Small‐cell planning in LTE HetNet to improve energy efficiency

Cell planning is one essential operation in wireless networks, and it significantly affects system performance and cost. Many research efforts consider the cell planning problem with identical base stations (BSs) or to construct a new network on the region without any infrastructure. However, long‐term evolution (LTE) adopts heterogeneous network, which allows operators to tactically deploy small cells to enhance signal coverage and improve performance. It thus motivates us to propose a small‐cell planning problem by adaptively adding low‐powered BSs with the limitation of budget to an existing network to increase its energy efficiency, which is defined by the ratio of network throughput to the amount of energy consumption of BSs. We consider 2 types of LTE small cells, namely, microcells and picocells, and develop different clustering strategies to deploy these cells. Based on the available resource and traffic demand in each cell, we then adjust the transmitted power of the deployed BS with energy concern. Experimental results demonstrate that our small‐cell planning solution can achieve high‐energy efficiency of LTE networks, which means that BSs can better use their transmitted energy to satisfy the traffic demands of user devices. This paper contributes in proposing a practical problem for cell planning with heterogeneous network consideration and developing an efficient solution to provide green communications.     

Energy‐efficient cluster division for multi‐cell joint transmission technology

Coordinated Multi‐Point (CoMP) is an effective way to improve user performance in next‐generation wireless cellular networks, such as 3GPP LTE‐Advanced(LTE‐A). The base station cooperation can reduce interference, and increase the signal to interference and noise ratio (SINR) of cell‐edge users and improve the system capacity. However, the base station cooperation also adds additional power consumption for signal processing and sharing information through back‐haul links between cooperative base stations. As such, CoMP may potentially consume more energy. This paper studies such energy consumption issue in CoMP, presents a semi‐dynamic CoMP cluster division algorithm based on energy efficiency (SCCD‐EE) that can effectively adapt to users' real‐time interference, and employs the idea of Maximal Independent Set (MIS) to solve the problem of cluster overlapping. To verify the feasibility of the proposed algorithm, this paper performs comprehensive evaluations in terms of energy efficiency and system capacity. The simulation results show that the proposed semi‐dynamic cluster division algorithm can not only improve the system capacity and the quality of service (QoS) of cell‐edge users, but also achieve higher network energy efficiency compared with static cluster methods and Non‐CoMP approaches. Copyright © 2016 John Wiley & Sons, Ltd.     

Energy efficiency based joint cell selection and power allocation scheme for HetNets

Heterogeneous networks (HetNets) composed of overlapped cells with different sizes are expected to improve the transmission performance of data service significantly. User equipments (UEs) in the overlapped area of multiple cells might be able to access various base stations (BSs) of the cells, resulting in various transmission performances due to cell heterogeneity. Hence, designing optimal cell selection scheme is of particular importance for it may affect user quality of service (QoS) and network performance significantly. In this paper, we jointly consider cell selection and transmit power allocation problem in a HetNet consisting of multiple cells. For a single UE case, we formulate the energy efficiency of the UE, and propose an energy efficient optimization scheme which selects the optimal cell corresponding to the maximum energy efficiency of the UE. The problem is then extended to multiple UEs case. To achieve joint performance optimization of all the UEs, we formulate an optimization problem with the objective of maximizing the sum energy efficiency of UEs subject to QoS and power constraints. The formulated nonlinear fractional optimization problem is equivalently transformed into two subproblems, i.e., power allocation subproblem of each UE-cell pair, and cell selection subproblem of UEs. The two subproblems are solved respectively through applying Lagrange dual method and Kuhn–Munkres (K-M) algorithm. Numerical results demonstrate the efficiency of the proposed algorithm.     

Green cell association for multimedia transmission in cognitive heterogeneous networks

With the introduction of low‐powered pico/femto‐base stations and relay nodes into the macro‐cell, recent heterogeneous networks provide an attractive approach for future wireless communication. Although it may achieve better coverage and higher capacity, several problems remain unsolved before practical deployment. For example, how to select the proper cell from neighbor low‐powered cells and then occupy the radio resource without interference on macro‐users is both important and challenging, especially for rigorous multimedia applications. The traditional cell access algorithms and quality‐control parameters such as delay or throughput no longer suit well in this complex environment. An effective approach should be pursued. In this paper, we investigate this interesting cell association problem and propose a complete green resolution on the basis of thorough discussions about the multimedia transmission under these concerns. Cognitive radio is introduced to share spectrum between macro‐cell and low‐powered cells while securing the transmission of authorized macro‐users. We also bring forth the concept of ‘interference balance’ to better manage the overall interference and energy consumption in the network. Restless bandit model is formulated on the basis of channel state, data rate, interference control, and the carefully chosen intra‐refreshing rate for multimedia traffic. Then the cell association scheme is designed to be efficient and practical because of the simple index property of our model output. Simulation results have proven the performance of our proposed resolution compared with existing algorithms on interference constraint, multimedia distortion, and overall network energy consumption balance. Copyright © 2013 John Wiley & Sons, Ltd.     

A resource allocation and cell association algorithm for energy efficiency in HetNets

Aiming at further improving the energy efficiency of system while meeting the data rate requirement of the users, in this paper, a network energy efficiency optimization problem by jointly optimizing the resource allocation of macrocells and small cells is formulated and proven to be an NP‐hard problem, which cannot be worked out by direct methods in polynomial time. Considering the effectiveness of the modified particle swarm optimization (MPSO) algorithm, an MPSO‐based resource allocation and cell association algorithm is employed to solve the proposed joint optimization problem where the frequency resource partitioning of the macrocells, the transmission power, and the cell‐association bias of the small cells are jointly optimized. During the implementation of the optimization algorithm, the cell association alters with the transmission power and cell‐association bias adjustment, and the small cells where there are no active users associating with them will be turned off. The data rate requirement of the users is an indispensable metric in the communication system. Taking no account of the data rate requirement of the users, the frequency resource partitioning and cell association alteration will deteriorate the data rate of a few of users. Under such circumstances, we take the users' data rate requirement as a constraint of the joint optimization problem. The system level simulation results show that, by jointly optimizing the resource allocation of macrocells and small cells while guaranteeing the users' data rate requirement, the energy efficiency and system throughput can be further improved, and energy consumption can be further reduced.     

An efficient and fair cooperative approach for resource management in wireless networks

In cellular networks, the implementation of various resource management processes, such as bandwidth reservation and location updates, has been based on the one‐to‐one resource management information exchange paradigm, between the mobile nodes and the base stations. In this paper, we design and demonstrate the use of a distributed cooperative scheme that can be applied in the future wireless networks to improve the energy consumption for the routine management processes of mobile terminals, by adopting the peer‐to‐peer communication concept of wireless ad hoc networks. In our approach, the network is subdivided into one‐hop ad hoc clusters where the members of each cluster cooperate to perform the required management functions, and conventional individual direct report transmissions of the mobile terminals to the base stations are replaced by two‐hop transmissions. The performance evaluation and the corresponding numerical results presented in this paper confirm that our proposed scheme reduces significantly the overall system energy consumption when compared with the conventional one‐to‐one direct information management exchange approach. Furthermore the issue of fairness in dynamically selecting the various cluster heads in successive operational cycles of the proposed scheme is analyzed, and an enhanced algorithm is proposed and evaluated, which improves significantly the cluster head selection fairness, in order to balance the energy consumption among the various mobile terminals. Copyright © 2005 John Wiley & Sons, Ltd.     

Load-balance cell association for improving network capacity in heterogeneous cellular networks

Heterogeneous cellular networks improve the spectrum efficiency and coverage of wireless communication networks by deploying low power base station （BS） overlapping the conventional macro cell. But due to the disparity between the transmit powers of the macro BS and the low power BS, cell association strategy developed for the conventional homogeneous networks may lead to a highly unbalanced traffic loading with most of the traffic concentrated on the macro BS. In this paper, we propose a load-balance cell association scheme for heterogeneous cellular network aiming to maximize the network capacity. By relaxing the association constraints, we can get the upper bound of optimal solution and convert the primal problem into a convex optimization problem. Furthermore we propose a Lagrange multipliers based distributed algorithm by using Lagrange dual theory to solve the convex optimization, which converges to an optimal solution with a theoretical performance guarantee. With the proposed algorithm, mobile terminals （MTs） need to jointly consider their traffic type, received signal-to-interference-noise-ratios （SINRs） from BSs, and the load of BSs when they choose server BS. Simulation results show that the load balance between macro and pico BS is achieved and network capacity is improved significantly by our proposed cell association algorithm.     

A distributed implementation of mobility load balancing with power control and cell reselection in 4G network

We consider a self‐organizing network (SON) capability of mobility load balancing in a 4G network, which determines the transmission power level for individual base stations and cell reselection for individual mobile stations such that the network‐wide load is minimized while satisfying the minimum signal‐to‐noise and interference ratio (SINR) requirement of individual users. Both centralized and distributed schemes are proposed. The centralized scheme is based on the greedy algorithm, serving as a performance bound to the distributed scheme. The distributed scheme is to solve the system‐wide optimization problem in the flat network model, i.e. no central control node. Furthermore, it requires relatively low inter‐cell exchange information among neighboring cells over an inter‐cell channel, e.g. X2 interface in the LTE network. The proposed design objective is to minimize the number of mobile users that do not satisfy the specified average throughput, while distributing the user traffic load as uniformly as possible among the neighboring cells. Our simulation results for a uniform user distribution demonstrate that the proposed scheme can achieve up to almost 80% of a load balancing gain that has been achieved by a greedy algorithm in the centralized optimization. Copyright © 2014 John Wiley & Sons, Ltd.     

Two‐sided matching framework for optimal user association in 5G multi‐RAT UDNs

Recently, academic and industrial research communities are paying more explicit attention to the 5G multiple radio access technology ultra‐dense networks (5G multi‐RAT UDNs) for boosting network capacity, especially in UD urban zones. To this aim, in this paper, we intend to tackle the user association problem in 5G multi‐RAT UDNs. By considering the decoupled uplink/downlink access (DUDA), we divide our user association problem into two distinct subproblems representing, respectively, the uplink and the downlink channels. Next, we formulated each one as a nonlinear optimization problem with binary variables. Then, to solve them, we were restricted by the hard complexity, as well as the hard feasibility of centralized user association schemes. Thus, to resolve our user association problem in a reasonable time and distributed manner, we formulated each subproblem as a many‐to‐one matching game based on matching theory. Next, we provide two fully distributed association algorithms to compute the uplink and downlink stable matching among user equipments (UEs) and base stations (BSs). Simulation results corroborate our theoretical model and show the effectiveness and improvement of our achieved results in terms of the overall network performance, quality of service (QoS), and energy efficiency (EE) of UEs.     

Coverage and Energy Modeling of HetNet Under Base Station On‐Off Model

Small cell networks, as an important evolution path for next‐generation cellular networks, have drawn much attention. Different from the traditional base stations (BSs) always‐on model, we proposed a BSs on‐off model, where a new, simple expression for the probabilities of active BSs in a heterogeneous network is derived. This model is more suitable for application in practical networks. Based on this, we develop an analytical framework for the performance evaluation of small cell networks, adopting stochastic geometry theory. We derive the system coverage probability; average energy efficiency (AEE) and average uplink power consumption (AUPC) for different association strategies; maximum biased received power (MaBRP); and minimum association distance (MiAD). It is analytically shown that MaBRP is beneficial for coverage but will have some loss in energy saving. On the contrary, MiAD is not advocated from the point of coverage but is more energy efficient. The simulation results show that the use of range expansion in MaBRP helps to save energy but that this is not so in MiAD. Furthermore, we can achieve an optimal AEE by establishing an appropriate density of small cells.     

On the macroscopic optimization of multicell wireless systems with multiuser detection and multiple antennas - uplink analysis

In this paper, we consider a system with K single-antenna client users, n/sub B/ base stations (each base station has n/sub R/ antennas), as well as a centralized controller. A client user could be associated with a single base station at any time. All the base stations operate at the same frequency and have optimal multiuser detection per base station which cancels intracell interference only. We consider a general problem of uplink macroscopic resource management where the centralized controller dynamically determines an appropriate association mapping of the K users with respect to the n/sub B/ base stations over a macroscopic time scale. We propose a novel analytical framework for the above macroscopic scheduling problems. A simple rule is to associate a user with the strongest base station (camp-on-the-strongest-cell), and this has been widely employed in conventional cellular systems. However, based on the optimization framework, we found that this conventional approach is in fact not optimal when multiuser detection is employed at the base station. We show that the optimal macroscopic scheduling algorithm is of exponential complexity, and we propose a simple greedy algorithm as a feasible solution.     

Game of energy consumption balancing in heterogeneous sensor networks

In a multi‐hop sensor network, sensors largely rely on other nodes as a traffic relay to communicate with targets that are not reachable by one hop. Depending on the topology and position of nodes, some sensors receive more relaying traffic and lose their energy faster. Such imbalanced energy consumption may lead to server problems like network partitioning. In this paper, we study the problem of energy consumption balancing (ECB) in heterogeneous sensor networks by assuming general any‐to‐any traffic pattern. We consider both factors of transmission power and forwarding load in measuring energy consumption. To find a solution, we formulate the problem as a strategic network formation game with a new utility function. We show that this game is guaranteed to converge to strongly connected topologies which have better ECB and bounded inefficiency. We propose a localized algorithm in which every node knows only about its k‐hop neighbourhood. Through simulations on uniform and clustered networks with various densities, we show that the performance of our algorithm is comparable with global and centralized algorithms. Copyright © 2015 John Wiley & Sons, Ltd.     

QoS约束下多层异构蜂窝网中基于分层休眠的节能机制研究

针对多层异构蜂窝网中资源分配和节能问题,本文基于随机几何模型利用分布式配对算法实现了用户连接和资源分配的联合优化,并利用分层休眠机制提升了系统的整体能效。首先,采用随机几何工具对不同类型的基站进行建模,在建立模型的基础上提出用户QoS约束下的最小化系统总功耗的联合优化方案,然后将该方案分解简化为用户连接资源分配和基站休眠两个子问题,并分别利用稳定配对算法和基于投票法的分层休眠机制加以解决。仿真和分析结果表明本文方法可以显著提升系统能效,与已有的休眠策略相比具有更低的用户平均中断比。      

Energy-efficient placement and sleep control of relay nodes in heterogeneous small cell networks

Small cell (SC) provides low-power radio access with a relatively smaller coverage range than a macrocell. It has been regarded as a key solution for offloading traffic in the future 5G system. Previous works on energy-efficient placement of base stations (BSs) in cellular networks introduced placement and sleep control of low-power relay stations (RSs) to reduce the total power consumption for downlink transmission from BSs to users. As compared with legacy RSs, SC can serve as an RS with a larger coverage range though consuming a bit more circuit power. Hence, this work additionally considers to deploy small cells (SCs) in a one-dimensional highway cellular network with BSs and legacy RSs, and further models the power consumption minimization problem for placement and sleep control of legacy RSs and SCs in this network. Since the problem is hard to be solved analytically, a genetic algorithm with dynamic operator-selection mechanism is further proposed to resolve this problem. Performance of the proposed algorithm is evaluated via simulation with a practical experimental parameter setting.     

Network planning for mobile multi‐hop relay networks

In this paper, the coverage problem of network planning in mobile multi‐hop relay networks is defined on the basis of integer linear programming. In order to provide desired utilities and also meet deployment limitations for network planning, we propose a supergraph tree algorithm to place base stations and relay stations at the lowest cost position. Furthermore, another algorithm for avoiding the interference between base stations, which is called interference aware tree algorithm is also proposed. Both the proposed algorithms are formulated on the basis of a graph theoretic technique and analyzed in the simulation results. The results show that the supergraph tree algorithm provides the lowest construction cost with different network scenarios, and the interference aware tree algorithm provides the highest communication quality for mobile multi‐hop relay infrastructure‐based communication network planning. Copyright © 2013 John Wiley & Sons, Ltd.     

Distributed fuzzy approach to unequal clustering and routing algorithm for wireless sensor networks

Due to inherent issue of energy limitation in sensor nodes, the energy conservation is the primary concern for large‐scale wireless sensor networks. Cluster‐based routing has been found to be an effective mechanism to reduce the energy consumption of sensor nodes. In clustered wireless sensor networks, the network is divided into a set of clusters; each cluster has a coordinator, called cluster head (CH). Each node of a cluster transmits its collected information to its CH that in turn aggregates the received information and sends it to the base station directly or via other CHs. In multihop communication, the CHs closer to the base station are burdened with high relay load; as a result, their energy depletes much faster as compared with other CHs. This problem is termed as the hot spot problem. In this paper, a distributed fuzzy logic‐based unequal clustering approach and routing algorithm (DFCR) is proposed to solve this problem. Based on the cluster design, a multihop routing algorithm is also proposed, which is both energy efficient and energy balancing. The simulation results reinforce the efficiency of the proposed DFCR algorithm over the state‐of‐the‐art algorithms, ie, energy‐aware fuzzy approach to unequal clustering, energy‐aware distributed clustering, and energy‐aware routing algorithm, in terms of different performance parameters like energy efficiency and network lifetime.     

5G网络中基于覆盖区域优先级的微基站休眠算法

随着5G移动通信技术日渐成熟，移动终端数量快速增长，5G无线通信系统基站密集能耗问题突出，提出一种微基站区域分级休眠算法。考虑微基站负载、站间距离、层间配合对微基站休眠的影响，宏基站与宏基站之间重叠覆盖区域中微基站状态转换次数少，优先休眠操作节能效果好。仿真结果表明，节能率为23%，能适应不同的网络规模，在大规模网络中节能效果更优越。     

A novel residual energy‐based distributed clustering and routing approach for performance study of wireless sensor network

Non‐uniform energy consumption during operation of a cluster‐based routing protocol for large‐scale wireless sensor networks (WSN) is major area of concern. Unbalanced energy consumption in the wireless network results in early node death and reduces the network lifetime. This is because nodes near the sink are overloaded in terms of data traffic compared with the far away nodes resulting in node deaths. In this work, a novel residual energy–based distributed clustering and routing (REDCR) protocol has been proposed, which allows multi‐hop communication based on cuckoo‐search (CS) algorithm and low‐energy adaptive‐clustering–hierarchy (LEACH) protocol. LEACH protocol allows choice of possible cluster heads by rotation at every round of data transmission by a newly developed objective function based on residual energy of the nodes. The information about the location and energy of the nodes is forwarded to the sink node where CS algorithm is implemented to choose optimal number of cluster heads and their positions in the network. This approach helps in uniform distribution of the cluster heads throughout the network and enhances the network stability. Several case studies have been performed by varying the position of the base stations and by changing the number of nodes in the area of application. The proposed REDCR protocol shows significant improvement by an average of 15% for network throughput, 25% for network scalability, 30% for network stability, 33% for residual energy conservation, and 60% for network lifetime proving this approach to be more acceptable one in near future.     

A theoretical framework of uplink macroscopic optimization for multicell systems with multiuser detection

In this paper, we consider a system with K client users (single antenna), n/sub B/ base stations (each has single antenna), as well as a centralized controller. All the base stations operate at the same frequency and have optimal multiuser detection (MUD) per base station. The MUD at the base station is able to cancel only the intracell interference but not the intercell interference. A client user is associated with a single base station at any time. We consider a general problem of uplink macroscopic optimization (or macroscopic resource management) where the centralized controller dynamically determines an appropriate association mapping of the K users with respect to the n/sub B/ base stations over a macroscopic time scale. We propose a novel multicell capacity region as well as an analytical framework for the above macroscopic optimization problem. A simple conventional rule is to associate a user with the strongest base station (camp-on-the-strongest-cell) and this has been widely employed in conventional cellular systems. However, based on the optimization framework, we found that this conventional approach is in fact not optimal when MUD is employed at the base station. We show that the optimal macroscopic optimization algorithm is of exponential complexity and we propose a simple greedy algorithm as a feasible solution. It is shown that the macroscopic optimization gain over the conventional approach increases with decreasing path loss exponent due to large area of overlapping.     

Traffic analysis of heterogeneous mobile cellular networks using “wrap-up” cell structure

In this paper, we develop a tool for evaluating performance of a heterogeneous mobile network under different traffic conditions. The traffic condition is specified in a cluster of seven heterogeneous base stations, and “wrap-up” cell structure is applied to account for the traffic that crosses the boundary of the cluster. The performance of the network is specified in metrics which can be computed using the algorithm developed. These metrics relate to call rejection, i.e. the blocking of new calls, and the termination of handoff calls, as well as the carried traffic i.e. the expected number of ongoing calls in a station. These metrics are either measured for a single base station in a cluster, or measured for the average over the whole cluster. We apply the algorithm to some hypothetical data and present the numerical results. We also consider approximating a heterogeneous model with a simpler homogeneous model. Through some numerical results, we demonstrate the magnitude of errors that could result in the approximation.