Dynamic Base Station Research Articles

Base Station Energy Saving based on Imitation Learning in 5G Network

Abstract With the rapid development of communication technology, the large-scale deployment of base stations (BSs) has led to an increase in power consumption. To reduce power consumption, energy saving technologies for BSs have emerged, which are in line with the concept of green communications and can save operators’ costs. In this paper, our goal is to minimize the total power consumption of the base station by dynamically controlling the switching status of the base station. This article first proposes a dynamic base station switching framework based on deep reinforcement learning (DRL), which optimizes the power consumption of switching BSs. Then, considering the dynamic nature of the environment, this article proposes a dynamic BS switching algorithm that introduces the idea of imitation learning (IL) to update the policy by learning the expert’s action. The simulation results show that this scheme can effectively reduce power consumption.

Dynamic base station allocation for 6G wireless networks through narrow neural network

The 6G wireless communication system will utilize the terahertz (THz) frequency band (0.1-10 THz) to meet customer demand for increased data rates and ultra-high-speed communication in future applications. The exponential surge in data traffic, which is supported by dynamic resource allocation. To mitigate this challenge, the use of artificial intelligence-based methods, such as narrow neural network (NNN), can help to smooth the performance of the network. In this paper, an NNN-based approach for dynamic base station allocation for 6G wireless networks is proposed 14 different 6G parameters used to train the NNN model, initially achieving an accuracy of 89.5% and an F1 score of 0.72 for 200 users. Results demonstrate the efficacy of the proposed NNN approach for dynamic decision-making in 6G networks and its potential for application in other domains where similar problems exist. Moreover, the proposed narrow neural network model shows improved results with an increase in number of users and decrease in fully connected layers and regularization strength (lambda). The validation accuracy received is 98.9% and 99.6% for thousand users with single fully connected layer, none (linear) activation function and regularization strength lambda values of 0.01 and 0.001.

QoS-driven resource allocation in fog radio access network: A VR service perspective.

While immersive media services represented by virtual reality (VR) are booming, They are facing fundamental challenges, i.e., soaring multimedia applications, large operation costs and scarce spectrum resources. It is difficult to simultaneously address these service challenges in a conventional radio access network (RAN) system. These problems motivated us to explore a quality-of-service (QoS)-driven resource allocation framework from VR service perspective based on the fog radio access network (F-RAN) architecture. We elaborated details of deployment on the caching allocation, dynamic base station (BS) clustering, statistical beamforming and cost strategy under the QoS constraints in the F-RAN architecture. The key solutions aimed to break through the bottleneck of the network design and to deep integrate the network-computing resources from different perspectives of cloud, network, edge, terminal and use of collaboration and integration. Accordingly, we provided a tailored algorithm to solve the corresponding formulation problem. This is the first design of VR services based on caching and statistical beamforming under the F-RAN. A case study provided to demonstrate the advantage of our proposed framework compared with existing schemes. Finally, we concluded the article and discussed possible open research problems.

Joint Multi-Objective Optimization for Radio Access Network Slicing Using Multi-Agent Deep Reinforcement Learning

Radio access network (RAN) slices can provide various customized services for next-generation wireless networks. Thus, multiple performance metrics of different types of RAN slices need to be jointly optimized. However, existing efforts in multi-objective optimization problem (MOOP) for RAN slicing are only in the scalar form, which is difficult to achieve simultaneous optimization. In this paper, we consider a non-scalar MOOP for RAN slicing with three types of slices, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , the high-bandwidth slice, the low-delay slice, and the wide-coverage slice over the same underlying physical network. We jointly optimize the throughput, the transmission delay, and the coverage area by user-oriented dynamic virtual base stations (vBSs)' deployment, and sub-channel and power allocation. An improved multi-agent deep deterministic policy gradient (IMADDPG) algorithm, having the characteristics of centralized training and distributed execution, is proposed to solve the above non-deterministic polynomial-time hard (NP-hard) problem. The rank voting method is introduced in the inference process to obtain near-Pareto optimal solutions. Simulation results verify that the proposed scheme can ensure better performance than the traditional scalar utility method and other benchmark algorithms. The proposed scheme has the advantage of flexibly approaching any point of the Pareto boundary, while the traditional scalar method only subjectively approaches one of the Pareto optimal solutions. Furthermore, our proposal strikes a compelling tradeoff among three types of RAN slices due to the non-dominance between Pareto optimal solutions.

Ground-to-Air Communications Beyond 5G: A Coordinated Multipoint Transmission Based on Poisson-Delaunay Triangulation

This paper designs a novel ground-to-air communication scheme to serve unmanned aerial vehicles (UAVs) through legacy terrestrial base stations (BSs). In particular, a tractable coordinated multi-point (CoMP) transmission based on the geometry of Poisson-Delaunay triangulation is developed, which provides reliable and seamless connectivity for UAVs. An effective dynamic frequency allocation scheme is designed to eliminate inter-cell interference by using the theory of circle packing. For exact performance evaluation, the handoff probability of a typical UAV is characterized, and then the coverage probability with handoffs is attained. Simulation and numerical results corroborate that the proposed scheme outperforms the conventional CoMP scheme with three nearest cooperating BSs in terms of handoff and coverage probabilities. Moreover, as each UAV has a fixed and unique CoMP BS set, it avoids the real-time dynamic BS searching process, thus reducing the feedback overhead.

Energy Aware Drone Enabled Base Stations Deployment in Flying Adhoc Networks

The demand for Wireless Networks has increased rapidly for downloading and uploading all types of data, which is due to the inefficiency and high cost of infrastructure based cellular Networks. Unmanned Aerial Vehicle (UAVs) is the reliable solution for improving efficiency, latency redundancy, business, and troubleshooting problems on a high scale. Although, the deployment of UAVs to meet user demand in any environment or region is quite thought-provoking. This deployment not merely provides the only the coverage area, but also provides the capacity balancing and accommodation of extremely populated areas. In this research, the suggested solutions show significant achievements in terms of energy proficiency related to the fixed and dynamic drone base station system. We propose FSO (free space optical) communication system and evaluate three feasible algorithms for separate dynamic repositioning of drone base stations in terms of communicational energy. In MATLAB, the simulation result shows 20 percent of communicational energy consumption improvement can be obtained deprived of adversely affecting any other factor of the drone.

Dynamic base stations selection method for passive location based on GDOP.

For the problem of passive location in mobile cellular network, base stations (BSs) selection can improve positioning accuracy. Through the analysis of base station layout in cellular networks, using Geometric Dilution of Precision (GDOP) as the optimization objective, we propose a Dynamic Base Stations Selection (DBSS) method in a cellular unit. This method enables the system to dynamically select the positioning base station when positioning target in the detection area. DBSS mainly include three steps: nearest base station calculation, layout of base stations analysis, and base station selection based on the target location. We mainly focus on the derivation of four-base station dynamic selection (DBSS4) and five-base station dynamic selection (DBSS5) algorithms. In simulation experiments, DBSS4 algorithm and DBSS5algorithm were compared with the state-of-the-art of BSs selection methods. The results show that our proposed method can achieve the exhaustive search in cellular cells and reduce more than 20% of the GDOP cumulative positioning error compared with the fixed four-base station selection algorithm. Meanwhile, the proposed method is more efficient, requires less running time and floating-point operations (FLOPs) than other comparison algorithm, and is independent of localization algorithms.

Energy minimization by dynamic base station switching in heterogeneous cellular network

Energy minimization by dynamic base station switching in heterogeneous cellular network

User-Centric Cluster Design and Analysis for Hybrid Sub-6GHz-mmWave-THz Dense Networks

The terahertz (THz) waves with enormous bandwidth can be used along with the existing sub-6GHz and millimeter wave (mmWave) bands to achieve the ever evolving ecosystem of applications that need to be supported by the modern wireless networks. This paper investigates a user-centric dynamic base station (BS) clustering design for a hybrid network where THz, mmWave, and sub-6GHz BSs coexist. Invoking the proposed clustering model, the BS cooperation within each band is made possible by considering long term channel variations and all the surrounding BSs within a cluster with tunable size. A typical user is associated with the best BS cluster, from either a sub-6GHz, mmWave or THz tier based on the maximum signal-to-interference-plus-noise-ratio (SINR) criterion or the maximum rate criterion. Using tools from stochastic geometry, we assess the performance of the proposed user-centric hybrid system in terms of SINR and rate coverage performances, while accounting for: band specific propagation models, directional beamfroming, and BSs random locations. The accuracy of the analytical results is validated with Monte-Carlo simulations. The obtained results recognize a clear coverage/rate trade-off where a high fraction of THz BSs improves the rate significantly but may degrade the coverage performance. Thus, with carefully planned networks, enabling user-centric BS cooperation for hybrid wireless systems can achieve ultra-high rates while maintaining sufficient coverage in sixth-generation (6G) networks.

Rate Splitting Multiple Access for Multigroup Multicast Beamforming in Cache-Enabled C-RAN

This paper investigates rate splitting multiple access (RSMA) for multigroup multicast beamforming in a cache-enabled cloud radio access network (C <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> -RAN). To maximize the minimum weighted rate among all users and ensure the base station (BS) transmit power and backhaul capacity constraints, we jointly optimize the dynamic BS clustering, message splitting, and beamforming vectors. More specifically, users requesting the same content are collected to a multicast group and served by a cluster of BSs. Each BS has limited storage space and connects to the central processor (CP) via a backhaul link. However, the resulting optimization problem is discrete and nonconvex and has a non-smooth objective function. To alleviate these problems, we construct two surrogates to approximate the objective function locally. These surrogates are strictly lower bounds and concave. Also, we build an upper-bound convex surrogate function to handle the backhaul cost constraint and derive a minorization-maximization (MM)-based iterative algorithm. Besides, we adopt a quadratic transform method to recast the achievable rate and binary BS selection problems to a more tractable form to reduce time cost. We transform the resulting problem into a two-tier alternating optimization problem; the outer-tier problem is handled with closed-form expressions while the inner-tier one is solved by a convex framework. We thus develop a quadratic transform-based alternating (QTA) algorithm. Extensive simulation results confirm the superiority of our proposed transmit strategy and algorithms.

Blimp-Borne Laser Communication Technology Based on Space Dynamic Base Station

Space dynamic communication base stations are characterized by high mobility, flexible deployment and wide coverage. It has a wide range of needs in the fields of emergency communication, earthquake relief and island communication. To solve the problem of a limited number of users due to the limited bandwidth of space base stations, so we should improve the reliability and efficiency of data transmission between space mobile base stations. According to the characteristics of the airship platform, a blimp-borne laser communication terminal applied to space dynamic base stations is designed to meet the demand for bi-directional transmission of 5G signal laser carriers in space between airships. The experimental results show that the blimp-borne laser communication terminal achieves an overall technical index of capture probability is better than 98%, the average capture time is 5s, the tracking accuracy is better than 6–15 μrad, the communication rate is 2.5 Gbps and BER is 1E-7. In order to realize the real-time evaluation of the performance of laser communication system under the turbulent channel and guide the research of anti-turbulence fading method, a real-time test method of communication channel turbulence characteristics based on the principle of light intensity scintillation is proposed. And the real-time test of turbulence characteristics of airship inclined link and the horizontal link is completed. The research results play a positive role in promoting the development of 5G mobile technology and space optical communication technology.

Development of functionalities extension approach and implementation of address routing for IFogSim based simulators

The object of research is an approach of functionality extension for simulation toolkits based on iFogSim. It is assumed by the native approach that enhancement of functionalities should be achieved by inheriting the fog device class and defining new features in its body. However, this approach makes it impossible to use inherited simulators together and significantly decreases flexibility even when utilizing a single simulator. Another problem related exclusively to iFogSim is a specific communication scheme between application modules, which results in data routing limitations in fog architectures and odd data streams taken into account. This paper introduces an alternative extension approach incorporating a peculiar inheritance scheme which tries to reconsider the standard approach from a behavioral design patterns point of view. The key feature of the suggested approach is an extraction of fog device features from the native class into separate behavioral classes. Meanwhile, the designed inheritance scheme allows to flexibly override and combine behaviors. According to the approach principles the developed simulator extends iFogSim with application modules addressing capabilities solving limitations, along with implementing users’ mobility and dynamic wireless connectivity as it is done in MobFogSim. With the aim to check its correctness, the designed toolkit was validated with the standard for iFogSim case study of «EEG Tractor Beam game» application. The validation included four scenarios. In the first two scenarios the features of users’ mobility and dynamic base station connectivity were validated. And in the next scenarios that utilized address routing the obtained delay and network usage values were compared with theoretically calculated ones. The validation results indicated the correct simulator behavior, and introduced functionalities extension approach, being more complex in comparison with the inative one, can significantly improve flexibility of the simulator

Dynamic Base Station Clustering in User-Centric mmWave Networks: Performance Analysis and Optimization

In millimeter wave (mmWave) networks, base stations (BSs) are expected to be densely deployed in order to meet the demands of mobile users. A major challenge in dense mmWave networks is the interference experienced by the user from the neighboring BSs which limits the density of deployed BSs. A promising solution to this challenge is to adopt user-centric BS cooperation that allows a user to be connected to a set (cluster) of BSs instead of being associated with a single one. In this paper, we investigate the performance of user-centric mmWave networks with dynamic clustering. First, we propose a dynamic clustering model to enable the user-centric BS cooperation. Then, based on the proposed model, we analyze the coverage probability and average spectral efficiency (ASE) performances using stochastic geometry tools. We also propose a BS clustering optimization framework to achieve maximum ASE performance for given network configuration. Finally, numerical and simulation results are provided, showing that the proposed dynamic clustering schemes always outperform static clustering and single-BS selection schemes and yield an optimum system performance for given network parameters.

Deep Reinforcement Learning With Spatio-Temporal Traffic Forecasting for Data-Driven Base Station Sleep Control

To meet the ever increasing mobile traffic demand in 5G era, base stations (BSs) have been densely deployed in radio access networks (RANs) to increase the network coverage and capacity. However, as the high density of BSs is designed to accommodate peak traffic, it would consume an unnecessarily large amount of energy if BSs are on during off-peak time. To save the energy consumption of cellular networks, an effective way is to deactivate some idle base stations that do not serve any traffic demand. In this paper, we develop a traffic-aware dynamic BS sleep control framework, named DeepBSC, which presents a novel data-driven learning approach to determine the BS active/sleep modes while meeting lower energy consumption and satisfactory Quality of Service (QoS) requirements. Specifically, the traffic demands are predicted by the proposed GS-STN model, which leverages the geographical and semantic spatial-temporal correlations of mobile traffic. With accurate mobile traffic forecasting, the BS sleep control problem is cast as a Markov Decision Process that is solved by Actor-Critic reinforcement learning methods. To reduce the variance of cost estimation in the dynamic environment, we propose a benchmark transformation method that provides robust performance indicator for policy update. To expedite the training process, we adopt a Deep Deterministic Policy Gradient (DDPG) approach, together with an explorer network, which can strengthen the exploration further. Extensive experiments with a real-world dataset corroborate that our proposed framework significantly outperforms the existing methods.

Dynamic Aerial Base Station Placement for Minimum-Delay Communications

Queuing delay is of essential importance in the Internet-of-Things scenarios where the buffer sizes of devices are limited. The existing cross-layer research contributions aiming at minimizing the queuing delay usually rely on either transmit power control or dynamic spectrum allocation. Bearing in mind that the transmission throughput is dependent on the distance between the transmitter and the receiver, in this context we exploit the agility of the unmanned-aerial-vehicle (UAV)-mounted base stations (BSs) for proactively adjusting the aerial BS (ABS)’s placement in accordance with wireless teletraffic dynamics. Specifically, we formulate a minimum-delay ABS placement problem for UAV-enabled networks, subject to realistic constraints on the ABS’s battery life and velocity. Its solutions are technically realized under three different assumptions in regard to the wireless teletraffic dynamics. The backward induction technique is invoked for both the scenario where the full knowledge of the wireless teletraffic dynamics is available, and for the case where only their statistical knowledge is available. In contrast, a reinforcement learning aided approach is invoked for the case when neither the exact number of arriving packets nor that of their statistical knowledge is available. The numerical results demonstrate that our proposed algorithms are capable of improving the system’s performance compared to the benchmark schemes in terms of both the average delay and of the buffer overflow probability.

3-D Dynamic UAV Base Station Location Problem

We address a dynamic covering location problem of an unmanned aerial vehicle base station (UAV-BS), in which the location sequence of a single UAV-BS in a wireless communication network is determined to satisfy data demand arising from ground users. This problem is especially relevant in the context of smart grid and disaster relief. The vertical movement ability of the UAV-BS and nonconvex covering functions in wireless communication restrict utilizing classical planar covering location approaches. Therefore, we develop new formulations to this emerging problem for a finite time horizon to maximize the total coverage. In particular, we develop a mixed-integer nonlinear programming formulation that is nonconvex in nature and propose a Lagrangean decomposition algorithm (LDA) to solve this formulation. Because of the high complexity of the problem, the LDA is still unable to find good local solutions to large-scale problems. Therefore, we develop a continuum approximation (CA) model and show that CA would be a promising approach in terms of both computational time and solution accuracy. Our numerical study also shows that the CA model can be a remedy to build efficient initial solutions for exact solution algorithms. Summary of Contribution: This paper addresses a facet of mixed integer nonlinear programming formulations. Dynamic facility location problems (DFLPs) arise in a wide range of applications. However, classical DFLPs typically focus on the two-dimensional spaces. Emerging technologies in wireless communication and some other promising application areas, such as smart grids, have brought new location problems that cannot be solved with classical approaches. For practical reasons, many research attempts to solve this new problem, especially by researchers whose primary research area is not OR, have seemed far from analyzing the characteristics of the formulations. Rather, solution-oriented greedy heuristics have been proposed. This paper has two main objectives: (i) to close the gap between practical and theoretical sides of this new problem with the help of current knowledge that OR possesses to solve facility location problems and (ii) to support the findings with an exhaustive computational study to show how these findings can be applied to practice.

Enhanced QoS and QoE Support through Energy Efficient Handover Algorithm for UMTS Architectures

Spectrum inefficiency is a significant issue because of the increasingly more solicitation of transmission capacity by the end clients. Accomplishing high transmission rates and elevated levels of Quality of Service (QoS) speaks to in any case an open issue. Long haul Evolution (LTE) has been proposed as the reason for the fourth era versatile cell systems (4G) that points of the LTE standard are higher client bit rates, lower delays, expanded range proficiency, diminished expense, and operational effortlessness. In any case, this innovation is as yet being worked on and a few open issues must be still explored, for example, obstruction coordination, and power utilization, assets the board and handover procedures. The point of this work is to ensure the decrease of intensity utilization utilizing another handover calculation dependent on green arrangement. Also, the proposed conspire ensures the minimization of superfluous handovers. In any case, the issue in this technique is doesn't mulls over of the impact of green strategies in high force hubs. Along these lines, in the proposed framework the impact of high force hubs are thought of. This situation is expanded more than one full scale cell and various kinds of low force hubs, for example, pico-cell and microcells. In this situation, the force sparing is broke down during the handover strategies. Think about these issues; another strategy is proposed called Dynamic base Station arranging (DBSP) for accomplishing vitality productivity. The fundamental idea is to kill a BS individually that will insignificantly influence the system by utilizing a recently presented thought of system sway, which considers the extra burden increases brought to its neighboring BSs. So as to additionally diminish the flagging and execution overhead over the air and back take, utilize the estimated estimations of system sway as their choice measurements. A trial result shows that the proposed strategy accomplishes high vitality proficiency under different situations. In this work the presentation of an UMTS organize situation is assessed by utilizing different estimations of the priority bits of the CBR application.

Energy-efficient 5G cloud RAN with virtual BBU server consolidation and base station sleeping

Heterogeneous network (HetNet) deployment where macro cells are overlaid by small cells is considered a de-facto solution for meeting the ever increasing mobile traffic demand in fifth generation (5G) networks. However, deployment of a large number of small cell base stations (BSs) result in considerable increase in HetNet energy consumption. Cloud radio access networks (C-RAN) has been proposed as an energy-efficient architecture that leverages cloud computing technology where baseband processing is performed in virtual baseband units (vBBU) in the BS cloud. In this paper, we address the energy efficiency (EE) optimization problem in the downlink for two-tier heterogeneous C-RAN (H-CRAN) comprising of macro and pico-cells. At the radio side of H-CRAN, a dynamic pico BS switching OFF algorithm based on a utility function is proposed while maintaining coverage and quality of service (QoS). In the cloud side, heuristic approximation algorithms are proposed including simulated annealing (H-CRAN SA) and genetic algorithm (H-CRAN GA) to minimize energy consumption by reducing the number of BBU servers used through vBBU placement. The proposed scheme is compared with distributed long term evolution advanced (LTE-A) Hetnet system and simulation results show that the proposed H-CRAN SA and H-CRAN GA schemes save 48% and 45% of energy on a daily average, respectively while maintaining the required QoS.

A novel approach of dynamic base station switching strategy based on Markov decision process for interference alignment in VANETs

In Vehicular Ad-hoc Networks (VANETs), high frequent interaction of safety-related information is required among vehicles and imposes urgent demand on information update. In order to reduce communication delay and improve the capacity of concurrent communication, in this paper we propose that the multi-antenna vehicle could takeover the channel management as dynamic base station to apply Multiple-Input Multiple-Output communication and interference management approach in Vehicle to Vehicle (V2V) communications. Firstly, we construct an Markov decision process (MDP) model for multi-antenna vehicle to estimate whether it is appropriate to be dynamic base station. In addiction, Monte Carlo Tree Search algorithm is introduced to derive MDP policy. Thirdly, the V2V Interference Alignment (V2V-IA) model is constructed for dynamic base station to obtain IA scheme to manage V2V communications and IA in VANETs. To achieve the goal of improving frequency of information update, we propose an optimized problem to minimize total number of time slots, which is required for completing global safety-related information delivery. Simulation results show that the frequency of information update can be improved effectively by the proposed approach and the average improvement could go up to 40%.

Max–min fairness driven multicast sparse beamforming for cache-enabled Cloud RAN

Cloud radio access network (C-RAN) is able to boost network capacity through centralized scheduling of multiple distributed base stations (BSs). Yet it also places serious burdens on backhaul links. Caching and multicast are two enabling candidates to alleviate backhaul cost and achieve efficient content delivery. In this paper, we consider a cache-enabled multicast C-RAN, where multiple BSs cooperatively serve multiple user groups. Each BS is equipped with local storage and is connected to the central processor (CP) via a backhaul link. Then, the beamforming vectors and dynamic BS clustering is carefully designed to maximize fairness among users. To achieve such a goal, a semidefinite relaxation (SDR)-based iterative difference-of-two-convex-function (D.C.) algorithm is proposed by using semidefinite programming (SDP) and smoothed l0-norm approximation approaches. After that, to reduce computational complexity, a two-tier convex quadratic-based alternating (TCQA) algorithm is devised by decoupling multi-ratio fractional constraints. In this algorithm, the outer problem is handled in closed-form expressions while the inner one is solved by using D.C. programming. Convergence and complexity of the two proposed algorithms are analyzed. Finally, extensive simulation results demonstrate that our proposed TCQA algorithm significantly outperforms the SDR-based iterative D.C. algorithm.