Capacity Of Base Station Research Articles

Hybrid Control Strategy for 5G Base Station Virtual Battery-Assisted Power Grid Peak Shaving

With the rapid development of the digital new infrastructure industry, the energy demand for communication base stations in smart grid systems is escalating daily. The country is vigorously promoting the communication energy storage industry. However, the energy storage capacity of base stations is limited and widely distributed, making it difficult to effectively participate in power grid auxiliary services by only implementing the centralized control of base stations. Aiming at this issue, an interactive hybrid control mode between energy storage and the power system under the base station sleep control strategy is delved into in this paper. Grounded in the spatiotemporal traits of chemical energy storage and thermal energy storage, a virtual battery model for base stations is established and the scheduling potential of battery clusters in multiple scenarios is explored. Then, based on the time of use electricity price and user fitness indicators, with the maximum transmission signal and minimum operating cost as objective functions, a decentralized control device is used to locally and quickly regulate the communication system. Furthermore, a multi-objective joint peak shaving model for base stations is established, centrally controlling the energy storage system of the base station through a virtual battery management system. Finally, a simulation analysis was conducted on data from different types of base stations in the region, designing two distinct scheduling schemes for four regional categories. The analysis results demonstrate that the proposed model can effectively reduce the power consumption of base stations while mitigating the fluctuation of the power grid load.

Distribution network restoration supply method considers 5G base station energy storage participation

This paper proposes a distribution network fault emergency power supply recovery strategy based on 5G base station energy storage. This strategy introduces Theil's entropy and modified Gini coefficient to quantify the impact of power supply reliability in different regions on base station backup time, thereby establishing a more accurate base station's backup energy storage capacity model that can fully utilize the base station's energy storage resources to participate in the emergency power supply of distribution network faults. First, the optimal Copula function in each cycle is determined through the Akaike information criterion and squared Euclidean distance to establish a wind power-photovoltaic output scenario. Secondly, the traditional Gini coefficient is modified using the weighted average node degree and influence rate indicators. A comprehensive vulnerability model is established through the modified Gini coefficient and Theil's entropy indicators. Based on the comprehensive vulnerability model, a backup energy storage time model and a modified backup energy storage capacity model of the base station affected by power supply reliability are established. Thus, the callable energy storage capacity of base stations in different areas is obtained. Finally, a two-stage robust optimization model is introduced to minimize system operating costs to solve the volatility of 5G base station communications and wind-solar output, thereby establishing an emergency power supply recovery model for base station energy storage and wind-solar output. Simulated with the improved IEEE-33 node model, the results show that the proposed base station's energy storage model improves the utilization of the base station energy storage resources and, at the same time, effectively reduces the loss of load during the fault phase of the distribution network and improves the absorption of the PV output.

Doubly Selective with Expectation Maximization based Massive-Mimo-Non-Orthogonal Multiple Access systems

The Long-Term Evaluation (LTE) based Fourth Generation (4G) and Fifth Generation (5G) communications require higher data rates to satisfy the requirements of user equipment. However, it is a difficult task for conventional Multiple Input Multiple Output (MIMO) systems to deliver the highest throughput and data rates to end users. Additionally, an increase in data rates led to increased power consumption, which decreased spectrum efficiency and energy efficiency. Therefore, this paper focuses on the implementation of the Doubly Selective with Expectation Maximization (DSEM) channel estimation method for Massive-MIMO based Non-Orthogonal Multiple Access (NOMA) systems. Here, the DSEM scheme divides the channel into expectation and maximization vectors based on antenna diversity. Further, time sample and frequency band base dual channel analysis are performed to maintain the synchronization in the receiver. The simulations conducted using MatlabR2018a show that base station (BS) capacity, user equipment capacity, uplink (UL)-Spectrum Efficiency (SE), and downlink (DL)-SE performance measures are improved in Massive-MIMO-NOMA systems using DSEM channel estimation and shows the superior performance than state of art approaches.

Reconfigurable Digital Satellite-Borne Base Station Design and Virtual Function Fast Migration Algorithm.

A breakthrough in the technology for virtualizing satellite-borne networks and computing and storage resources can significantly increase the processing capacity and resource utilization efficiency of satellite-borne base stations in response to the development trend in multi-star and multi-system converged satellite internet iterative systems. The protocol processing function of traditional satellite communication systems is generally placed in the ground station system for processing, with poor flexibility and low efficiency. As a result, a reconfigurable digital satellite-borne base station architecture design is suggested, allowing for separation of the hardware and software of the satellite-borne base station and flexible programming and dynamic loading of the satellite-borne base station's functions by software. Meanwhile, a fast adaptive migration algorithm based on multi-dimensional environment awareness is proposed on top of the reconfigurable digital base station, and migration precomputation and real-time computation are added in order to realize rapid deployment of the digital base station system network. Simulation results demonstrate the effectiveness of the proposed algorithm in enhancing system stability and decreasing real-time calculation costs associated with system network migration under conditions of high dynamic changes for each network element in a star-loaded environment. In conclusion, a digital satellite-borne base station system that effectively addresses the issues of low flexibility and high dynamic changes of nodes in the resource-constrained satellite environment can be created by combining the adaptive migration algorithm and the reconfigurable digitized satellite-borne base station architecture.

Resource allocation method for Mobility‐Aware and Multi‐UAV‐Assisted mobile edge computing systems with energy harvesting

AbstractDue to the limited coverage of base station (BS) and battery capacity of mobile users, the resource allocation strategy in multiple unmanned aerial vehicles (UAVs)‐assisted edge computing system with nonlinear energy harvesting is investigated in this paper. The cooperation between BS and multi‐UAV is considered, which can provide extensive coverage for users with mobility. Mobile users can simultaneously offload computation bits to the BS and the UAV, and mobile users harvest energy from BS and UAV. Meanwhile, the mobility of users is taken into account. Moreover, an echo state network (ESN)‐based prediction algorithm is utilized for predicting the future positions of mobile users. Therefore, the UAV can reach the predicted users' positions in advance to ensure the continuity of communication. The objective of the resource allocation strategy is to maximize the energy efficiency by jointly optimizing bandwidth allocation, computation resources, the trajectory of UAV, and transmitting power of mobile users. Then, the resource allocation problem is formulated as a mixed‐integer nonlinear programming problem. The quantum‐behaved particle swarm optimization (QPSO) algorithm is used to solve the problem. Simulation results demonstrate that the proposed strategy can achieve higher energy efficiency than other benchmark strategies. In addition, QPSO algorithm outperforms the standard particle swarm optimization algorithm and genetic algorithm in terms of energy efficiency.

User Selection and Pairing for Future Power Domain Non-Orthogonal Multiple Access (PD-NOMA) using Deep Learning Techniques

The next-generation wireless networks and communications such as 5G/6G offers various benefits such as low latency, high data rates, and improvement in user numbers with increased base station capacity and quality of service. These advantages are obtained from the increasing receiver complexity through the non-orthogonal multiple access (NOMA) of users. It is the promising radio access approach used to enhance next-generation wireless communications. Among the techniques of NOMA such as power and code domain, this paper concentrates on power domain NOMA. The user in the network for transmission is selected using a deep learning approach called deep neural network (DNN). This user selection results are the training data and the loss function is modified for the selection of users that could meet the constraint the selected user cannot be in both strong and weak groups. The user aggregation/user pairing among the sub-channels is performed through the exhaustive analysis using particle swarm optimization (PSO). The usage of DNN-PSO enables the transmitter and required minimum uplink and downlink transmitting power and guaranteed for Quality of Service of each user. The simulation and comprehensive statistical evaluation are performed with the comparative analysis of energy efficiency and maximum achievable rate with the given spectrum efficiency (SE) of PD-NOMA. The proposed model ensures reduced latency, increased throughput, less energy, achievable data rate, user fairness and increased reliability and quality of service.

Uplink non-orthogonal multiple access in heterogeneous networks: A review of recent advances and open research challenges

Fifth-generation wireless communications provide several benefits, including high throughput, lower latency, massive connectivity, considerable improvement in the number of users, higher base station capacity, and achieved quality of service. Non-orthogonal multiple access, an effective approach for sharing the same radio resources, has been highlighted as a viable technology in the fifth-generation wireless networks to achieve the demands of available bandwidth, user connectivity, and application latency. Non-orthogonal multiple access and heterogeneous networks have recently emerged as promising network infrastructures for enhancing the spectrum capacity and accommodating more users by sharing the same resources with high throughput. This potential capability has made the non-orthogonal multiple access–enabled heterogeneous networks a new research topic in the modern era. In this survey, the concept of non-orthogonal multiple access and its significance in different emerging technologies has been well explored. Furthermore, this survey covers a systematic overview of the state-of the-art techniques based on non-orthogonal multiple access–enabled heterogeneous networks and devising taxonomy for uplink non-orthogonal multiple access–enabled heterogeneous networks. In addition, this survey provides critical insights and identifies several open research challenges considering the uplink non-orthogonal multiple access–enabled heterogeneous networks.

Self-Powered Wide Area Infrastructure Based on WiMAX for Real Time Applications of Smart Grid

This work presents a wireless communication network (WCN) infrastructure for the smart grid based on the technology of Worldwide Interoperability for Microwave Access (WiMAX) to address the main real-time applications of the smart grid such as Wide Area Monitoring and Control (WAMC), video surveillance, and distributed energy resources (DER) to provide low cost, flexibility, and expansion. Such wireless networks suffer from two significant impairments. On one hand, the data of real-time applications should deliver to the control center under robust conditions in terms of reliability and latency where the packet loss is increased with the increment of the number of industrial clients and transmission frequency rate under the limited capacity of WiMAX base station (BS). This research suggests wireless edge computing using WiMAX servers to address reliability and availability. On the other hand, BSs and servers consume affected energy from the power grid. Therefore, the suggested WCN is enhanced by green self-powered based on solar energy to compensate for the expected consumption of energy. The model of the system is built using an analytical approach and OPNET modeler. The results indicated that the suggested WCN based on green WiMAX BS and green edge computing can handle the latency and data reliability of the smart grid applications successfully and with a self-powered supply. For instance, WCN offered latency below 20 msec and received data reliability up to 99.99% in the case of the heaviest application in terms of data.

Performance evaluation of precoding system for massive multiple-input multiple-output

Low latency, high data speeds, and a higher degree of perceived service quality for consumers and base station capacity are only some of the advantages of fifth generation (5G) mobile communications. This paper focuses on the design of a precoding system for downlink transmission of multi-user multiple-input multiple-output (MU-MIMO). For MU-MIMO systems, the traditional precoding techniques investigated are difficult since the transmitter precoding matrices created by singular value decomposition (SVD) are calculated twice. This paper implements different techniques of precoding with channel coding. Two advanced precoding, zero forcing (ZF) and maximum ratio transmitter (MRT) systems will be evaluated to find the best between them. Three different coding channels (turbo, low-density parity-check (LDPC), and polar) are used in this paper. The results indicate that the ZF-MU-MIMO with turbo coding outperforms MRT precoding, and more spatial diversity gain may be gained, in terms of throughput, number of users supported, and lower error rate in downlink and uplink massive MIMO.

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.

Distributed User Association With Grouping in Satellite–Terrestrial Integrated Networks

The satellite–terrestrial integrated network (STIN) has been envisioned as an emerging architecture to provide global anytime anywhere network access, and satisfy transmission requirements of high-capacity backhaul data. However, the integration of satellite and terrestrial networks will aggravate the diversity of base stations (BSs) in backhaul delay and capacity, as well as coverage area, which makes it difficult for users with diverse requirements to access the most suitable service BS. As an effort to address the above problems, a distributed user association with grouping (DUAG) mechanism is proposed via the interaction between BSs and users to maximize the sum rate and balance the load of STIN while meeting the user’s demand by user grouping. First, the transmission characteristics of terrestrial and backhaul links are analyzed after constructing a STIN model, which consists of satellite, three types of BSs, and the variety of intelligent terminals. Then, the user association problems are formulated to maximize the sum rate and balance the load of STIN via jointly considering the backhaul capacity of BSs and mobility and delay of users. Meanwhile, the DUAG mechanism is proposed to associate users with the most suitable service BSs. In DUAG, a greedy-based user association algorithm with user grouping is developed for maximizing the sum rate via giving priority to users with high data rate, and a matching algorithm with user grouping is designed for balancing the load by means of performing multiple iterations between users and BSs. Simulation results demonstrate that the proposed DUAG can maximize the sum rate and balance the load of STIN while guaranteeing the delay demand of user with the increase of user density.

Comparison study of channel coding on non-orthogonal multiple access techniques

Some of the benefits of fifth-generation (5G) mobile communications include low latency, fast data rates, and increased amount of perceived service quality of users and base station capacity. The purpose of this paper is to solve some of the problems in the traditional mobile system by increasing the channel capacity, non-orthogonal multiple access (NOMA), has a chance of winning the race, power-domain NOMA (PD-NOMA) is widely used in but it requires a large power imbalance between the signals allocated to various users to work. This paper also proposes an improved mobile system model and compares it with a traditional mobile system, then evaluates the effect of channel coding types on the spectrum efficiency performance. A proposed mobile system relied on increasing the number of users as well as increasing the frequency spectrum and is also proposed to improve the error rate, which is incorporated into NOMA and orthogonal frequency division multiplexing (OFDM) schemes at the same time to provide great flexibility and compatibility with other services, such as the 5G and sixth-generation (6G) systems. The mobile gully system (MGS) system is compared to a traditional system, the result is demonstrated that the proposed outperforms the orthogonal multiple access (OMA) system in terms of sum-rate capacity, and bit error rate (BER) performance.

LoRaWAN Base Station Improvement for Better Coverage and Capacity

Low Power Wide Area Network (LPWAN) technologies provide long-range and low power consumption for many battery-powered devices used in Internet of Things (IoT). One of the most utilized LPWAN technologies is LoRaWAN (Long Range WAN) with over 700 million connections expected by the year 2023. LoraWAN base stations need to ensure stable and energy-efficient communication without unnecessary repetitions with sufficient range coverage and good capacity. To meet these requirements, a simple and efficient upgrade in the design of LoRaWAN base station is proposed, based on using two or more concentrators. The development steps are outlined in this paper and the evaluation of the enhanced base station is done with a series of measurements conducted in Zagreb, Croatia. Through these measurements we compared received messages and communication parameters on novel and standard base stations. The results showed a significant increase in the probability of successful reception of messages on the novel base station which corresponds to the increase of base station capacity and can be very beneficial for the energy consumption of most LoRaWAN end devices.

Hybrid deep learning‐based throughput analysis for UAV‐assisted cellular networks

Mobile users are interested in utilising high network capabilities without time and place constraints. However, with a high level of interest in the usage of mobile phones and internet facilities, the limited capacity of terrestrial base stations (BSs) is unbalanced. As a potential alternative to BSs, unmanned aerial vehicles (UAVs) are emerging as a means of transmitting wireless data to ground mobile users. As an air-to-ground communication network, the real UAVs deployed and collected communication data from ground mobile users. The main objective of this study is to analyse and evaluate user throughput, interference, and power transmission when the UAVs are at different heights. The parameters used include the locations of the UAVs and users, the altitudes and elevation angles from the users to UAVs, signal-to-noise-ratio, throughput values, the categories of line-of-sight, and non-line-of-sight links. Furthermore, K-means used as a clustering method for class identification, long short-term memory (LSTM), and gated recurrent unit (GRU) to analyse and evaluate system performance. The system's performance was compared with a multi-layer perceptron approach. The evaluation results show that the proposed LSTM–GRU provides reliable and encouraging performance with low computational complexity, which is appropriate for heterogeneous networks.

Nonorthogonal Multiple Access for Next-Generation Mobile Networks: A Technical Aspect for Research Direction

5G mobile communications offer several benefits, which include providing extremely low latency, very high data rates, significant improvement in the number of users, and increase in base station capacity and perceived quality of service. This may be achieved at the cost of an increased receiver complexity by nonorthogonal access of users. Nonorthogonal multiple access (NOMA) is one of the capable contenders to achieve the vision of 5G wireless communications. Supporting a higher number of users than available orthogonal resources is the key feather of NOMA. In this article, the basic principle of NOMA has been reviewed and compared with other orthogonal multiple access (OMA). A comprehensive survey is presented in the latest NOMA scheme. The distinguished NOMA schemes design principle features, and recent deployments are discussed. Furthermore, the performance is compared in terms of the bit error rate, system capacity, and energy efficiency. The performance results show that NOMA can achieve the required goals, in terms of the user data rate, system capacity, interference cancellation scheme, and reception complexity.

Capacity-Aware Edge Caching in Fog Computing Networks

This article studies edge caching in fog computing networks, where a capacity-aware edge caching framework is proposed by considering both the limited fog cache capacity and the connectivity capacity of base stations (BSs). By allowing cooperation between fog nodes and cloud data center, the average-download-time (ADT) minimization problem is formulated as a multi-class processor queuing process. We prove the convexity of the formulated problem and propose an Alternating Direction Method of Multipliers (ADMM)-based algorithm that can achieve the minimum ADT and converge much faster than existing algorithms. Simulation results demonstrate that the allocation of fog cache capacity and BS connectivity capacity needs to be balanced to take the full advantage of edge caching. While the maximization of the edge-cache-hit-ratio (ECHR) by utilizing all available fog cache capacity is helpful when the BS connectivity capacity is sufficient, it is preferable to keep a lower ECHR and allocate more traffic to the cloud when the BS connectivity capacity is deficient.

Transmit Antenna Selection Strategies for SC- FDMA- IDMA Massive MIMO Systems

The 5 G wireless offers many advancements over the prevalent 4 G LTE communication networks, such as enhanced data transmission rates (in order of Gbps), Substantially reduced latency, many times increase in the Base Station Capacity and praiseworthy betterments in the QoS offered to the users. IDMA has already proven its potential in 5 G and it can be easily integrated in to massive MIMO systems. The ideology behind the concept of Massive MIMO is to match the requirement for efficient usage of spectrum, and is implemented using several numbers of antennas at the Base Station, catering to a number of subscribers concurrently using same band of the given frequency. However, the cost and complexity of implementation of such large-scale antenna systems is quite high. Thus for reduction in the number of radio frequency chains, the technique of Transmit Antenna Selection is used. In this paper, we propose two transmit antenna selection strategies considered for individual user or overall users based on selection criteria maximum sum rate or minimum bit error performance considering for (MIMO) multiuser multiple input and multiple output single carrier frequency division multiple access based interleave division multiple access wireless system (MIMO-SCFDMA-IDMA). We have taken this scheme for the uplink communication and selection of the subcarrier is through the bulk selection. The performance may get degraded in case of heavy load of multiple subcarriers attached to one antenna, which will in turn call for the role of power amplifier and thus efficiency of the system may go down. Therefore, here we prefer bulk subcarrier selection instead of per subcarrier selection. It is shown in this paper that enhanced sum rate can be achieved as more users are allowed to transmit concurrently, and thus multiuser gain is achieved. We also demonstrated the comparison with simulation result of sum rate performance and Bit error rate performance of varying users. Result shows that antenna selection based on overall system is better than the antenna selection considering each user.

Low Cost Communication for High Speed Railway

The demand for wireless communication has widely increased in high-speed railway (HSR). The people demand high capacity and efficient communication independent of their location and speed. As the HSR provides more convenience to people, so the main attention is given to provide the reliable communication inside the train. It is the challenging task due to the high mobility of train and frequent need of handoff. To reduce the number of handoffs, the modified Hata model has been used in this paper. The performance of system is evaluated in terms of outage probability, coverage probability, transmission capacity and number of base stations. Results show improvement in coverage probability, transmission capacity and reduction in the outage probability and number of base stations. Hence, the cost of communication for high mobility vehicles can be reduced by decreasing the number of base stations.

A short survey on next generation 5G wireless networks

Current 4G - the fourth-generation wireless communication, which exists in most countries, represents an advance of the previous 3 generation wireless communication. However, there are some challenges and limitations, associated with an explosion of wireless devices, which cannot be accommodated by 4G. Increasing the proliferation of smart devices, the development of new multimedia applications, and the growing demand for high data rates are among the main problems of the existing 4G system. As a solution, the wireless system designers have started research on the fifth-generation wireless systems. 5G will be the paradigm shift that could provide with ultra-high data rate, low latency, an increase of the base station capacity, and the improved quality of services. This paper is a review of the changes through the evolution of existing cellular networks toward 5G. It represented a comprehensive study associated with 5G, requirements for 5G, its advantages, and challenges. We will explain the architecture changes – radio access network (RAN), air interfaces, smart antennas, cloud RAN, and HetNet. Furthermore, it discussed physical layer technologies, which include new channel modes estimation, new antenna design, and MIMO technologies. Also, it discussed MAC layer protocols. The article included three kinds of technologies: heterogeneous networks, massive multiple-input and output, and millimeter-wave. Finally, it explained the applications, supported by 5G, new features, various possibilities, and predictions.

Multi-Location-Aware Joint Optimization of Content Caching and Delivery for Backhaul-Constrained UDN.

Mobile edge caching is regarded as a promising way to reduce the backhaul load of the base stations (BSs). However, the capacity of BSs’ cache tends to be small, while mobile users’ content preferences are diverse. Furthermore, both the locations of users and user-BS association are uncertain in wireless networks. All of these pose great challenges on the content caching and content delivery. This paper studies the joint optimization of the content placement and content delivery schemes in the cache-enabled ultra-dense small-cell network (UDN) with constrained-backhaul link. Considering the differences in decision time-scales, the content placement and content delivery are investigated separately, but their interplay is taken into consideration. Firstly, a content placement problem is formulated, where the uncertainty of user-BS association is considered. Specifically, different from the existing works, the specific multi-location request pattern is considered that users tend to send content requests from more than one but limited locations during one day. Secondly, a user-BS association and wireless resources allocation problem is formulated, with the objective of maximizing users’ data rates under the backhaul bandwidth constraint. Due to the non-convex nature of these two problems, the problem transformation and variables relaxation are adopted, which convert the original problems into more tractable forms. Then, based on the convex optimization methods, a content placement algorithm, and a cache-aware user association and resources allocation algorithm are proposed, respectively. Finally, simulation results are given, which validate that the proposed algorithms have obvious performance advantages in terms of the network utility, the hit ratio of the cache, and the quality of service guarantee, and are suitable for the cache-enabled UDN with constrained-backhaul link.