorithm consistently and significantly outperforms the 3GPP default settings, random search, and conventional BO. In one realistic setting, and compared to conventional BO, our approach increases the average sum-log-rate by over 60% while decreasing the outage probability by over 80%. Compared to the 3GPP default settings, the gains from our approach are considerably larger. The results also indicate that the practically important combination of DL throughput and UL coverage can be greatly improved by joint UL-DL optimization.

关键词： coverage and capacity optimization Bayesian optimization evolutionary computing machine learning antenna parameter optimization uplink (UL) downlink (DL)

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A Reinforcement Learning Approach for Self-optimization of coverage and capacity in Heterogeneous Cellular Networks

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IEICE TRANSACTIONS ON COMMUNICATIONS 2021年第10期E104B卷 1318-1327页

作者： Wang, Junxuan Yu, Meng Zhang, Xuewei Jiang, Fan Xian Univ Post & Telecommun Sch Commun & Informat Engn Xian 710121 Peoples R China

Heterogeneous networks (HetNets) are emerging as an inevitable method to tackle the capacity crunch of the cellular networks. Due to the complicated network environment and a large number of configured parameters, coverage and capacity optimization (CCO) is a challenging issue in heterogeneous cellular networks. By combining the self-optimizing algorithm for radio frequency (RF) parameters with the power control mechanism of small cells, the CCO problem of self-organizing network is addressed in this paper. First, the optimization of RF parameters is solved based on reinforcement learning (RL), where the base station is modeled as an agent that can learn effective strategies to control the tunable parameters by interacting with the surrounding environment. Second, the small cell can autonomously change the state of wireless transmission by comparing its distance from the user equipment with the virtual cell size. Simulation results show that the proposed algorithm can achieve better performance on user throughput compared to different conventional methods.

关键词： coverage and capacity optimization self-organizing networks radio frequency reinforcement learning heterogeneous cellular networks

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coverage and capacity optimization in Heterogeneous Networks (HetNets): A Green Approach

Coverage and Capacity Optimization in Heterogeneous Networks...

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ISWCS 2013;The Tenth International Symposium on Wireless Communication Systems

作者： Dimitrios Karvounas Panagiotis Vlacheas Andreas Georgakopoulos Marios Logothetis Vera Stavroulaki Kostas Tsagkaris Panagiotis Demestichas Department of Digital Systems University of Piraeus Piraeus Greece

This paper considers a coverage and capacity optimization problem in heterogeneous networks (HetNets), where small cells are deployed within the area of a macro base station. The small cells are configured to the optimal power level in order to maximize the users' throughput without causing interference to the other users and simultaneously taking into account the network's energy consumption by switching off redundant small cells. The problem is formulated and solved in terms of a complex optimization problem through the IBM ILOG CPLEX optimization Studio. Two cases are studied, firstly by taking into account only network performance (PRF case) and then with the introduction of green aspects (EE case). The evaluation proves that the power consumption is reduced by 33 % in the energy efficient case, while the average user throughput is reduced by 18 %.

关键词： Heterogeneous networks coverage and capacity optimization Energy efficiency Resource allocation

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A Differentiable Throughput Model for Load-Aware Cellular Network optimization Through Gradient Descent

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IEEE ACCESS 2024年 12卷 14547-14562页

作者： Eller, Lukas Svoboda, Philipp Rupp, Markus TU Wien Inst Telecommun A-1040 Vienna Austria TU Wien Inst Telecommun Christian Doppler Lab Digital Twin Assisted AI Sus A-1040 Vienna Austria

The efficient operation of cellular networks requires careful tuning of configuration parameters, such as the transmit power or antenna tilts, to adequately balance interference while providing the necessary capacity to the connected UEs. As manual tuning of these parameters is typically unfeasible, several automated coverage and capacity optimization methods have been proposed. However, most existing solutions are either based on poorly scalable black-box optimization methods or solely consider interference management, while omitting the potential of congested cells. In this work, we instead propose a differentiable framework for cellular network optimization, centered around the end-user throughput, that enables load-aware tuning of network parameters through gradient descent. Hereby, we approach the problem from a data-driven perspective, and include dedicated model subcomponents derived from monitoring data, which enable the calibration to site-specific traffic patterns and KPI measurements. We validate our approach for joint transmit power optimization in a real-world network layout with approximate to 150 cells in two frequency bands. In our evaluation, the gradient descent-based optimization reliably reduces the outage ratio for different levels of demand, while the black-box baseline struggles to explore the large search space. Our results further reveal substantial differences between the proposed load-aware and commonly used SINR-based objectives, for which we repeatably obtain unbalanced network configurations with severely congested cells. In contrast, the proposed end-user throughput objective promotes a balanced network configuration, providing adequate resources to the connected UEs while also limiting interference.

关键词： coverage and capacity optimization cellular network optimization gradient descent radio planning load balancing monitoring data minimization of drive tests 4G 5G 6G wireless networks

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Optimizing Wireless coverage and capacity with PPO-Based Adaptive Antenna Configuration 59

Optimizing Wireless Coverage and Capacity with PPO-Based Ada...

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59th Annual IEEE International Conference on Communications (IEEE ICC)

作者： Gu, Yingshuo Chai, Shuqi Sun, Bowen Chen, Yi Shi, Qingjiang Chinese Univ Hong Kong Sch Sci & Engn Shenzhen Peoples R China Shenzhen Res Inst Big Data Shenzhen Peoples R China Tongji Univ Sch Software Engn Shanghai Peoples R China

ISBN: (纸本)9781728190549

Optimizing antenna parameters like azimuth, down-tilt, and power is crucial for coverage and capacity optimization (CCO) in next-generation wireless networks. However, traditional expert knowledge-based methods struggle to maintain optimal results when faced with changing environments. To address this, we propose a guided deep reinforcement learning (DRL) algorithm that learns a policy to dynamically adjust antenna parameters based on the evolving environment. Our approach employs proximal policy optimization-based DRL and integrates a problem-specific pretraining process using zero-order gradient descent. The pretrain policy serves as a guiding policy, enabling the agent to explore and discover high-reward regions, thus accelerating the learning process. The performance of our solution is validated by numerical experiments conducted on a 5G simulation platform with real-world topological properties. The results show that our approach achieves significantly faster convergence and outperforms baseline methods in terms of CCO performance.

关键词： coverage and capacity optimization Reinforcement learning Wireless Network Configurations

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Two-Step Dynamic Cell optimization Algorithm for HAPS Mobile Communications

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IEEE ACCESS 2022年 10卷 68085-68098页

作者： Shibata, Yohei Takabatake, Wataru Hoshino, Kenji Nagate, Atsushi Ohtsuki, Tomoaki SoftBank Corp Technol Res Lab Koto Ku Tokyo 1350064 Japan Keio Univ Grad Sch Sci & Technol Yokohama Kanagawa 2238522 Japan

High altitude platform stations are attracting much attention as novel mobile communication platforms for ultra-wide coverage areas and disaster-resilient networks. Multi-cell configurations can increase communication capacity in a wide area. Conventional work optimizes the cell configuration for a multi-cell configuration based on a genetic algorithm (GA). This method identifies an optimal cell configuration in terms of spectral efficiency depending on the number of cells under a uniform user distribution. However, user distributions differ depending on location;thus, cell configuration optimization is also required for non-uniform user distributions. The problem is an increased number of parameters because each cell requires different antenna parameters for non-uniform user distribution compared to a uniform user distribution. This increases the difficulty of optimization, even when using a GA in some cases. Therefore, we propose a GA-based two-step cell optimization algorithm that comprises both search space reduction and antenna parameter optimization steps to address this problem. The proposed method employs the concept of co-evolution, i.e., a divide-and-conquer method. The proposed method divides multiple cells into several groups to reduce the number of optimized parameters and optimizes each group in order. In addition, the search space is reduced based on a marginal histogram obtained via optimization with a large step size. Simulation results demonstrate that this technique can reduce the number of combinations compared to the case without sub-area division. In addition, there are cases where the search space reduction further reduces the number of combinations without degrading the sum of the square root of throughput.

关键词： Antennas Satellite broadcasting Genetic algorithms Gain Space vehicles Bandwidth Simulation Co-evolution coverage and capacity optimization genetic algorithm HAPS non-uniform user distribution search space reduction

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Optimizing coverage and capacity in Cellular Networks using Machine Learning

Optimizing Coverage and Capacity in Cellular Networks using ...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Dreifuerst, Ryan M. Daulton, Samuel Qian, Yuchen Varkey, Paul Balandat, Maximilian Kasturia, Sanjay Tomar, Anoop Yazdan, Ali Ponnampalam, Vish Heath, Robert W. Facebook Menlo Pk CA 94025 USA Univ Texas Austin Wireless Networking & Commun Grp Austin TX 78712 USA North Carolina State Univ Dept Elect & Comp Engn Raleigh NC USA

ISBN: (纸本)9781728176055

Wireless cellular networks have many parameters that are normally tuned upon deployment and re-tuned as the network changes. Many operational parameters affect reference signal received power (RSRP), reference signal received quality (RSRQ), signal-to-interference-plus-noise-ratio (SINR), and, ultimately, throughput. In this paper, we develop and compare two approaches for maximizing coverage and minimizing interference by jointly optimizing the transmit power and downtilt (elevation tilt) settings across sectors. To evaluate different parameter configurations offline, we construct a realistic simulation model that captures geographic correlations. Using this model, we evaluate two optimization methods: deep deterministic policy gradient (DDPG), a reinforcement learning (RL) algorithm, and multi-objective Bayesian optimization (BO). Our simulations show that both approaches significantly outperform random search and converge to comparable Pareto frontiers, but that BO converges with two orders of magnitude fewer evaluations than DDPG. Our results suggest that data-driven techniques can effectively self-optimize coverage and capacity in cellular networks.

关键词： coverage and capacity optimization Bayesian optimization machine learning reinforcement learning

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