Base Station Selection Research Articles

Resource Allocation for Cellular Zero-Touch Deterministic Industrial M2M Networks: A Reinforcement Learning-Based Scheme

In this letter, we investigate the resource allocation problem for cellular zero-touch deterministic industrial machine-to-machine networks. We consider a scenario in which multiple self-adaptive industrial machine-to-machine (M2M) networks are deployed underlaying cellular networks to emulate a variety of vertical use cases in future Industry 4.0 with the objective of guaranteeing the determinacy of latency by learning-inspired resource allocation policy. Specifically, in order to figure out the relationship between latency determinacy and the utilization of wireless resources meanwhile modeling the uncertainty of the stochastic environment, we first formulate the resource allocation problem as a Markov game by jointly considering transmission power control, frequency spectrum allocation, and the selection of base stations. Afterward, a random graph-based sparse long short-term memory network is proposed to solve the optimization problem while reducing the computational complexity. Finally, numerical result demonstrates the effectiveness of the proposed scheme.

Site Selection Planning of Urban Base Station

TWith the development of mobile communication technology, the coverage quality of the network began to become an important measure of a regional communication network. With the development of 5G technology, the communication bandwidth is increasing, the coverage of the base station is getting smaller and smaller, and the types and signal coverage characteristics of base stations are also different. Therefore, the problem of site selection and planning of base stations in cities begins to become more prominent. Based on the principle of priority business volume and the cost performance of base station, this paper establishes a set of models to solve the site selection planning problem of urban base station. In short, in view of the base station construction planning problems in cities, this paper establishes the relevant model and algorithm, and gives a solution based on the principle of giving priority to the business volume requirements and the cost of base station construction.

User-BS Selection Strategy Optimization with RSSI-Based Reliability in 5G Wireless Networks

Although fifth-generation (5G) wireless communication can ] support well a high data rate of transmission, issues such as base station (BS) failure and poor BS signals may cause serious interruption problems. This paper studies the user-BS selection strategy with received signal strength indication (RSSI)-based reliability in 5G wireless networks. First, reliability is defined on the basis of the RSSI and failure probability of the BS. The problem is modeled as a selection strategy optimization problem with BS capacity and receiving sensitivity as constraints. Second, the original problem can be transformed into a resource allocation problem with probabilistic constraints. For the situation where user distribution is known, we used dynamic programming to obtain the optimal BS selection strategy. For the situation where user distribution is unknown, starting from user trajectory data, we used the space–time density estimation method based on the Epanechnikov kernel to estimate user density and bring it into dynamic programming to obtain the optimal selection strategy. Simulation results show that our density estimation algorithm is more accurate than the commonly used density estimation algorithm. Compared with the distance-based optimization method, our RSSI-based optimization method also improved the communication signal quality under different scenarios.

Efficient resource allocation in 5G URLLC with packet duplication based macro-diversity

Packet duplication based macro-diversity is a potential solution to achieve high reliability and low latency, for ultra-reliable low-latency communication (URLLC) applications, like mission critical internet of things (IoT), in 5G cellular systems. Increased radio resource usage is the price paid for the benefits accrued. This paper proposes a framework to find the relaxed per link reliability targets and to perform radio resource and power allocation over the links, which can guarantee the user equipment (UE) reliability target and also provide spectrum efficient resource usage, when multiple base stations (BSs) serve each UE. The resource allocation problem that involves BS selection, packet length adaptation, and power allocation, is formulated to minimize the overall resource usage within the constraints of reliability, latency, resource availability, and transmit power. Inherent system dynamics that exist among packet length, decoding error probability, and transmit power are used in devising a lightweight heuristic solution. The two important findings are that: (i) with each UE, a stringent reliability target for the best link and relaxed reliability targets for the other links can provide a spectrum efficient solution; (ii) additional resource savings can be achieved by allocating more power to the UEs with poor channel conditions, at each BS. The proposed method can achieve up to 20% resource saving when compared to the blind duplication with equal power allocation.

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

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

Best base station location with a given area as an example

In the communication infrastructure construction, how to reasonably configure base station type and location according to different traffic volume areas, so as to improve the communication efficiency of base station coverage area and enhance the sharing of resources. The problem of communication bandwidth becomes bigger and bigger, but the coverage of BTS becomes smaller and smaller, which makes the number of BTS needing to cover the same area become more and more. We simplify the type of BTS into macro and micro BTS according to the requirements of the project, and use the CHARACTERISTICS of BTS given in the project to conduct mathematical modeling. In the actual construction process, we adopt effective site selection, which can not only improve the investment efficiency, but also reduce the construction and maintenance cost of base station. Therefore, it is of practical significance to study the site selection of base station. Genetic algorithm is used to analyze the optimal solution in simulation space and find the optimal method of station construction. The simulation software is used to simulate the coverage of the base station and verify the rationality of the site selection scheme.

Solution to improve the signal coverage intensity under cluster analysis

In the construction of communication infrastructure, how to reasonably allocate base station types and base station locations according to different business volume in different regions, so as to improve the communication efficiency of the base station coverage area and improve the sharing of resources. In view of the problem that the communication bandwidth is getting larger and larger, but the energy coverage of the base station is getting smaller and smaller, making the number of required base stations to cover the same area, we simplify the types to macro station and micro base station according to the requirements of the topic, and use the base station characteristics given by the problem to conduct mathematical modeling. When considering the different business volume in different regions, priority is given to solving the areas with high business volume and weak coverage. Our use of effective site selection work can not only improve the investment efficiency, but also reduce the construction and maintenance cost of base stations in the actual construction process. Therefore, it is a practical significance to study the site selection of base station. Conduct an example simulation. After the simulation software simulates the grille, the clustering algorithm merges all the weak coverage areas into one group, namely, the similar data is merged, and then the base station is optimized to effectively improve its coverage.

Research on Site Planning of Mobile Communication Network

With the development of 5G technology, network communication has become the most commonly used tool in People’s Daily life and production. Due to the huge user base, the problem of site selection of communication network has become more and more complex. This paper mainly studies the problem of mobile communication network site planning, using 0 - 1 planning knapsack model, heuristic algorithm, simulated annealing algorithm and other methods, combined with MATLAB software to effectively solve the location of site coordinates and the type of base station selection, give the optimal results of site selection. It has certain guiding significance to mobile communication network site planning.

Association and Caching in Relay-Assisted mmWave Networks: A Stochastic Geometry Perspective

Limited backhaul bandwidth and blockage effects are two main factors limiting the practical deployment of millimeter wave (mmWave) networks. To tackle these issues, we study the feasibility of relaying as well as caching in mmWave networks. A user association and relaying (UAR) criterion dependent on both caching status and maximum biased received power is proposed by considering the spatial correlation caused by the coexistence of base stations (BSs) and relay nodes (RNs). Using stochastic geometry tools, we decouple the UAR and caching placement issues by analyzing the relationship between UAR probabilities and caching placement probabilities. We then optimize the formulated caching placement problem based on polyblock outer approximation by exploiting the monotonic property in the general case and utilizing convex optimization in the noise-limited case. Accordingly, we propose a BS and RN selection algorithm where caching status at BSs and maximum biased received power are jointly considered. Experimental results demonstrate a significant enhancement of backhaul offloading using the proposed algorithms, and show that deploying more RNs and increasing cache size in mmWave networks is a more cost-effective alternative than increasing BS density to achieve similar backhaul offloading performance.

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.

New approach based on direction and genetic algorithm to predict target base station in mobile WiMAX

The IEEE 802.16e [Mobile Worldwide Interoperability for Microwave Access (MWiMAX)] standard allows Subscriber Stations (SS) to move, transforming them into Mobile Stations (MS). This brings up one of IEEE 802.16e's most difficult issues: handover (HO). When MS moves from one cell to the next, HO occurs. Reducing HO delay is critical for improving service quality. This paper proposes method to reduce delay by two steps. The first, serving base station eliminate BSs that are not in the direction of the user’s movement. Second using genetic algorithm (GA) to optimize the selection of target Base station (TBS) depending on speed of user so that it ensures balance between reducing the scanning time and dropping call. Finally, MATLAB was utilized to obtain a result that indicates a lower TBS and higher throughput during handover than the traditional approach.

Research on TD-LTE wireless communication network propagation model optimization based on visual simulation and\xa0GIS

With the support of GIS spatial analysis technology, based on an in-depth study of the wireless propagation environment of a city, combined with the analysis of project requirements, it proposes to use the SPM model to correct the propagation model parameters, using SPM. The wireless propagation model, and research and analysis of the SPM wireless propagation model correction algorithm, further corrected the parameters of a city's SPM wireless propagation model. On this basis, the propagation loss of several classic propagation models in different environments is compared, and the SPM propagation model suitable for the signal frequency band and propagation environment of this study is selected. The correction of the SPM propagation model is based on the designed correction principle and correction process, that is, the weighted least square method is used to fit and analyze the measured level data to obtain an SPM prediction improvement model with local characteristics, and according to the designed verification link. Evaluation of the correction results shows that the accuracy requirements are met. Based on the corrected SPM prediction model, link loss calculations were performed on the 13 test base stations studied in the experiment, and the effective coverage radius of each base station community was obtained. In combination with GIS technology, model parameters and workers of each base station participated in the electronic map loading of the area Go to the network planning software to get the wireless signal coverage prediction map of each base station. Finally, according to the technical requirements of the TD-LTE system network planning and network optimization engineering, the objectiveness and rationality of the site selection and number of base stations in the area were verified, and specific problems regarding poor coverage and overlapping coverage in the area were proposed.

Deep Q-Network based resource allocation for UAV-assisted Ultra-Dense Networks

With the rapid development of the fifth-generation (5G) wireless communications, the number of users is increasing dramatically and Ultra-Dense Networks (UDN) are becoming more important for supporting numerous users and emerging mission-critical applications. In order to conquer the communication restrictions caused by natural disasters, an emergency communication system using Unmanned Aerial Vehicles (UAV) as a flying base station (BS) to assist UDN is proposed. By virtue of the resource allocation scheme of UAV-assisted UDN systems, communication resources can be reasonably and effectively allocated to improve the quality of user experience. Firstly, aiming to maximize the system energy efficiency (EE), a UDN system model including the BS selection is constructed. Secondly, Markov Decision Process (MDP) theory is applied to transform the system model into a stochastic optimization problem. Finally, by using deep reinforcement learning (DRL) technique, we propose a Deep Q-Network (DQN) based resource allocation scheme to maximize the system energy efficiency. Simulation results exhibit that the proposed DQN-based resource allocation scheme can significantly improve the system EE compared with the legacy Q-Learning, random and maximum resource allocation algorithms.

Computation Offloading in Heterogeneous Vehicular Edge Networks: On-Line and Off-Policy Bandit Solutions

With the rapid advancement of Intelligent Transportation Systems (ITS) and vehicular communications, Vehicular Edge Computing (VEC) is emerging as a promising technology to support low-latency ITS applications and services. In this paper, we consider the computation offloading problem from mobile vehicles/users in a heterogeneous VEC scenario, and focus on the network- and base station selection problems, where different networks have different traffic loads. In a fast-varying vehicular environment, computation offloading experience of users is strongly affected by the latency due to the congestion at the edge computing servers co-located with the base stations. However, as a result of the non-stationary property of such an environment and also information shortage, predicting this congestion is an involved task. To address this challenge, we propose an on-line learning algorithm and an off-policy learning algorithm based on multi-armed bandit theory. To dynamically select the least congested network in a piece-wise stationary environment, these algorithms predict the latency that the offloaded tasks experience using the offloading history. In addition, to minimize the task loss due to the mobility of the vehicles, we develop a method for base station selection. Moreover, we propose a relaying mechanism for the selected network, which operates based on the sojourn time of the vehicles. Through intensive numerical analysis, we demonstrate that the proposed learning-based solutions adapt to the traffic changes of the network by selecting the least congested network, thereby reducing the latency of offloaded tasks. Moreover, we demonstrate that the proposed joint base station selection and the relaying mechanism minimize the task loss in a vehicular environment.

Simulation Analysis of Signal Coverage of ADS-B Base Station Based on DEM

The most basic requirement is to provide continuous and reliable flight dynamic information for monitoring equipment within the range of air traffic control. ADS-B is the future major surveillance technology determined by the International Civil Aviation Organization. Based on the theory of electromagnetic wave and electromagnetic field, this paper analyzes the influence of space propagation on ADS-B signal coverage. Combined with DEM topography, the sightline range cut-off distance of ADS-B was calculated. The base station antenna is simulated and the antenna radiation performance is calculated. In this paper, the actual application of ADS-B base station signal coverage is simulated and the methods and measures to improve the signal coverage of base station is put forward. According to the experiment results, this method meets the needs of engineering application, has certain practical value, and provides a basis for the site selection of base stations.

Towards an efficient weighted random walk domination

In this paper, we propose and study a new problem called the weighted random walk domination. Given a weighted graph G ( V, E ) and a budget B of the weighted random walk, it aims to find a k -size set S , which can minimize the total costs of the remaining nodes to access S through the weighted random walk, which is bounded by B. This problem is critical to a range of real-world applications, such as advertising in social networks and telecommunication base station selection in wireless sensor networks. We first present a dynamic programming based greedy method (DpSel) as a baseline. DpSel is time-consuming when | V | is huge. Thus, to overcome this drawback, we propose a matrix-based greedy method (MatrixSel), which can reduce the computation cost greatly. To further accelerate MatrixSel, we propose a BoundSel approach to reduce the number of the gain computations in each candidate selection by proactively estimating the upper bound of the marginal gain of the candidate node. Notably, all methods can achieve an approximation ratio of (1 - 1/ e ). Experiments on real datasets have been conducted to verify the efficiency, effectiveness, memory consumption and scalability of our methods.

A phase-difference-of-arrival assisted ultra-wideband positioning method for elderly care

Ultra-wideband (UWB) is a promising mean for elderly care in line-of-sight (LOS) environments. One major limitation of UWB-based localization is the non-line-of-sight (NLOS) for signals caused by severe environments, resulting in dramatic performance degradation. Although many NLOS identification and compensation methods in presence of complex indoor environments have been proposed, they are only applicable to specific environments or require enough LOS BSs to detect NLOS measurements. To address these issues comprehensively, we propose a phase-difference-of-arrival (PDOA) assisted UWB positioning method (PDOA-UWB) for elderly care in this paper, which consists of three steps: (1) coarse localization, calculated from PDOA measurements at multiple receiver antennas of UWB base stations (BSs), (2) LOS BSs selection, and (3) fine localization through a convex regression estimator. Our method is simple and effective and can be used in various practical environments without prior information about the NLOS error. Overall experimental results verify that PDOA-UWB can achieve better positioning accuracy than existing methods in severe NLOS environments.

Planning mm-Wave Access Networks Under Obstacle Blockages: A Reliability-Aware Approach

Millimeter-wave (mm-wave) technologies are the main driver to deliver the multiple-Gbps promise in next-generation wireless access networks. However, the GHz-bandwidth potential must coexist with a harsh propagation environment. While strong attenuations can be compensated by directional antenna arrays, the severe impact of obstacle blockages can only be mitigated by smart resource allocation techniques. Multi-connectivity, as multiple mm-wave links from a mobile device to different base stations, is one of them. However, the higher reliability provided by several access alternatives can be fully exploited only if uncorrelated link statuses are guaranteed. Therefore, spatial diversity must be enforced. Moreover, since interposing obstacles can block a link, short access links allow reducing the link unavailability probability. Smart base-station selections can be made once the network is deployed, however, our results show that much better results are achievable if spatial diversity and link-length aspects are directly included in the network planning phase. In this article, we propose an mm-wave access network planning framework that considers base-station spatial diversity, link lengths, and achievable user throughput, according to channel conditions and network congestion. The comparison against traditional k-coverage approaches shows that our approach can obtain much better access reliability, thus providing higher robustness to random obstacles and self-blockage phenomena.

Modified Least Squares Algorithm for Three-Dimensional Target Location Based on Wireless Communication Base Stations

The observed values of time of arrival (TOA) for the radio signals between the target and the wireless communication base stations are mainly affected by signal non-line-of-sight (NLOS) propagation in target location. TOA with NLOS makes a lot of signal noises and propagation delays, that is, location errors. For the first time, this paper focuses on the problem of modifying the Z-axis location coordinates in three-dimensional (3D) target location. A novel algorithm is proposed by establishing the modified least squares 3D location model for the accurate target location. Meanwhile, an optimal base station selection strategy is proposed by using the spectral clustering algorithm, which is based on the spatial distribution of the base stations. Compared with the existing algorithms, the proposed algorithm in this paper has better performance on the accurate target 3D location in real scenes, which has a high value of practical application. The simulations illustrate that the location error of the proposed algorithm is smaller than those of other existing algorithms based on the same simulation data and conditions.

The efficacy of handoff strategy based spectrum allocation using TSB-MFO for D2D and CU communications

Device-to-Device (D2D) based communication plays a major part in improving the 5G network’s system capacity. In pursuance of accomplishing this, effective resource and power allocation algorithms have to be designed for D2D users. As D2D users are considered as secondary users, their interference with cellular users (CUs) should not restrict the communication which is performed based on CU. Most of the existing methodologies performed on bandwidth allocation assume full Channel State Information (CSI) at the base station (BS), which resulted in uncertainty resource allocation. Hence to resolve this, the proposed scheme is developed with an efficient handoff strategy based on bandwidth allocation using Thompson Sampling Bandwidth (TSB) and Magnetic Force Optimization (MFO) for D2D and CU communications along with guaranteed QoS constraint. Here, the available free bandwidth is selected using the TSB technique which in turn is based on overall data rate, level of interference and number of CU users. In this, Magnetic Force optimization (MFO) is utilized for base station selection. This is done based on signal strength variation, coverage area between user and base station, and bandwidth capacity. Thus it results in guaranteed QoS constraint, i.e., parameters like Packet delay; throughput, data rate and reduction in allocated sub-carriers are attained. Finally, the simulation outcomes are performed and the results are estimated in terms of average delay, throughput, node deployment, and data rate. Therefore, the comparative analysis is carried out between the attained outcomes with that of the existing techniques to prove the efficacy of the proposed TSB-MFO technique.