Macro Base Station Research Articles

A Power Control and Intervention Algorithm for Co-Existing IMT Base Stations and Satellite Services

IMT-2020 (International Mobile Telecommunications-2020) is the prevailing mobile communication technology at the moment, significantly affecting societal progress. Nevertheless, the roll-out of the IMT-2020 system has introduced numerous interferences to existing services. The coexistence with fixed satellite services has become a topical issue currently under consideration. This paper discusses the compatibility and interference issues between IMT-2020 and the 14 GHz FSS (fixed-satellite service) uplink, as well as the spectrum access issue solved by artificial intelligence methods. The study shows that the interference from IMT-2020 macro-base stations to FSS space stations exceeds the ITU standard by approximately 10 dB. To control the interference, a partition-based power control algorithm is proposed, which divides ground base stations into multiple areas and virtualizes each area’s base stations into a single large base station then applies power control to maximize the total transmission power of the base stations within the area. Furthermore, three intra-partition power control algorithms are introduced: average power allocation, power allocation based on channel gain, andna power allocation method based on the maximum intra-partition sum rate. Additionally, under the assumption that dynamic satellite nodes are available in the system for ground user access, a spectrum access algorithm utilizing deep reinforcement learning is designed. Simulation results confirm the effectiveness of the proposed scheme, which can reduce the interference from the IMT-2020 system to the FSS service below the threshold, ensuring harmonious coexistence of the two services.

Cooperative edge-caching based transmission with minimum effective delay in heterogeneous cellular networks

In heterogeneous cellular networks (HCNs), neighboring users often request similar contents asynchronously. Based on the content popularity, base stations (BSs) can download and cache contents when the network is idle, and transmit them locally when the network is busy, which can effectively reduce the backhaul burden and the transmission delay. We consider a two-tier HCN, where macro base stations (MBSs) and small base stations (SBSs) can cooperatively and probabilistically cache contents. Each user is associated to the BS with the maximum average received signal power in any tier. With the cooperative content transfer between MBS tier and SBS tier, users can adaptively obtain contents from BSs or remote content servers. We properly model both wired and wireless delays when a user requests an arbitrary content, and propose the concept of effective delay. Content caching probabilities are optimized using the Marine Predators Algorithm via minimizing the average effective delay. Numerical results show that our proposed cooperative caching scheme achieves much shorter delays than the benchmark caching schemes.

Optimizing cell association in 5G and beyond networks: a modified load-aware biased technique

Cellular networks are moving towards increasing heterogeneity by deploying more small cells into macro base station (MBS) to meet rapidly growing traffic demands. To leverage the advantages of these small cells, mobile users should be offloaded onto small base stations (BSs), which will typically be lightly populated and can give a higher data rate by presenting the mobile users with many more channels than the MBS. Likewise, a more balanced cell association will lessen the pressure on the MBS, allowing it to serve its remaining users more effectively. This paper addresses the cell association challenge for Quality of Service (QoS) provisioning in terms of throughput and load-balancing for 5G and future generation networks. This problem is quite challenging because BSs have varying backhaul capacities and users have varying QoS needs. Most of the previous studies are based on reference signal received power (RSRP), signal to interference and noise ratio (SINR) or its variants and most importantly majority of them are not load-aware. Therefore, a modified load-aware biased cell association scheme based on distance is proposed to attain better QoS provisioning in terms of throughput and load-balancing. Simulation results depict that the proposed load-aware-based method outperforms conventional cell association schemes based on RSRP and its variants, and in terms of throughput and load-balancing. Furthermore, the algorithm’s complexity has been assessed through a comparison and analysis of computational time, demonstrating better performance compared to state-of-the-art techniques.

Intelligent Dynamic Power Control with Cell Range Expansion for Small-Cells in 5G HetNets

In 5G heterogeneous networks (HetNets), small-cell base stations (SBSs) are deployed in the coverage of macro base stations (MBSs) to improve the system performance. However, some macro user equipments (MUEs) have strong interference from neighboring SBSs and thus the performance of MBSs decreases. Thus, in this paper, we propose a novel intelligent dynamic power control (DPC) with cell range expansion (CRE) to improve the downlink performance of both small-cell user equipments (SUEs) and CRE user equipments (CUEs) in 5G HetNets. That is, in the proposed DPC scheme, each MUE first collects the received signal strength indicator (RSSI) measurements from neighboring SBSs and sends them to the serving MBS. Then, the MBS finds MUEs with strong interference from neighboring SBSs based on a given target threshold of CRE and offloads a fraction of MUEs from MBSs to SBSs. In addition, SBSs divide their SUEs and CUEs into two groups, i.e., inner and outer groups, to assign different subchannels and dynamically allocate the appropriate transmission power to increase the performance of both SUEs and CUEs. Through simulation results, it is shown that the proposed DPC scheme outperforms others in terms of the capacity and outage probability of SUEs and CUEs.

Layout optimization scheme for coexistence of multiple coverage capabilities in base stationsBased on genetic algorithm

With the development of 5G technology, communication networks have become increasingly complex, leading to a continuous growth in the number of base stations. While this has resulted in faster speeds, lower latency, and greater capacity, it has also presented a pressing issue: weak coverage points, areas with weak signal radiation from base stations. To address this challenge, this paper proposes a layout optimization scheme for the coexistence of base stations with multiple coverage capabilities, based on a genetic algorithm. The scheme considers the rational deployment of various types of base stations (represented as macro base stations and micro base stations in the paper) to improve the situation of weak coverage points. The specific steps of the optimization layout scheme include initializing existing base station coverage, calculating distances to weak coverage points, selecting new base station locations, updating data, and analyzing calculations. The final output includes the coordinates and types of new base stations needed. Through model simulation, the proposed optimization layout scheme can consider cost, coverage effectiveness, and threshold constraints while accommodating various requirements. Additionally, the scheme is highly applicable and can be integrated with computer analysis software. Finally, a dialectical analysis of the model is conducted, recognizing the flexibility and practicality advantages of the layout optimization scheme based on genetic algorithms, while acknowledging potential limitations such as overly idealistic assumptions.

Joint user association, service caching, and task offloading in multi-tier communication/multi-tier edge computing heterogeneous networks

Due to the wide range of intensive computational applications and ubiquitous connectivity of the Internet of Things (IoT) paradigms, it has become crucial to develop a new platform that can achieve low delay, high network throughput, and enhanced quality of service (QoS). This paper proposes a joint user association, service caching, and task offloading strategy to reduce delay and enhance users’ QoS in multi-tier communication and multi-tier edge computing heterogeneous networks (HetNets). The considered system model consists of multi-users with different tasks and service data sizes communicating in a heterogeneous network of one massive multiple-input multiple-output (M-MIMO) macro base station and some small base stations. The proposed work investigates user association, power allocation, optimum service data caching, and task offloading strategies at the computing network edges. Thereby, the objectives of this work are to propose an efficient framework to reduce the system delay, increase the network throughput, and meet the user requirements in multi-tier communication and multi-tier edge computing heterogeneous networks. The simulation results show that the proposed algorithm outperforms the state-of-the-art with a 49.48% decrease in system delay, 80% reduction in cost and hardware complexity in terms of the reduced number of installed antennas, and 48.58% enhancement in the network throughput.

Decoupled Association With Rate Splitting Multiple Access in UAV-Assisted Cellular Networks Using Multi-Agent Deep Reinforcement Learning

In unmanned aerial vehicles (UAVs) assisted cellular networks, user association plays an important role in interference control and spectrum efficiency. In this paper, we study the performance of uplink-downlink decoupled (UDDe) user association in a multi-UAV assisted network in which each user can associate with different UAVs or the macro base station (MBS) for uplink (UL) and downlink (DL) transmissions. Since some popular data may be requested by multiple users, grouping these users and applying multicasting can significantly improve spectral efficiency. Unlike traditional linear precoding that treats interference entirely as noise, we propose a rate-splitting multiple access (RSMA) policy that employs rate splitting at the transmitter and successive interference cancellation (SIC) at the receiver. To be specific, the transmitted signal is split into a common part and a private part, and the interference is partially decoded and partially treated as noise. In this context, we formulate a joint optimization problem of UL-DL association and beamforming for maximizing the sum-rate of users in UL and that of multicast groups in DL under the constraints of UAV backhaul capacity and power budget. Since the formulated problem is non-convex with intricate states and an individual UAV may not know the rewards of other UAVs, we convert it into a robust partially observable Markov decision process (POMDP). Then we resort to multi-agent deep reinforcement learning (MADRL) that enables each UAV to learn and optimize its policy in a distributed manner. To achieve an optimal policy, we further propose an improved clip and count-based proximal policy optimization (PPO) algorithm to train actor and critic networks. Simulation results demonstrate the superiority of the proposed decoupled association strategy with RSMA and the MADRL learning algorithm.

A Joint Optimization Algorithm for Trajectory Planning and Resource Allocation of Vehicle Mobile Base Stations for On-Demand Coverage Networks

In today’s urban hotspot regions, service traffic exhibits dynamic variations in both time and location. Traditional fixed macro base stations (FMBSs) are unable to meet these dynamic demands due to their fixed coverage and capacity. Therefore, this paper introduces a novel algorithm for the joint optimization of the placement of terrestrial vehicle-mounted mobile micro base stations (mBSs), the correlation of service clusters (SCs) with mBSs, and resource assignments. The objective is to maximize the matching degree between network capacity and service demands while adhering to constraints related to the power, coverage, and bandwidth of mBSs, as well as the data rate required for the services. Additionally, we investigate the mobility of the mBSs towards the SCs in the spatiotemporal changing service demand network and obtain optimal trajectories for the mBSs. We begin by formulating the problem of maximizing the matching degree by analyzing the capacity provided by the base stations and the network service demand. Subsequently, we derive solutions to the optimization problem using our algorithm. The simulation results demonstrate that the proposed algorithm can effectively meet the capacity demand of dynamically changing hotspot regions and achieve on-demand, resilient coverage of hotspot regions in the network.

Dynamic Multi-Sleeping Control with Diverse Quality-of-Service Requirements in Sixth-Generation Networks Using Federated Learning

The intensive deployment of sixth-generation (6G) base stations is expected to greatly enhance network service capabilities, offering significantly higher throughput and lower latency compared to previous generations. However, this advancement is accompanied by a notable increase in the number of network elements, leading to increased power consumption. This not only worsens carbon emissions but also significantly raises operational costs for network operators. To address the challenges arising from this surge in network energy consumption, there is a growing focus on innovative energy-saving technologies designed for 6G networks. These technologies involve strategies for dynamically adjusting the operational status of base stations, such as activating sleep modes during periods of low demand, to optimize energy use while maintaining network performance and efficiency. Furthermore, integrating artificial intelligence into the network’s operational framework is being explored to establish a more energy-efficient, sustainable, and cost-effective 6G network. In this paper, we propose a small base station sleeping control scheme in heterogeneous dense small cell networks based on federated reinforcement learning, which enables the small base stations to dynamically enter appropriate sleep modes, to reduce power consumption while ensuring users’ quality-of-service (QoS) requirements. In our scheme, double deep Q-learning is used to solve the complex non-convex base station sleeping control problem. To tackle the dynamic changes in QoS requirements caused by user mobility, small base stations share local models with the macro base station, which acts as the central control unit, via the X2 interface. The macro base station aggregates local models into a global model and then distributes the global model to each base station for the next round of training. By alternately performing model training, aggregation, and updating, each base station in the network can dynamically adapt to changes in QoS requirements brought about by user mobility. Simulations show that compared with methods based on distributed deep Q-learning, our proposed scheme effectively reduces the performance fluctuations caused by user handover and achieves lower network energy consumption while guaranteeing users’ QoS requirements.

A Spectral Gap-Based Topology Control Algorithm for Wireless Backhaul Networks

This paper presents the spectral gap-based topology control algorithm (SGTC) for wireless backhaul networks, a novel approach that employs the Laplacian Spectral Gap (LSG) to find expander-like graphs that optimize the topology of the network in terms of robustness, diameter, energy cost, and network entropy. The latter measures the network’s ability to promote seamless traffic offloading from the Macro Base Stations to smaller cells by providing a high diversity of shortest paths connecting all the stations. Given the practical constraints imposed by cellular technologies, the proposed algorithm uses simulated annealing to search for feasible network topologies with a large LSG. Then, it computes the Pareto front of the set of feasible solutions found during the annealing process when considering robustness, diameter, and entropy as objective functions. The algorithm’s result is the Pareto efficient solution that minimizes energy cost. A set of experimental results shows that by optimizing the LSG, the proposed algorithm simultaneously optimizes the set of desirable topological properties mentioned above. The results also revealed that generating networks with good spectral expansion is possible even under the restrictions imposed by current wireless technologies. This is a desirable feature because these networks have strong connectivity properties even if they do not have a large number of links.

Fair resource allocation and SBS selection for MC-NOMA-based HetNets supporting D2D communications

This paper studies the application of downlink multi-carrier non-orthogonal multiple access (MC-NOMA) and device-to-device (D2D) in heterogeneous networks (HetNets). In the considered model, the small base stations (SBSs) can reuse the sub-channels (SCs) of the macro base stations (MBS). The paper aims to maximize the system sum-rate while managing co-tier and co-channel interference by optimizing joint SBS selection, fair SC allocation, and power allocation, under the constraints of guaranteeing a certain amount of resources to all cellular users (CUs)and D2D pairs and satisfying CUs’ minimum signal-to-interference-plus-noise ratio (SINR) requirements. However, obtaining an efficient exact solution is challenging due to the non-convexity of the mixed integer optimization problem. To deal with this issue, the problem is decoupled into two sub-problems: 1- SBS selection and fair SC allocation problem and 2- power allocation problem. Then, a four-sided many-to-one matching method is proposed to solve the SBS selection and fair SC allocation problem jointly. The performance of the SC fair allocation algorithm is investigated in terms of the system quality of services (QoS) using a proposed mathematical expression. Furthermore, a novel dynamic search operator is introduced to enhance the performance of the matching-based algorithm. Next, the non-convex power allocation problem is transformed into a deep reinforcement learning (DRL) based framework, and the deep deterministic policy gradient (DDPG) approach is employed to solve the problem. Moreover, a joint SBS selection, SC allocation, and power allocation iterative algorithm is proposed to improve the system sum-rate. Simulation results validate our analysis and indicate the superiority of the proposed algorithms in terms of the system sum-rate compared to the benchmark algorithms.

QoS-Driven Slicing Management for Vehicular Communications

Network slicing is introduced for elastically instantiating logical network infrastructure isolation to support different application types with diversified quality of service (QoS) class indicators. In particular, vehicular communications are a trending area that consists of massive mission-critical applications in the range of safety-critical, intelligent transport systems, and on-board infotainment. Slicing management can be achieved if the network infrastructure has computing sufficiency, a dynamic control policy, elastic resource virtualization, and cross-tier orchestration. To support the functionality of slicing management, incorporating core network infrastructure with deep learning and reinforcement learning has become a hot topic for researchers and practitioners in analyzing vehicular traffic/resource patterns before orchestrating the steering policies. In this paper, we propose QoS-driven management by considering (edge) resource block utilization, scheduling, and slice instantiation in a three-tier resource placement, namely, small base stations/access points, macro base stations, and core networks. The proposed scheme integrates recurrent neural networks to trigger hidden states of resource availability and predict the output of QoS. The intelligent agent and slice controller, namely, RDQ3N, gathers the resource states from three-tier observations and optimizes the action on allocation and scheduling algorithms. Experiments are conducted on both physical and virtual representational vehicle-to-everything (V2X) environments; furthermore, service requests are set to massive thresholds for rendering V2X congestion flow entries.

Clustered jamming and antenna beam-width fluctuations for UAV-assisted cellular networks

Jamming devices have the capability to deliberately disrupt legitimate communications. Clustered jamming pertains to the deployment of jammers that are concentrated within specific clusters to amplify the interference. This paper models the impact of clustered jamming interference on unmanned aerial vehicles (UAV)-assisted cellular networks, taking into account fluctuations of the 3-dimensional (3D) beam-width of low altitude platform (LAP) antennas. The distribution of UAVs and macro base station (MBS) is modeled by independent 3D and 2D homogeneous Poisson point processes, respectively, while jammers are modeled using an independent Matern cluster process. Antenna 3D beam-width fluctuations are modeled according to Normal distribution. The modeling centers on the probability of association as well as coverage and spectral efficiency for line-of-sight LAP, non-line-of-sight LAP, and MBS links. This modeling investigation is conducted under multiple jamming clusters and the antenna 3D beam-width fluctuations of UAV. Analytical expressions are derived to characterize association, coverage, and SE performance in the context of clustered jamming and UAV beam-width variation. The obtained results indicate that UAV-assisted cellular networks experience a notable decline in the proximity of clustered jammers. Furthermore, this performance degradation is significantly exacerbated when the antenna 3D beam-width exhibits fluctuations. Consequently, to enhance the performance of UAV-assisted cellular networks, it is imperative for system designers to consider the implementation of robust anti-jamming techniques.

Conflict-Free Parallel Data Access Technology for Matrix Calculation in Memory System of ASIP of 5G/6G Macro Base Stations

Among the physical layer baseband algorithms in macro base stations, the matrix processing has the dom-inant computing cost, large data access overhead and com-plicated addressing mode. The existing data access methods are not the best solution for memory system of ASIP (Ap-plication Specific Instruction Set Processor) in 5G/6G macro stations. We hence proposed a parallel conflict-free data access method for matrix. Moreover, we proposed a non-redundant access method for positive definite matrix that stores only trigonometric part, and the corresponding parallel addressing method with low storage overhead. Our method solves the problem of data conflict and minimizes the memory space of ASIP, and supports ASIP to approach the performance limit of architecture to the maximum ex-tent under the constraint of architecture and data parallel-ism. We implemented the proposed method as a static memory optimizer, which provides a tool for the design and optimization of memory system of ASIP in 5G/6G macro base stations with low overhead. Experimental results show that for 64W64 positive definite matrix inversion algorithm based on Cholesky decomposition, our addressing method can save 32% of the execution time, and the cost of memory space is reduced by half.

Joint Design of Antenna Selection and Resource Allocation in Massive MIMO‐Based Ultra Dense Heterogeneous Networks

Fifth generation (5G) communication network is anticipated to satisfy the need for higher data rates, less latency, ubiquitous connectivity with minimum consumption of energy and acceptable quality of Service (QoS). The massive MIMO and heterogeneous network (HetNet) has evolved as a promising technology to address the aforementioned challenges. In the proposed framework, the massive MIMO technology at the macro base station and full duplex (FD) technology enabled at the small cell base station in HetNet are investigated. An optimal antenna selection strategy, user association (UA), and power allocation (PA) in a massive MIMO‐based HetNet with FD‐enabled small cells are proposed to optimize system sum rate. Initially, the objective function of the optimization problem is nonconvex and a mixed integer nonlinear programming problem. Then, the solution is obtained by transforming the problem into convex problem utilizing Lagrangian decomposition. Simulation results manifest the effect of the number of base station antennas, number of small cell base stations, self‐interference cancellation factor, and channel state information on system sum rate. Moreover, it is evident that the propounded algorithm is effective in terms of optimizing the system sum rate when compared to the conventional maximum signal‐ to ‐ interference ‐ plus ‐noise ‐ ratio (SINR) algorithm.

Random Caching Design for Multi-User Multi-Antenna HetNets With Interference Nulling

The strong interference suffered by users can be a severe problem in cache-enabled networks (CENs) due to the content-centric user association mechanism. To tackle this issue, multi-antenna technology may be employed for interference management. In this paper, we consider a user-centric interference nulling (IN) scheme in two-tier multi-user multi-antenna CEN, with a hybrid most-popular and random caching policy at macro base stations (MBSs) and small base stations (SBSs) to provide file diversity. All the interfering SBSs within the IN range of a user are requested to suppress the interference at this user using zero-forcing beamforming. Using stochastic geometry analysis techniques, we derive a tractable expression for the area spectral efficiency (ASE). A lower bound on the ASE is also obtained, with which we then consider ASE maximization, by optimizing the caching policy and IN coefficient. To solve the resultant mixed integer programming problem, we design an alternating optimization algorithm to minimize the lower bound of the ASE. Our numerical results demonstrate that the proposed caching policy yields performance that is close to the optimum, and it outperforms several existing baselines.

Intelligent deep reinforcement learning-based scheduling in relay-based HetNets

We consider a fundamental file dissemination problem in a two-hop relay-based heterogeneous network consisting of a macro base station, a half-duplex relay station, and multiple users. To minimize the dissemination delay, rateless code is employed at the base station. Our goal is to find an efficient channel-aware scheduling policy at the half-duplex relay station, i.e., either fetch a packet from the base station or broadcast a packet to the users at each time slot, such that the file dissemination delay is minimized. We formulate the scheduling problem as a Markov decision process and propose an intelligent deep reinforcement learning-based scheduling algorithm. We also extend the proposed algorithm to adapt to dynamic network conditions. Simulation results demonstrate that the proposed algorithm performs very close to a lower bound on the dissemination delay and significantly outperforms baseline schemes.

Analysis of Fluctuating Antenna Beamwidth in UAV-Assisted Cellular Networks

This paper investigates a cellular network assisted by unmanned aerial vehicles (UAVs) in the presence of a fluctuating 3-dimensional (3D) antenna beamwidth. The primary objective is to perform an analysis of typical user equipment (T-UE) performance with a specific focus on coverage probability and spectral efficiency (SE) in the presence of fluctuations of 3D antenna beamwidth. Within this analytical framework, the macro base stations (MBSs) are meticulously characterized through the application of an independent 2D homogeneous Poisson point process (PPP), while the low-altitude platforms (LAPs) are described using an independent 3D PPP. The study entails the derivation of association probabilities, determining the likelihood of the T-UE associating with MBSs, line-of-sight LAPs, and non-line-of-sight LAPs. Through rigorous mathematical analysis, the paper formulates precise analytical expressions that encapsulate the association and coverage probabilities, taking into account the inherent variability in the UAV antenna beamwidth. This research focuses on a thorough performance evaluation of the T-UE across diverse network configurations, encompassing LAP density, the transmission power of LAPs, and the critical signal-to-interference ratio threshold. The outcomes of this study distinctly underscore the substantial disruptive impact resulting from fluctuating beamwidth on the performance of the T-UE within the UAV-assisted cellular network. Additionally, this performance is further impacted by larger densities and transmission power of the LAP. Hence, it is imperative to take into account the influence of these fluctuations on network association, coverage, and SE whenever contemplating a UAV-assisted cellular network.

Intelligent Cloud-Edge Collaborations Assisted Energy-Efficient Power Control in Heterogeneous Networks

We consider a typical heterogeneous network (HetNet), which consists of a macro base station (BS) and multiple small BSs sharing the same spectrum band. Since the spectrum sharing among different BS-user links may cause severe mutual interference and degrades the global energy efficiency (GEE), it is important to optimize the transmit power of each BS and enhance the GEE. Conventional methods first collect the global instantaneous channel state information (CSI) and then optimize the transmit power in a centralized manner. Nevertheless, it is demanding to obtain the global instantaneous CSI in practical situations and the centralized optimization may easily overwhelm the coherence time of wireless channels. To tackle these issues, we leverage the strong computing capability of the (cloud) core network and the fast configuration capability of (edge) BSs and propose an intelligent cloud-edge collaboration framework. By properly designing the cloud-edge collaboration, we develop a deep reinforcement learning (DRL) based energy efficient power control algorithm. With the proposed algorithm, each BS can configure its transmit power independently and enhance the GEE. Simulation results reveal that, in both static-user and mobile-user scenarios, the proposed algorithm can provide comparable GEE performance with the conventional methods while requiring a much lower time complexity.

Resource Allocation Control of UAV-Assisted IoT Communication Device

This work aims to explore the effect of Resource Allocation and Control (RAC) of the Unmanned Aerial Vehicle (UAV)-assisted Internet of Things (IoT) communication equipment. The communication congestion problem is caused by insufficient computing resources of macro base stations when many user terminals access the IoT system. To this end, this work innovatively constructs the IoT system combined with UAVs to satisfy the user’s communication experience and allocate channel resources simultaneously. Finally, a UAV-assisted IoT resource allocation and control model is constructed and tested through experiments on the simulation software to analyze its performance. The results indicate that the transmission delay of the model reported here is significantly lower than that of other algorithms with the increased number of end users and the proportion of tasks. When the number of users is 500, the transmission delay of data resources is 47.5s. Besides, it requires lower energy consumption and shorter task completion time while maintaining lower throughput. At the same time, the uplink transmission rate of the model is generally stable at 3.10bps; the uplink transmission rate of the entire wireless communication system shows an increasing trend with the increase of the transmit power. Therefore, it is found that the model constructed here has strong robustness and achieves an excellent data resource allocation effect while ensuring lower data delay. This model provides an experimental reference and contribution to the improvement of the performance of IoT systems and the accurate allocation of network resources.