Base station (BS) sleeping has emerged as a viable solution to enhance the overall network energy efficiency by inactivating the underutilized BSs. However, it affects the performance of users in sleeping cells depending on their BS association criteria, their channel conditions toward the active BSs, and scheduling criteria and traffic loads at the active BSs. This paper characterizes the performance of cellular systems with BS sleeping by developing a systematic framework to derive the spectral efficiency and outage probability of downlink transmission to the sleeping cell users taking into account the aforementioned factors. In this context, a user association scheme is also developed in which sleeping cell users associate to a BS with maximum mean channel access probability (MMAP). The MMAP-based user association scheme adapts according to traffic load and scheduling criteria at the active BSs. We consider greedy and round-robin schemes at active BSs for scheduling users in a channel. We also derive the analytical results for the conventional maximum received signal power (MRSP)-based user association scheme. Finally, we derive the statistics of the received signal and interference power to evaluate the downlink spectral efficiency of a given sleeping cell user. Numerical results provide a comparison between two user association schemes as a function of system parameters and demonstrate the efficacy of the MMAP-based association scheme in non-uniform traffic load scenarios.
A distance-aware BS switching algorithm is presented in @cite_21 that recommends to switch-off a BS with maximum average distance from its own and neighboring cell users. In @cite_20 four heuristic-based BS switching-off patterns are proposed and the coordination among the BSs is exploited to provide coverage to the sleeping cell users.
@cite_21 @cite_20
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In this paper, we propose a switch on off algorithm for Base Stations (BSs), which exploits the knowledge of the distance between the User Equipments (UEs) and their associated BS. Our novel approach hopes to provide an improvement to the problem of energy consumption. The major concern lies on reducing the energy consumption of the telecommunication networks by optimizing the power utilization without sacrificing the offered Quality of Service (QoS). Our proposed scheme achieves a significant power saving, based on switching off the Base Stations that are underutilized during low traffic periods (especially during night) in the LTE-Advanced. The energy consumption of the information and communication technology (ICT) industry, which has become a serious problem, is mostly due to the network infrastructure rather than the mobile terminals. In this paper, we focus on reducing the energy consumption of base stations (BSs) by adjusting their working modes (active or sleep). Specifically, the objective is to minimize the energy consumption while satisfying quality of service (QoS, e.g., blocking probability) requirement and, at the same time, avoiding frequent mode switching to reduce signaling and delay overhead. The problem is modeled as a dynamic programming (DP) problem, which is NP-hard in general. Based on cooperation among neighboring BSs, a low-complexity algorithm is proposed to reduce the size of state space as well as that of action space. Simulations demonstrate that, with the proposed algorithm, the active BS pattern well meets the time variation and the non-uniform spatial distribution of system traffic. Moreover, the tradeoff between the energy saving from BS sleeping and the cost of switching is well balanced by the proposed scheme. In this paper, we investigate dynamic base station (BS) switching to reduce energy consumption in wireless cellular networks. Specifically, we formulate a general energy minimization problem pertaining to BS switching that is known to be a difficult combinatorial problem and requires high computational complexity as well as large signaling overhead. We propose a practically implementable switching-on off based energy saving (SWES) algorithm that can be operated in a distributed manner with low computational complexity. A key design principle of the proposed algorithm is to turn off a BS one by one that will minimally affect the network by using a newly introduced notion of network-impact, which takes into account the additional load increments brought to its neighboring BSs. In order to further reduce the signaling and implementation overhead over the air and backhaul, we propose three other heuristic versions of SWES that use the approximate values of network-impact as their decision metrics. We describe how the proposed algorithms can be implemented in practice at the protocol-level and also estimate the amount of energy savings through a first-order analysis in a simple setting. Extensive simulations demonstrate that the SWES algorithms can significantly reduce the total energy consumption, e.g., we estimate up to 50-80 potential savings based on a real traffic profile from a metropolitan urban area. In this paper, traffic-aware sleeping control (SC) and power matching (PM) of a single base station (BS) in cellular networks are studied. The objective is to find the sleeping control and power matching configurations that achieve the Pareto optimal tradeoff between total power consumption and average delay. Two types of sleeping control schemes are considered: The BS goes to sleep whenever there is no active user, and wakes up when N users are assembled or after a period of multiple or single vacation time. We first discuss when to incorporate sleeping control into power matching energy efficiently. The explicit relationship between total power consumption and average delay with varying service rate is analyzed theoretically, indicating that sacrificing delay cannot always be traded for energy saving, and we also provide conditions under which the energy-optimal rate exists. Moreover, the optimal pair of sleeping parameter and service rate to achieve the optimal energy-delay tradeoff, and the energy consumption lower bound are also derived. Both the analytical and simulation results show that tolerable sacrifice of delay performance can be traded for substantial amount of energy saving given that careful designs were made according to our analysis. Reducing the energy consumption (EC) of base station (BS) is one of the major concerns in wireless cellular networks. Additionally, turning off some underutilized BSs during off-peak period and performing effective compensation without delay are the most efficient way to save energy. However, large-scale energy conservation yet remains to be investigated at macro level. In this paper, to solve the problem that long convergence time and poor convergence precision in the large-scale network, we propose a BS topology-aware based energy-saving (ES) model, whose core is cell adjacency graph (CAG) with vertexes and links representing eNodeBs (eNBs) and their neighboring relationship. In addition, we introduce new metrics, predicted energy efficiency (PEE) and quality of compensation (QoC), as the weights of nodes and links respectively. Consequently, the model transforms the ES problem into average weights maximization in CAG. In view of the model presented, centralized and hybrid algorithms are put forward to solve the problem. Compared with classic distributed algorithm, simulation results claim that our hybrid approach achieves the maximization of ES with guaranteed QoC while our centralized approach maximize the PEE. Energy-efficiency, one of the major design goals in wireless cellular networks, has received much attention lately, due to increased awareness of environmental and economic issues for network operators. In this paper, we develop a theoretical framework for BS energy saving that encompasses dynamic BS operation and the related problem of user association together. Specifically, we formulate a total cost minimization that allows for a flexible tradeoff between flow-level performance and energy consumption. For the user association problem, we propose an optimal energy-efficient user association policy and further present a distributed implementation with provable convergence. For the BS operation problem (i.e., BS switching on off), which is a challenging combinatorial problem, we propose simple greedy-on and greedy-off algorithms that are inspired by the mathematical background of submodularity maximization problem. Moreover, we propose other heuristic algorithms based on the distances between BSs or the utilizations of BSs that do not impose any additional signaling overhead and thus are easy to implement in practice. Extensive simulations under various practical configurations demonstrate that the proposed user association and BS operation algorithms can significantly reduce energy consumption. This paper considers maximizing the network-wide minimum supported rate in the downlink of a two-cell system, where each base station (BS) is endowed with multiple antennas. This is done for different levels of cell cooperation. At one extreme, we consider single cell processing where the BS is oblivious to the interference it is creating at the other cell. At the other extreme, we consider full cooperative macroscopic beamforming. In between, we consider coordinated beamforming, which takes account of inter-cell interference, but does not require full cooperation between the BSs. We combine elements of Lagrangian duality and large system analysis to obtain limiting SINRs and bit-rates, allowing comparison between the considered schemes. The main contributions of the paper are theorems which provide concise formulas for optimal transmit power, beamforming vectors, and achieved signal to interference and noise ratio (SINR) for the considered schemes. The formulas obtained are valid for the limit in which the number of users per cell, K, and the number of antennas per base station, N, tend to infinity, with fixed ratio β = K N. These theorems also provide expressions for the effective bandwidths occupied by users, and the effective interference caused in the adjacent cell, which allow direct comparisons between the considered schemes. The integration of millimeter-wave base stations (mmW- BSs) with conventional microwave base stations (&#956;W-BSs) is a promising solution for enhancing the quality-of- service (QoS) of emerging 5G networks. However, the significant differences in the signal propagation characteristics over the mmW and &#956;W frequency bands will require novel cell association schemes cognizant of both mmW and &#956;W systems. In this paper, a novel cell association framework is proposed that considers both the blockage probability and the achievable rate to assign user equipments (UEs) to mmW-BSs or &#956;W-BSs. The problem is formulated as a one-to-many matching problem with minimum quota constraints for the BSs that provides an efficient way to balance the load over the mmW and &#956;W frequency bands. To solve the problem, a distributed algorithm is proposed that is guaranteed to yield a Pareto optimal and two-sided stable solution. Simulation results show that the proposed matching with minimum quota (MMQ) algorithm outperforms the conventional max-RSSI and max-SINR cell association schemes. In addition, it is shown that the proposed MMQ algorithm can effectively balance the number of UEs associated with the &#956;W-BSs and mmW-BSs and achieve further gains, in terms of the average sum rate. Using stochastic geometry, we develop a tractable uplink modeling paradigm for outage probability and spectral efficiency in both single and multi-tier cellular wireless networks. The analysis accounts for per user equipment (UE) power control as well as the maximum power limitations for UEs. More specifically, for interference mitigation and robust uplink communication, each UE is required to control its transmit power such that the average received signal power at its serving base station (BS) is equal to a certain threshold ρ o . Due to the limited transmit power, the UEs employ a truncated channel inversion power control policy with a cutoff threshold of ρ o . We show that there exists a transfer point in the uplink system performance that depends on the following tuple: BS intensity λ, maximum transmit power of UEs P u , and ρ o . That is, when P u is a tight operational constraint with respect to (w.r.t.) λ and ρ o , the uplink outage probability and spectral efficiency highly depend on the values of λ and ρ o . In this case, there exists an optimal cutoff threshold ρ o *, which depends on the system parameters, that minimizes the outage probability. On the other hand, when P u is not a binding operational constraint w.r.t. λ and ρ o , the uplink outage probability and spectral efficiency become independent of λ and ρ o . We obtain approximate yet accurate simple expressions for outage probability and spectral efficiency, which reduce to closed forms in some special cases. With the explosive deployment of the information and communications technology (ICT) infrastructures, the rising cost of energy and increased environmental awareness has sparked a keen interest in the development and deployment of energy-efficient communication technologies. As a major player in the ICT sector, the energy efficiency of the mobile cellular networks can be significantly improved by switching off some base stations during off-peak periods. In this paper, we propose an energy-efficient BS switching strategy, and use cooperative communication techniques among the base stations to effectively extend network coverage. We incorporate both the path-loss and fading effects in our system model, and derive closed-form expressions for two important quality of service metrics, the call-blocking probability and the channel outage probability. The proposed scheme guarantees the quality of service of the user equipments by identifying the user equipments situated at the worst-case locations. The energy-saving performance is evaluated and compared with the conventional uni-pattern operation. Both analytical and numerical results show that the proposed energy-efficient switching strategy, facilitated by BS cooperation, can provide significant energy-saving potential for the cellular networks with guaranteed quality of service. In this paper, the optimal BS (Base Station) density for both homogeneous and heterogeneous cellular networks to minimize network energy cost is analyzed with stochastic geometry theory. For homogeneous cellular networks, both upper and lower bounds of the optimal BS density are derived. For heterogeneous cellular networks, our analysis reveals the best type of BSs to be deployed for capacity extension, or to be switched off for energy saving. Specifically, if the ratio between the micro BS cost and the macro BS cost is lower than a threshold, which is a function of path loss and their transmit power, then the optimal strategy is to deploy micro BSs for capacity extension or to switch off macro BSs (if possible) for energy saving with higher priority. Otherwise, the optimal strategy is the opposite. The optimal combination of macro and micro BS densities can be calculated numerically through our analysis, or alternatively be conservatively approximated with a closed-form solution. Based on the parameters from EARTH, numerical results show that in the dense urban scenario, compared to the traditional macro-only homogeneous cellular network with no BS sleeping, deploying micro BSs can reduce about 40 of the total energy cost, and further reduce up to 35 with BS sleeping capability. Universal mobile telecommunication system (UMTS) networks should be deployed according to cost-effective strategies that optimize a cost objective and satisfy target quality-of-service (QoS) requirements. In this paper, we propose novel algorithms for joint uplink downlink UMTS radio planning with the objective of minimizing total power consumption in the network. Specifically, we define two components of the radio planning problem: 1) continuous-based site placement and 2) integer-based site selection. In the site-placement problem, our goal is to find the optimal locations of UMTS base stations (BSs) in a certain geographic area with a given user distribution to minimize the total power expenditure such that a satisfactory level of downlink and uplink signal-to-interference ratio (SIR) is maintained with bounded outage constraints. We model the problem as a constrained optimization problem with SIR-based uplink and downlink power control scheme. An algorithm is proposed and implemented using pattern search techniques for derivative-free optimization with augmented Lagrange multiplier estimates to support general constraints. In the site-selection problem, we aim to select the minimum set of BSs from a fixed set of candidate sites that satisfies quality and outage constraints. We develop an efficient elimination algorithm by proposing a method for classifying BSs that are critical for network coverage and QoS. Finally, the problem is reformulated to take care of location constraints whereby the placement of BSs in a subset of the deployment area is not permitted due to, e.g., private property limitations or electromagnetic radiation constraints. Experimental results and optimal tradeoff curves are presented and analyzed for various scenarios. We consider the interference management problem in a multicell MIMO heterogeneous network. Within each cell there is a large number of distributed micro pico base stations (BSs) that can be potentially coordinated for joint transmission. To reduce coordination overhead, we consider user-centric BS clustering so that each user is served by only a small number of (potentially overlapping) BSs. Thus, given the channel state information, our objective is to jointly design the BS clustering and the linear beamformers for all BSs in the network. In this paper, we formulate this problem from a sparse optimization perspective, and propose an efficient algorithm that is based on iteratively solving a sequence of group LASSO problems. A novel feature of the proposed algorithm is that it performs BS clustering and beamformer design jointly rather than separately as is done in the existing approaches for partial coordinated transmission. Moreover, the cluster size can be controlled by adjusting a single penalty parameter in the nonsmooth regularized utility function. The convergence of the proposed algorithm (to a stationary solution) is guaranteed, and its effectiveness is demonstrated via extensive simulation. Green communications has received much attention recently. For cellular networks, the base stations (BSs) account for more than 50 percent of the energy consumption of the networks. Therefore, reducing the power consumption of BSs is crucial to greening cellular networks. With the development of green energy technologies, BSs can be powered by green energy in order to reduce the on-grid power consumption, and subsequently reduce the carbon footprints. In this paper, we have proposed a distributed scheme to enable Green-energy Aware and Latency Aware (GALA) user-BS associations in heterogeneous cellular networks whose BSs are powered by both on-grid power and green energy. The GALA algorithm considers both the traffic delivery latency and the green energy generation rate in BSs for determining the user-BS association. We have proved that the GALA algorithm converges to the optimal solution which minimizes the summation of the weighted latency ratios of BSs. The simulation results show that the GALA algorithm enables the trade-off between the on-grid power consumption and the average traffic delivery latency, and saves a significant amount of on-grid power in heterogeneous cellular networks that are powered by both on-grid power and green energy. The issue of energy efficiency (EE) in Orthogonal Frequency-Division Multiple Access (OFDMA) wireless networks is discussed in this paper. Our interest is focused on the promising concept of base station (BS) sleep mode, introduced recently as a key feature in order to dramatically reduce network energy consumption. The proposed technical approach fully exploits the properties of stochastic geometry, where the number of active cells is reduced in a way that the outage probability, or equivalently the signal to interference plus noise (SINR) distribution, remains the same. The optimal EE gains are then specified with the help of a simplified but yet realistic BS power consumption model. Furthermore, the authors extend their initial work by studying a non-singular path loss model in order to verify the validity of the analysis and finally, the impact on the achieved user capacity is investigated. In this context, the significant contribution of this paper is the evaluation of the theoretically optimal energy savings of sleep mode, with respect to the decisive role that the BS power profile plays. In this paper, we investigate the downlink performance of dense cellular networks with elevated base stations (BSs) using a channel model that incorporates line-of-sight (LOS) non-line-of-sight (NLOS) propagation into both small-scale and large-scale fading. Modeling LOS fading with Nakagami- @math fading, we provide a unified framework based on stochastic geometry that encompasses both closest and strongest BS association. This paper is particularized to two distance-dependent LOS NLOS models of practical interest. Considering the effect of LOS propagation alone, we derive closed-form expressions for the coverage probability with Nakagami- @math fading, showing that the performance for strongest BS association is the same as in the case of Rayleigh fading, whereas for closest BS association it monotonically increases with the shape parameter @math . Then, focusing on the effect of elevated BSs, we show that network densification eventually leads to near-universal outage even for moderately low BS densities: in particular, the maximum area spectral efficiency is proportional to the inverse of the square of the BS height. Energy efficiency (EE) is becoming an important design goal for wireless communication systems providing high spectral efficiency (SE). Both massive multi-input multi-output (MIMO) and small cell network (SCN) are expected to achieve high EE for high throughput cellular networks, though using different mechanisms. Massive MIMO improves EE by exploiting a large array gain, while SCN improves EE by deploying a large number of low-power base stations (BSs) to reduce the propagation loss and increase the opportunity of BS sleep. In this paper, we compare the EEs as well as the SEs of Massive MIMO and SCN. For a fair comparison, we consider a multi-cell network with the same user density, antenna density and average cell-edge signal-to-noise-ratio (SNR). Perfect channel information is assumed, and three BS sleep strategies are considered. Our analysis shows that the EE of SCN increases with the cell size shrinking, and the achievable SEs of SCN and Massive MIMO increase with the cell-edge SNR. When the number of cells is large, SCN is always more energy efficient than Massive MIMO. On the other hand, when the number of cells is small, Massive MIMO achieves higher EE than SCN when the circuit power consumptions of Massive MIMO are much lower than SCN. Base station (BS) sleeping is an effective way to improve the energy-efficiency of cellular networks. However, it may bring extra user-perceived delay. We conduct a theoretical study into the impact of BS sleeping on both energy-efficiency and user-perceived delay. We consider hysteresis sleep and three typical wake-up schemes, namely single sleep, multiple sleep, and @math -limited schemes. We model the system as an @math vacation queue, which captures the setup time, the mode-changing cost, as well as the counting or detection cost during the sleep mode. Closed-form expressions for the average power and the Laplace–Stieltjes transform of delay distribution are obtained. The impacts of system parameters on these expressions are analyzed. We then formulate an optimization problem to design delay-constrained energy-optimal BS sleeping policies. We show that the optimal solutions possess a special structure, thereby allowing us to obtain them explicitly or numerically by simple bisection search. In addition, the relationship between the optimal power consumption and the mean delay constraint is analyzed, so as to answer the fundamental question: how much energy can be saved by trading off a certain amount of delay? It is shown that this optimal relationship is linear only when the delay constraint is lower than a threshold. Numerical studies are also conducted, where the impact of detection or counting cost during the sleep mode is explored, and the delay distribution under the optimal policy is obtained. In this work we propose a novel handover (HO) algorithm, that considers system performance from both user and network perspective, in the context of heterogeneous networks (HetNets), i.e., networks composed of BSs with asymmetrical transmission power. In such an environment, conventional HO algorithms that consider only the user perspective, e.g., received signal strength (RSS)-based, might offer suboptimal performance, since they mainly push users to cells with high transmission powers. Thus, new algorithms that take into account also the network perspective, e.g., cell load, are needed. In this work, a load-aware algorithm is proposed considering the service delay that a user experiences from the network. In addition, an implementable framework based on Software Defined Networking (SDN) architecture is sketched to support the algorithm. The proposed algorithm is compared with the traditional one we meet in long-term evolution (LTE) systems and a distance-based one. Extracted cell assignment probability and user service delay performance results show that the load-aware approach outperforms both of them. In small cell networks (SCNs) co-channel interference is an important issue, and necessitates the use of interference mitigation strategies that allocate resources efficiently. This work discusses a distributed utility-based algorithm for downlink resource allocation (i.e., power and scheduling weights per carrier) in multicarrier SCNs. The proposed distributed downlink resource allocation (DDRA) algorithm aims to maximize the sum utility of the whole system. To achieve this goal, each base station (BS) selects the resource allocation strategy to maximize a surplus function comprising both, own cell utility and interference prices (that reflect the interference that is caused to neighboring cells). Two different utility functions are considered: max-rate and proportional fair-rate. For performance evaluation, a SCN deployed in a single story WINNER office building is considered. Simulation results show that the proposed algorithm is effective in enhancing not only the sum data rate of a SCN, but also the degree of fairness in resource sharing among users.