Cluster Base Station Research Articles

Effective Service Architecture and Antenna Allocation Mechanism for CoMP Transmissions in Ultra Dense Networks

A practical and effective service deployment method is proposed for coordinated multi-point (CoMP) transmissions in ultra-dense networks (UDN). Despite the potential of UDN for throughput enhancement, the throughput of edge users is still considerably deteriorated by interferences from adjacent cells. Applying the CoMP transmissions among cells appears to be a feasible solution to resolve this issue. However, the nature of the almost randomly deployed locations of small cell base stations (BSs) and their limited numbers of antennas make it difficult to design an effective resource coordination mechanism for the CoMP services in UDN. Reconsidering this problem from an antenna allocation viewpoint, we present a simple yet effective approach to improve the cell-edge throughput under a centralized radio access network architecture. The first step is to partition the entire service area into multiple subareas, and the second step is to allocate the antenna degrees of freedom to each subarea. To improve the scalability of this service method, a two-tier service deployment method is further introduced based on the BS clustering and service area partitioning of the entire network. The simulation results show that the proposed service architecture can provide a significant improvement on the cell-edge throughput while still maintaining a good average throughput in the UDN.

Coordination Multipoint Enabled Small Cells for Coalition-Game-Based Radio Resource Management

Fifth-generation networks are becoming a reality, and radio access network architectures are being redefined and rebuilt to accommodate the emerging specifications and demands. The future radio access networks need to incorporate in their architectural design smooth reconfigurability and high flexibility without compromising the throughput and the perceived quality of the new services and traffic types that future networks introduce. Such densely distributed access networks suffer the effect of inter-cell interference. Robust and intelligent coordination of multiple access networks is required in order to minimize the effect of interference and maximize users' gains. Coordinated multipoint operation is already considered as a promising technique for satisfying the challenges of the spectrum and interference management, especially for users at the edge of the cell coverage area. To increase the benefits of coordination multipoint technology in optimizing service quality in dense small cell networks, this study proposes the adaptation of game theory, which will optimize the formation of cooperated base station clusters. The system-level simulations performed indicate that there is a significant increase in the throughput of users located at the edge of cells, without compromising the quality of service experienced by the rest of the users, thus rendering it a suitable candidate for the emerging next-generation networks.

Coordinated Hybrid Precoding Design in Millimeter Wave Fog-RAN

In this paper, we propose a femto base station cluster (FBSC)-based fog radio access network (F-RAN) structure, where multiple femto base stations (FBSs) coordinately serve users in each FBSC with the millimeter wave band. In addition, the FBS head (FBSH) in each FBSC receives data from the central processor (CP) via fronthaul link with the microwave band. Based on this, we formulate a transmission delay minimization problem by optimizing coordinated hybrid analog/digital precoding under transmit power constraints of each FBS in the FBSCs and the base station (BS) in the CP. Then, we propose a coordinated analog precoding scheme for multiple FBSs within the same FBSC. Meanwhile, we equivalently divide the original problem into two independent optimization problems, and propose an effective iterative algorithm to solve them. Simulation results show the effectiveness of our proposed algorithms.

Resource Management in Multicloud IoT Radio Access Network

Cloud radio access network (CRAN) is a promising approach to provide ubiquitous and on demand access to future Internet of Things (IoT) networks. The existing CRANs assume a single cloud which suffers from computational complexity and signaling latency to support massive number of IoT devices in large scale network deployments. This paper focuses on the scheduling of IoT devices in a multicloud IoT network scenario. This paper considers the downlink of an IoT network consisting of multiple clouds, each coordinates a cluster of several base stations (BSs) allowing joint signal processing. The transmit frame of each BS is composed of several resource blocks (RBs). The multiple clouds are linked to the central cloud which performs scheduling of IoT devices and synchronization of transmit frames. The work models the IoT devices to RBs assignment problem considering the intercloud and intracloud interference. The optimization problem maximizes the overall network utilization under practical network constraints. Further, this paper also proposes a low complexity heuristic algorithm to solve the constraint resource allocation problem in linear time. Complexity analysis of proposed algorithm is carried out and simulations results for a number of IoT network scenarios demonstrate that proposed solution is numerically accurate and performs close to the optimal solution.

Online Supervisory Control and Resource Management for Energy Harvesting BS Sites Empowered with Computation Capabilities

The convergence of communication and computing has led to the emergence of multi-access edge computing (MEC), where computing resources (supported by virtual machines (VMs)) are distributed at the edge of the mobile network (MN), i.e., in base stations (BSs), with the aim of ensuring reliable and ultra-low latency services. Moreover, BSs equipped with energy harvesting (EH) systems can decrease the amount of energy drained from the power grid resulting into energetically self-sufficient MNs. The combination of these paradigms is considered here. Specifically, we propose an online optimization algorithm, called Energy Aware and Adaptive Management (ENAAM), based on foresighted control policies exploiting (short-term) traffic load and harvested energy forecasts, where BSs and VMs are dynamically switched on/off towards energy savings and Quality of Service (QoS) provisioning. Our numerical results reveal that ENAAM achieves energy savings with respect to the case where no energy management is applied, ranging from 57% to 69%. Moreover, the extension of ENAAM within a cluster of BSs provides a further gain ranging from 9% to 16% in energy savings with respect to the optimization performed in isolation for each BS.

A Framework for Automated Cellular Network Tuning With Reinforcement Learning

Tuning cellular network performance against always occurring wireless impairments can dramatically improve reliability to end users. In this paper, we formulate cellular network performance tuning as a reinforcement learning (RL) problem and provide a solution to improve the performance for indoor and outdoor environments. By leveraging the ability of Q-learning to estimate future performance improvement rewards, we propose two algorithms: (1) closed loop power control (PC) for downlink voice over LTE (VoLTE) and (2) self-organizing network (SON) fault management. The VoLTE PC algorithm uses RL to adjust the indoor base station transmit power so that the signal to interference plus noise ratio (SINR) of a user equipment (UE) meets the target SINR. It does so without the UE having to send power control requests. The SON fault management algorithm uses RL to improve the performance of an outdoor base station cluster by resolving faults in the network through configuration management. Both algorithms exploit measurements from the connected users, wireless impairments, and relevant configuration parameters to solve a non-convex performance optimization problem using RL. Simulation results show that our proposed RL based algorithms outperform the industry standards today in realistic cellular communication environments.

Modeling and Analysis of Multi-Tier Clustered Millimeter-Wave Cellular Networks With User Classification for Large-Scale Hotspot Area

This paper focuses on a comprehensive framework design and performance analysis for a large-scale hotspot communication scenario where there exist large number of clustered pico base stations (P-BSs) and femto BSs (F-BSs) working on millimeter-Wave (mm-Wave). For such realistic hotspots' deployment, to exploit the coupling between user equipments (UEs) and base stations (BSs), we model the geographical centers of UE hotspots as independent PPP around which UEs, P-BSs and F-BSs are scattered and form independent and non-homogeneous Poisson cluster processes (PCPs). However, with clustered P-BSs and F-BSs, the UEs would experience the severer intra-cluster interference so that the performance is degraded greatly. To overcome this problem in clustered networks, an effective interference management scheme is proposed by using the ratio of the first and second nearest distances of a typical UE from P-BSs so that a typical UE can be classified as either cluster-center UEs (CCUEs) or cluster-edge UEs (CEUEs). Correspondingly, the total available spectrum band is divided as CCUE-band and CEUE-band, which are occupied by P-BSs associated with CCUEs and CEUEs, respectively. Unlike P-BSs, the F-BSs randomly access the CEUE-band and CCUE-band with a given access portion factor. By using the method from stochastic geometry and the distinguishing feature of mm-Wave, such as directional beamforming with sectored antenna model and modified line of sight ball model for blockage, the UE association probability and Laplace transforms of interference are derived. This yields the derivations of downlink transmission rates. The numerical results show that the proposed clustered heterogeneous scheme with the cluster-UE classification outperforms the traditional ones without the UE classification. This approach not only captures the UE-BS coupling and the non-homogeneous nature of multi-tier networks, but also restricts the severe intra-cluster interference due to clustered BSs.

Hybrid Key Management Scheme for Heterogeneous Wireless Sensor Networks

Wireless sensor network (WSN) is a large scale network with thousands of tiny sensor nodes deployed in the field which is widely used in real time applications. The greatest issue in wireless sensor network is secure key establishment and communication. In this paper, hybrid key management scheme is proposed for heterogeneous WSN. Initially the keys are generated and pre-distributed into sensor nodes using hyper elliptic curve equation. After the deployment, the node tries to form a cluster and then establishes a symmetric key using orthogonal Latin square method, where the cluster heads and base station use public key encryption method based on hyper elliptic curve cryptography (HECC). The simulation results show that the proposed scheme is better in terms of robustness, connectivity and lesser computational overhead with reduced key size.

Interference Mitigation via Rate-Splitting and Common Message Decoding in Cloud Radio Access Networks

Cloud-radio access networks (C-RAN) help in overcoming the scarcity of radio resources by enabling dense deployment of base-stations (BSs) and connecting them to a central-processor (CP). This paper considers the downlink of a C-RAN, where the cloud is connected to the BSs via limited-capacity backhaul links. We propose and optimize a C-RAN transmission scheme that combines rate splitting, common message decoding, and beamforming vectors design and clustering. To this end, this paper optimizes a transmission scheme that combines rate splitting (RS), common message decoding (CMD), and clustering and coordinated beamforming. In this paper, we focus on maximizing the weighted sum-rate subject to per-BS backhaul capacity and transmit power constraints, so as to jointly determine the RS-CMD mode of transmission, the cluster of BSs serving private and common messages of each user, and the associated beamforming vectors of each user private and common messages. This paper proposes solving such a complicated non-convex optimization problem using $l_{0}$ -norm relaxation techniques, followed by inner-convex approximations (ICA), so as to achieve stationary solutions to the relaxed non-convex problem. The numerical results show that the proposed method provides significant performance gain as compared to conventional interference mitigation techniques in C-RAN which simply treat interference as noise (TIN).

Stochastic Modeling and Analysis of User-Centric Network MIMO Systems

This paper provides an analytical performance characterization of both uplink (UL) and downlink (DL) user-centric network multiple-input multiple-output (MIMO) systems, where a cooperating BS cluster is formed for each user individually and the clusters for different users may overlap. In this model, cooperating BSs (each equipped with multiple antennas) jointly perform zero-forcing beamforming to the set of single-antenna users associated with them. As compared to a baseline network MIMO systems with disjoint BS clusters, the effect of user-centric clustering is that it improves signal strength in both UL and DL, while reducing cluster-edge interference in DL. This paper quantifies these effects by assuming that BSs and users form Poisson point processes and by further approximating both the signal and interference powers using Gamma distributions of appropriate parameters. We show that BS cooperation provides significant gain as compared to single-cell processing for both UL and DL, but the advantage of user-centric clustering over the baseline disjoint clustering system is significant for the DL cluster-edge users only. Although the analytic results are derived with the assumption of perfect channel state information and infinite backhaul between the cooperating BSs, they nevertheless provide architectural insight into the design of future cooperative cellular networks.

Optimizing the MIMO Cellular Downlink: Multiplexing, Diversity, or Interference Nulling?

A base-station (BS) equipped with multiple antennas can use its spatial dimensions in three different ways: 1) to serve multiple users, thereby achieving a multiplexing gain; 2) to provide spatial diversity in order to improve user rates; and 3) to null interference in neighboring cells. This paper answers the following question: What is the optimal balance between these three competing benefits? We answer this question in the context of the downlink of a cellular network, where multi-antenna BSs serve multiple single-antenna users using zero-forcing beamforming with equal power assignment, while nulling interference at a subset of out-of-cell users. Any remaining spatial dimensions provide transmit diversity for the scheduled users. Utilizing tools from stochastic geometry, we show that, surprisingly, to maximize the per-BS ergodic sum rate, with an optimal allocation of spatial resources, interference nulling does not provide a tangible benefit. The strategy of avoiding inter-cell interference nulling, reserving some fraction of spatial resources for multiplexing, and using the rest to provide diversity, is already close-to-optimal in terms of the sum-rate. However, interference nulling does bring significant benefit to cell-edge users, particularly when adopting a range-adaptive nulling strategy where the size of the cooperating BS cluster is increased for cell-edge users.

F5N‐based unequal clustering protocol (F5NUCP) for wireless sensor networks

SummaryWireless sensor networks (WSNs) became very popular and still remains an active research because of diverse applications. Minimizing the energy dissipation and maximizing the network lifetime is an important design issue in WSN. Clustering is the widely used energy effective technique to lessen the overall energy consumption of WSN. An appropriate selection of cluster heads (CHs) and cluster size is a crucial process in clustered WSN. The CHs nearer to base station (BS) suffer from hot spot problem and die earlier than the normal lifetime. This paper contributes a five input fuzzy‐based unequal clustering protocol (F5NUCP) for selecting CHs and determining the appropriate cluster size to prevent the network from hot spot problem. Unequal clusters eliminates the hot spot problem by producing smaller clusters for the nodes located near the BS and larger clusters for the nodes located far away from the BS. F5NUCP uses a nonprobabilistic approach for selecting tentative CHs by introducing a back‐off timer where the timer value is set using the remaining energy of the node. The proposed method uses five input parameters, namely, remaining energy, distance to BS, distance to its neighboring nodes, link quality, and node degree. The output fuzzy parameters used in this research are cluster size and the probability of becoming CHs. The proposed method is implemented in MATLAB. The simulation results show that F5NUCP performs well when compared with LEACH, DEEC, TEEN, and EAUCF in terms of network lifetime and reduced energy consumption.

Joint Base Station Clustering and Beamforming for Non-Orthogonal Multicast and Unicast Transmission With Backhaul Constraints

The demand for providing multicast services in cellular networks is continuously and fastly increasing. In this work, we propose a non-orthogonal transmission framework based on layered-division multiplexing (LDM) to support multicast and unicast services concurrently in cooperative multi-cell cellular networks with limited backhaul capacity. We adopt a two-layer LDM structure where the first layer is intended for multicast services, the second layer is for unicast services, and the two layers are superposed with different beamformers. Each user decodes the multicast message first, subtracts it, and then decodes its dedicated unicast message. We formulate a joint multicast and unicast beamforming problem with adaptive base station clustering that aims to maximize the weighted sum of the multicast rate and the unicast rate under per-BS power and backhaul constraints. To solve the problem, we first develop a branch-and-bound algorithm to find its global optimum. We then reformulate the problem as a sparse beamforming problem and propose a low-complexity algorithm based on convex-concave procedure. Simulation results demonstrate the significant superiority of the proposed LDM-based non-orthogonal scheme over orthogonal schemes in terms of the achievable multicast-unicast rate region.

Optimized Base-Station Cache Allocation for Cloud Radio Access Network With Multicast Backhaul

The performance of cloud radio access network (C-RAN) is limited by the finite capacities of the backhaul links connecting the centralized processor (CP) with the base-stations (BSs), especially when the backhaul is implemented in a wireless medium. This paper proposes the use of wireless multicast together with BS caching, where the BSs pre-store contents of popular files, to augment the backhaul of C-RAN. For a downlink C-RAN consisting of a single cluster of BSs and wireless backhaul, this paper studies the optimal cache size allocation strategy among the BSs and the optimal multicast beamforming transmission strategy at the CP such that the user's requested messages are delivered from the CP to the BSs in the most efficient way. We first state a multicast backhaul rate expression based on a joint cache-channel coding scheme, which implies that larger cache sizes should be allocated to the BSs with weaker channels. We then formulate a two-timescale joint cache size allocation and beamforming design problem, where the cache is optimized offline based on the long-term channel statistical information, while the beamformer is designed during the file delivery phase based on the instantaneous channel state information. By leveraging the sample approximation method and the alternating direction method of multipliers (ADMM), we develop efficient algorithms for optimizing cache size allocation among the BSs, and quantify how much more cache should be allocated to the weaker BSs. We further consider the case with multiple files having different popularities and show that it is in general not optimal to entirely cache the most popular files first. Numerical results show considerable performance improvement of the optimized cache size allocation scheme over the uniform allocation and other heuristic schemes.

Deep mobile traffic forecast and complementary base station clustering for C-RAN optimization

The increasingly growing data traffic has posed great challenges for mobile operators to increase their data processing capacity, which incurs a significant energy consumption and deployment cost. With the emergence of the Cloud Radio Access Network (C-RAN) architecture, the data processing units can now be centralized in data centers and shared among base stations. By mapping a cluster of base stations with complementary traffic patterns to a data processing unit, the processing unit can be fully utilized in different periods of time, and the required capacity to be deployed is expected to be smaller than the sum of capacities of single base stations. However, since the traffic patterns of base stations are highly dynamic in different time and locations, it is challenging to foresee and characterize the traffic patterns in advance to make optimal clustering schemes. In this paper, we address these issues by proposing a deep-learning-based C-RAN optimization framework. First, we exploit a Multivariate Long Short-Term Memory (MuLSTM) model to learn the temporal dependency and spatial correlation among base station traffic patterns, and make accurate traffic forecast for a future period of time. Afterwards, we build a weighted graph to model the complementarity of base stations according to their traffic patterns, and propose a Distance-Constrained Complementarity-Aware (DCCA) algorithm to find optimal base station clustering schemes with the objectives of optimizing capacity utility and deployment cost. We evaluate the performance of our framework using data in two months from real-world mobile networks in Milan and Trentino, Italy. Results show that our method effectively increases the average capacity utility to 83.4% and 76.7%, and reduces the overall deployment cost to 48.4% and 51.7% of the traditional RAN architecture in the two datasets, respectively, which consistently outperforms the state-of-the-art baseline methods.

Effects of Base-Station Spatial Interdependence on Interference Correlation and Network Performance

The spatial-and-temporal correlation of interference has been well-studied in Poisson networks, where the interfering base stations (BSs) are independent of each other. However, there exists spatial interdependence including attraction and repulsion among the BSs in practical wireless networks, affecting the interference distribution and hence the network performance. In view of this, by modeling the network as a Poisson clustered process, we quantify the effects of spatial interdependence among BSs on the interference correlation and analytically prove that BS clustering increases the level of interference correlation. In particular, it is shown that the level is a monotone-increasing function of the mean number of BSs in each cluster and a monotone-decreasing function of cluster radius, but is independent of the locations of the clusters. Furthermore, we study the effects of spatial interdependence among BSs on network performance with Type-I HARQ retransmission scheme via considering heterogeneous cellular networks in which small-cell BSs exhibit a clustered topology in practice. We derive the numerically integrable expressions and their bounds for the joint success probabilities, defined as the success probability in multiple successive transmissions, for macro-cell users and small-cell users. It is shown that BS clustering improves the performance of macro-cell users. Further, the level is enhanced by the repulsion between the BSs from different tiers.

Wireless resource management mechanism with green communication for multimedia streaming

In the face of massive parallel multimedia streaming and user access, multimedia servers are often in an overload state, resulting in the delay of service response and the low utilization of wireless resources, which makes it is difficult to satisfy the user experience quality. Aiming at the problems of low utilization rate of multimedia communication resources and large computing load of servers, this paper proposes a self management mechanism and architecture of wireless resources based on multimedia flow green communication. First, based on the combination of multimedia server, relay base station and user cluster, a multimedia green communication system architecture is built based on the comprehensive utilization rate of multimedia communication, and a cluster green communication control algorithm is proposed. Secondly, aiming at the dynamic service demand and asynchronous multimedia communication environment, aiming at ensuring the balance of resource allocation and accelerating the speed of resource allocation, we build a dynamic multimedia wireless resource architecture. Finally, the experimental results of statistics and analysis, from the server in different scale parallel multimedia streams under different scale delay, number of users relay network free resources proportion, user satisfaction, packet loss rate and other performance show that the proposed algorithm is effective and feasible.

Joint Optimization of Beamforming and Base Station Clustering Based on Leakage Rate

It has been proved that jointly designing the base station (BS) clusters and the beamformers performs better than designing separately in the heterogeneous wireless networks. In this letter, to make a tradeoff between the improvement of sum rate and the interference suppression, the joint optimization problem is formulated based on a new measurement named leakage rate and a per-user iterative algorithm is proposed to solve the objective problem. First, a leakage channel model using the leakage signal as the additive interference is defined. And a sum leakage rate maximization problem over the leakage channel is formulated with the limitation of BS transmit power and the sparse structure of beamformers. Second, we design a novel per-user iterative algorithm, which only iterates within each single cell. Compared with the capacity maximization method, simulation results indicate that our proposed algorithm can achieve the better performance of interference suppression with a small fraction of capacity loss.

Traffic-Aware Energy-Saving Base Station Sleeping and Clustering in Cooperative Networks

We consider energy efficient base station (BS) sleeping and clustering problems in cooperative cellular networks, where clusters of base stations jointly transmit to users. Our key idea of energy saving is to exploit spatio-temporal fluctuation of traffic demand, and use minimal energy to provide achievable data rate only slightly greater than varying traffic demand. However, it is highly challenging to design traffic-aware algorithms without the future traffic demand information. To overcome this difficulty, we develop joint BS sleeping and clustering algorithms using queue instead of the future traffic information. The queue length information captures spatio-temporal mismatch between traffic demand and offered data rate. For BS clustering problem, we propose an optimal algorithm under given BS sleep mode state that has polynomial complexity. We integrate the optimal clustering solution into the sleeping problem, which is a complex combinatorial problem, and develop a joint optimal clustering and sleeping algorithm with reduced complexity compared to the exhaustive search. We also develop a greedy algorithm that finds a near-optimal clustering and sleeping solution with polynomial complexity. Through extensive simulations, we show that the proposed algorithms can save significant energy when traffic load is low.

Resource Allocation for CoMP in Cellular Networks With Base Station Sleeping

Base station sleeping (BSS) can result in significant reduction in energy consumption of cellular networks during low traffic conditions. We show that the coverage loss due to BSS can be compensated via coordinated multi-point (CoMP) -based transmission in a cluster of base stations. For a BSS with CoMP-based system, we propose various BSS patterns to achieve suitable trade-offs between energy savings and throughput. We formulate the CoMP resource allocation and $\alpha -$ Fair user scheduling as a joint optimization problem. We derive the optimal time fraction and user scheduling for this problem and use it to formulate a simplified BSS with CoMP optimization problem. A heuristic that solves this problem is presented. Through extensive simulations, we show that suitable trade-offs among energy, coverage, and rate can be achieved by appropriately selecting the BSS pattern, CoMP cluster, and rate threshold.