Number Of Base Stations Research Articles

Grid-connected solar-powered cellular base-stations in Kuwait

Recently, the number of mobile subscribers, wireless services and applications have witnessed tremendous growth in the fourth and fifth generations (4G and 5G) cellular networks. In turn, the number of base-stations (BSs) has increased rapidly for wider ubiquitous networking; however, powering BSs has become a major issue for wireless service providers. Most BSs are either grid-connected, which are powered via fossil fuels-dependent power plants, or are off-grid, and operated via diesel generators. Hence, BSs are responsible for carbon dioxide (CO2) emissions. Intuitively, utilizing photovoltaic (PV) solar energy has posed itself as an alternative “green” renewable energy source. This paper studies utilizing PV solar power to energize on-grid (G) cellular BSs in Kuwait, and selling excess PV energy back to the grid to minimize the total cost over the BS operational lifetime. To this end, an on-grid electrical system is designed to power a 4G/5G cellular BS at an urban cell-site. Various electric system configurations are modeled, simulated, and optimized via the HOMER software, while incorporating PV panels, a diesel generator (DG), and/or a battery bank (BB). Comparisons are conducted in terms of net present cost (NPC), CO2 emissions, energy sold, and required system area. The simulation results revealed that the on-grid system configurations yield significantly lower NPC than their off-grid counterpart systems and the PV-G system configuration is the most economical. However, it cannot guarantee system autonomy in power outage scenarios, as it does not contain energy storage devices. Alternatively, the PV-BB-G system configuration is shown to be more reliable than the PV-G configuration, but at the expense of marginally higher NPC. Also, utilizing a dual-axis tracker has been shown to further reduce the NPC and CO2 emissions. In general, increasing the sell-back price effectively reduces the NPC and CO2 emissions, and increases energy sold, however, entails greater system area.

Using Reinforcement Learning to Reduce Energy Consumption of Ultra-Dense Networks With 5G Use Cases Requirements

In mobile networks, 5G Ultra-Dense Networks (UDNs) have emerged as they effectively increase the network capacity due to cell splitting and densification. A Base Station (BS) is a fixed transceiver that is the main communication point for one or more wireless mobile client devices. As UDNs are densely deployed, the number of BSs and communication links is dense, raising concerns about resource management with regard to energy efficiency, since BSs consume much of the total cost of energy in a cellular network. It is expected that 6G next-generation mobile networks will include technologies such as artificial intelligence as a service and focus on energy efficiency. Using machine learning it is possible to optimize energy consumption with cognitive management of dormant, inactive and active states of network elements. Reinforcement learning enables policies that allow sleep mode techniques to gradually deactivate or activate components of BSs and decrease BS energy consumption. In this work, a sleep mode management based on State Action Reward State Action (SARSA) is proposed, which allows the use of specific metrics to find the best tradeoff between energy reduction and Quality of Service (QoS) constraints. The results of the simulations show that, depending on the target of the 5G use case, in low traffic load scenarios and when a reduction in energy consumption is preferred over QoS, it is possible to achieve energy savings up to 80% with 50 ms latency, 75% with 20 ms and 10 ms latencies and 20% with 1 ms latency. If the QoS is preferred, then the energy savings reach a maximum of 5% with minimal impact in terms of latency.

Graph neural network-based cell switching for energy optimization in ultra-dense heterogeneous networks

The development of ultra-dense heterogeneous networks (HetNets) will cause a significant rise in energy consumption with large-scale base station (BS) deployments, requiring cellular networks to be more energy efficient to reduce operational expense and promote sustainability. Cell switching is an effective method to achieve the energy efficiency goals, but traditional heuristic cell switching algorithms are computationally demanding with limited generalization abilities for ultra-dense HetNet applications, motivating the usage of machine learning techniques for adaptive cell switching. Graph neural networks (GNNs) are powerful deep learning models with strong generalization abilities but receive little attention for cell switching. This paper proposes a GNN-based cell switching solution (GBCSS) that has a smaller computational complexity than existing heuristic algorithms. The presented performance evaluation uses the Milan telecommunication dataset based on real-world call detail records, comparing GBCSS with a traditional exhaustive search (ES) algorithm, a state-of-the-art learning-based algorithm, and the baseline without cell switching. Results indicate that GBCSS achieves a 10.41% energy efficiency gain when compared with the baseline and achieves 75.76% of the optimal performance obtained with ES algorithm. The results also demonstrate GBCSS’ significant scalability and generalization abilities to differing load conditions and the number of BSs, suggesting this approach is well-suited to ultra-dense HetNet deployment.

Dynamic power allocation in cellular network based on multi-agent double deep reinforcement learning

With the massively growing wireless data traffic, the dense cellular network has become a significant mode for the fifth generation (5G) network. To fully utilize the benefit of the cellular network, it is primary to design optimal allocation strategy with limited network resource. In this paper, we investigate the dynamic power allocation problem in downlink cellular network based on multi-agent reinforcement learning (RL), where each base station (BS)-user (UE) is modeled as a RL agent to learn optimal power allocation policy in order to maximize the total system capacity. Due to the non-convex and large-scale characteristic of the optimization problem, the computational complexity of centralized traditional methods is unacceptable in practice. Therefore, the power allocation problem is transformed into a multi-agent RL (MARL) issue which can be solved by Deep Reinforcement Learning (DRL) method in a distributed way. We address the expandability of reward function and state space, in order to adapt the variation of network size, such as the number of BSs or UEs and the coverage area of cells. Moreover, the impacts of learning hyperparameters are evaluated for the algorithmic performance. Finally, the effectiveness and superiority of the proposed method are validated by numerical results in different scenarios.

Energy-Efficient Millimeter-Wave Cell-Free Systems Under Limited Feedback

MmWave cell-free systems where multiple base stations (BSs) cooperatively serve user using the mmWave band signal have gained much attention recently due to its capability to dramatically improve the system capacity. One potential drawback of mmWave cell-free systems is that an intensive deployment of BSs increases the energy consumption of network substantially. To improve the energy efficiency, acquisition of accurate downlink channel state information (CSI) at the BSs is essential. However, this task is not easy since the CSI feedback overhead scales linearly with the number of antennas as well as the number of BSs. In this paper, we propose an approach to maximize the energy efficiency of mmWave cell-free systems under the limited feedback. Key idea of the proposed energy-efficient dominating path selection (EE-DPS) algorithm is to choose a small number of paths in the angular domain channel and then exploit the channel information of chosen paths in the data precoding and power allocation. By choosing the dominating paths maximizing the energy efficiency and then feeding back the components of chosen paths, we achieve a considerable reduction in the feedback overhead. Numerical results demonstrate that EE-DPS achieves more than 80% energy efficiency improvement over the conventional CSI feedback-based schemes.

Distributed Precoding Design via Over-the-Air Signaling for Cell-Free Massive MIMO

Most works on cell-free massive multiple-input multiple-output (MIMO) consider non-cooperative precoding strategies at the base stations (BSs) to avoid extensive channel state information (CSI) exchange via backhaul signaling. However, considerable performance gains can be accomplished by allowing coordination among the BSs. This paper proposes the first distributed framework for cooperative precoding design in cell-free massive MIMO (and, more generally, in joint transmission coordinated multi-point) systems that entirely eliminates the need for backhaul signaling for CSI exchange. A novel over-the-air (OTA) signaling mechanism is introduced such that each BS can obtain the same cross-term information that is traditionally exchanged among the BSs via backhaul signaling. The proposed distributed precoding design enjoys desirable flexibility and scalability properties, as the amount of OTA signaling does not scale with the number of BSs or user equipments. Numerical results show fast convergence and remarkable performance gains as compared with non-cooperative precoding design. The proposed scheme can also outperform the centralized precoding design under realistic CSI acquisition.

Performance of Multi-Cell Massive MIMO Systems With Interference Decoding

We consider a multi-cell massive MIMO system where a time-division duplex protocol is used to estimate the channel state information via uplink pilots. When maximum ratio combining (MRC) is used at the BSs, the re-use of pilots across cells causes the pilot contamination effect which yields interference components that do not vanish as the number of base-station (BS) antennas $M \rightarrow \infty$. When treating interference as noise (TIN), this phenomenon limits the performance of multi-cell massive MIMO systems. In this paper, we analyze more advanced schemes based on simultaneous unique decoding (SD) as well as simultaneous non-unique decoding (SND) of the interference that can provide unbounded rate as $M \rightarrow \infty$. We also establish a worst-case uncorrelated noise technique for multiple-access channels to derive achievable rate expressions for finite $M$. Furthermore, we study a much simpler subset of SND (called S-SND) which provides a lower bound to SND and achieves unbounded rate as $M \rightarrow \infty$, and also outperforms SD for finite $M$. For the special cases of two-cell and three-cell systems, using a maximum symmetric rate allocation policy we compare the performance of different interference decoding schemes with that of TIN. Finally, we numerically illustrate the improved performance of the proposed schemes.

Online Learning-Based Downlink Transmission Coordination in Ultra-Dense Millimeter Wave Heterogeneous Networks

In heterogeneous ultra-dense networks with millimeter wave macro cells and small cells, base stations (BSs) and mobile user equipments (UEs) perform beamforming operations to establish highly directional links. In spite of the spatial diversity achieved through directional links, as a number of BSs are densely deployed, inter-cell interference caused by concurrent directional transmissions of adjacent BSs becomes severe, resulting in downlink performance degradation in the network. However, it is very difficult to manage inter-cell interference because of the nature of the time-varying wireless fading environment, the dynamic changes in beam propagation directivity, and unpredictable UEs’ locations. In this paper, we propose an online learning-based transmission coordination algorithm based on the framework of multi-armed bandits to learn the unknown characteristics of inter-BS interference and exploit learned data to derive an optimal policy for maximizing the number of successful downlink transmissions. Through the numerical simulations, we verify the effectiveness of the proposed online learning-based inter-BS interference management scheme.

Modeling Infrastructure Sharing in mmWave Networks With Shared Spectrum Licenses

Competing cellular operators aggressively share infrastructure in many major U.S. markets. If operators were also to share spectrum in next-generation millimeter-wave (mmWave) networks, intra-network interference will become correlated with inter-network interference. We propose a mathematical framework to model a multi-operator mmWave cellular network with co-located base-stations (BSs). We then characterize the signal-to-interference-plus-noise ratio distribution for an arbitrary network and derive its coverage probability. To understand how varying the spatial correlation between different networks affects coverage probability, we derive special results for the two-operator scenario, where we construct the operators’ individual networks from a single network via probabilistic coupling. For external validation, we devise a method to quantify and estimate spatial correlation from actual BS deployments. We compare our two-operator model against an actual macro-cell-dominated network and an actual network primarily comprising distributed-antenna-system (DAS) nodes. Using the actual deployment data to set the parameters of our model, we observe that coverage probabilities for the model and actual deployments not only compare very well to each other, but also match nearly perfectly for the case of the DAS-node-dominated deployment. Another interesting observation is that a network that shares spectrum and infrastructure has a lower rate coverage probability at low rate thresholds than a network of the same number of BSs that shares neither spectrum nor infrastructure, suggesting that the latter is more suitable for low-rate applications.

Delay-based user association in heterogeneous networks with Backhaul

The Internet of things (IoT) as an important application of future communication networks puts a high premium on delay issues. Thus when IoT applications meet heterogeneous networks (HetNets) where macro cells are overlaid with small cells, some traditional problems need rethinking. In this paper, we investigate the delay-addressed association problem in two-tier HetNets considering different backhaul technologies. Specifically, millimeter wave and fiber links are used to provide high-capacity backhaul for small cells. We first formulate the user association problem to minimize the total delay which depends on the probability of successful transmission, the number of user terminals (UTs), and the number of base stations (BSs). And then two algorithms for active mode and mixed mode are proposed to minimize the network delay. Simulation results show that algorithms based on mutual selection between UTs and BSs have better performance than those based on distance. And algorithms for mixed modes have less delay than those for active mode when the number of BSs is large enough, compared to the number of UTs.

Low-Complexity Scheduling and Power Adaptation for Coordinated Cloud-Radio Access Networks

In practical wireless systems, the successful implementation of resource allocation techniques strongly depends on the algorithmic complexity. Consider a cloud-radio access network (CRAN), where the central cloud is responsible for scheduling devices to the frames’ radio resources blocks (RRBs) of the single-antenna base-stations (BSs), adjusting the transmit power levels, and for synchronizing the transmit frames across the connected BSs. Previous studies show that the jointly coordinated scheduling and power control problem in the considered CRAN can be solved using an approach that scales exponentially with the number of BSs, devices, and RRBs, which makes the practical implementation infeasible for reasonably sized networks. This letter instead proposes a low-complexity solution to the problem, under the constraints that each device cannot be served by more than one BS but can be served by multiple RRBs within each BS frame, and under the practical assumption that the channel is constant during the duration of each frame. The paper utilizes graph-theoretical based techniques and shows that constructing a single power control graph is sufficient to obtain the optimal solution with a complexity that is independent of the number of RRBs. Simulation results reveal the optimality of the proposed solution for slow-varying channels, and show that the solution performs near-optimal for highly correlated channels.

Cost‐efficient deployment of mmW small cells with hybrid backhauls

AbstractHybrid backhaul networks with millimeter wave (mmW) technologies are widely viewed as promising solutions to support next‐generation ultradense mobile networks. In this paper, we propose a cost‐efficient deployment framework of mmW small cells with hybrid backhaul, with the aim of providing useful insights on the optimal deployment of small cells for mmW mobile network planning. We investigate the trade‐off between the deployment cost and the number of user equipments that can be admitted while considering 2 kinds of base stations (BSs) in mmW networks: backhaul aggregate nodes (BANs) and wireless backhaul BSs (WBSs). We derive the deployment cost of mmW networks including BSs and wired backhaul links as a function of the number of BANs and WBSs and investigate the relationship between the number of BSs and the number of admitted user equipments. We formulate an optimization problem to maximize cost efficiency subject to the constraint of wireless backhaul link capacity and derive the optimal number of WBSs per BAN cluster as well as the optimal number of deployed BANs. Numerical results validate the effectiveness of our proposed BS deployment framework.

Toward the Performance Enhancement of Microwave Cellular Networks Through THz Links

Driven by the future need of exploiting the so-called extremely and tremendously high-frequency bands for the rate enhancement of microwave cellular networks, in this paper, we consider the possibility of upgrading the already existing infrastructure of microwave base stations (BSs) by enabling them to convey information through microwave or terahertz (THz) links, depending on the distance and whether a line-of-sight (LOS) link exists between a mobile terminal (MT) and its serving BS. By using a tractable stochastic-geometry approach for the modeling of the position and number of BSs and possible blockages in the radio path, we derive analytical formulas for the achieved coverage probability and average rate, which exhibit a close match with the simulation results and validate the expected significant average-rate gains with respect to the communication only through microwave links. In addition, the results show that the expected tradeoff between the average-rate enhancement and coverage-probability reduction due to the high path-loss attenuation of the THz links can be greatly counteracted by employing highly directional antennas in the THz case, especially for very dense BS setups.

Inter-BS Interference-Aware Transmission Coordination for Millimeter Wave Networks

In millimeter-wave wireless networks, base stations (BSs) are capable of performing beamforming transmissions to deliver downlink data packets to user equipments. Despite the advantage of spatial diversity (through beamforming), as the number of BSs installed in the network increases, the packet delivery success probability may decrease because of the increasing interference from the beamforming transmissions of neighboring BSs. We propose the downlink transmission coordination methods that allow each BS to decide whether or not to perform a downlink transmission with respect to the level of inter-BS interferences.

On the trade‐off between energy saving and number of switchings in green cellular networks

AbstractCellular networks are optimized by targeting multiple objectives. Usually, the different objectives are not coherent: minimizing the transmit power; the number of base station (BS) sleep‐mode switchings, ie, active/sleep state transitions; and the activity of the BSs and guaranteeing the quality‐of‐service (QoS) of users. Hence, suitable trade‐offs have to be managed by network planners to provide an efficient solution to the challenge of booming mobile data. In this paper, we propose a multiobjective optimization framework aimed at minimizing the power consumption and the number of BS sleep‐mode switchings in cellular networks, by jointly considering QoS requirements. These requirements are expressed in terms of a required bit rate for each mobile terminal. The framework deals with network management, such as the number of BSs that should be switched on, considering common diurnal patterns of the traffic demand. The optimization technique proposed in this paper is mixed‐integer quadratic programming, which solves the joint power allocation and user association problem while also considering optimized bandwidth allocation schemes. The trade‐off between the conflicting objectives, as well as the performance analysis in terms of the throughput and energy consumption of the network, is shown for different traffic load cases. The proposed optimization can obtain up to 60% energy savings during off‐peak hours, guaranteeing QoS target requirements. By optimizing the network configuration, a 70% reduction in BS switch on/off operations can be reached in a day with 3% more energy expense.

How Much Cache is Needed to Achieve Linear Capacity Scaling in Backhaul-Limited Dense Wireless Networks?

Dense wireless networks are a promising solution to meet the huge capacity demand in 5G wireless systems. However, there are two implementation issues, namely the interference and backhaul issues. To resolve these issues, we propose a novel network architecture called the backhaul-limited cached dense wireless network (C-DWN), where a physical layer (PHY) caching scheme is employed at the base stations (BSs) but only a fraction of the BSs have wired payload backhauls. The PHY caching can replace the role of wired backhauls to achieve both the cache-induced MIMO cooperation gain and cache-assisted Multihopping gain. Two fundamental questions are addressed. Can we exploit the PHY caching to achieve linear capacity scaling with limited payload backhauls? If so, how much cache is needed? We show that the capacity of the backhaul-limited C-DWN indeed scales linearly with the number of BSs if the BS cache size is larger than a threshold that depends on the content popularity. We also quantify the throughput gain due to cache-induced MIMO cooperation over conventional caching schemes (which exploit purely the cached-assisted multihopping). Interestingly, the minimum BS cache size needed to achieve a significant cache-induced MIMO cooperation gain is the same as that needed to achieve the linear capacity scaling.

Optimal base stations planning for Coordinated Multi-Point system

Through decreasing inter-cell interferences, CoMP (Coordinated Multi-Point) can enhance system capacity and spectral efficiency of cell-edge users significantly. BSs (Base stations) are the front-end accesses of CoMP and their planning influences the overall performance of the system directly. Different from the traditional cellular network, the coverage of BSs in CoMP always overlaps with each other to some degree. In order to investigate the BSs planning for CoMP, this paper builds two optimization models to obtain the location and the number of the BSs. A joint optimization algorithm is proposed to solve models, in which the methodologies of mesh adaptive direct search and successive elimination have been used. Numerical results show that the proposed models can effectively reduce the total transmission power and the number of BSs without losing the quality of service for users, which verifies the effectiveness of the proposed algorithms and the inherent advantage of CoMP.

GreenInfra: Capacity of Large-Scale Hybrid Networks With Cost-Effective Infrastructure

The cost-effective impact and fundamental limits of infrastructure support with rate-limited wired backhaul links (i.e., GreenInfra support), directly connecting base stations (BSs), are analyzed in a large-scale hybrid network of unit node density, where multiantenna BSs are deployed. We consider a general scenario such that the rate of each BS-to-BS link scales at an arbitrary rate relative to the number of randomly located wireless nodes $n$ . For the operating regimes with respect to the number of BSs and the number of antennas at each BS, we first analyze the minimum rate of each backhaul link $C_{\text{BS}}$ , required to guarantee the same throughput scaling as in the infinite-capacity backhaul link case. We then identify the operating regimes in which the required rate $C_{\text{BS}}$ scales slower than $n^\epsilon$ for an arbitrarily small $\epsilon > 0$ (i.e., the regimes where $C_{\text{BS}}$ does not need to be infinitely large). We also show the case where our network with GreenInfra is fundamentally in the infrastructure-limited regime, in which the performance is limited by the rate of backhaul links. In addition, we derive a generalized throughput scaling law including the case where the rate of each backhaul link scales slower than $C_{\text{BS}}$ . To validate the throughput scaling law for finite values of system parameters, numerical evaluation is also shown via computer simulations.

Performance analysis of ultra‐dense networks with MRC reception and successive interference cancellation under rate‐limited backhaul

SummaryUltra‐dense network (UDN) is one of candidate technologies for next generation cellular networks. In this paper, we explore performance limits of cooperative UDN with maximal ratio‐combining detection under limited‐capacity backhaul links. More concretely, based on the imperfect estimation, lower bounds of the average per‐user rate are derived for maximal ratio‐combining detection with and without successive interference cancellation scheme when the number of Base stations (BSs) in the network goes to infinite. Then we discuss how the average per‐user rate is impacted by the number of cooperative BSs and the backhaul capacity. Our results show that there is an optimal number of cooperative BSs in the UDN to achieve the best performance in low signal‐to‐noise‐ratio region. Simulations demonstrate that our asymptotic rate bounds are tight when the number of BSs is large. Copyright © 2016 John Wiley & Sons, Ltd.

How Much Bandpass Filtering is Required in Massive MIMO Basestations?

In this paper, we study the impact of aliased out-of-band interference signals on the information sum-rate of the maximum ratio combining receiver in massive multiple-input multiple-output (MIMO) uplink, with both perfect and imperfect channel estimates, in order to determine the required out-of-band attenuation in RF bandpass filters (BPFs). With imperfect channel estimates, our study reveals that as the number of base-station (BS) antennas ($M$) increases, the required attenuation at the BPFs increases as $\mathcal{O}(\sqrt{M})$ with $M \to \infty$, provided the desired information sum-rate (both in the presence and in the absence of AOOBIs (aliased out-of-band interferers)) remains fixed. This implies a practical limit on the number of BS antennas due to the increase in BPF design complexity and power consumption with increasing $M$.