text: 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.
  @cite_26: 
  @cite_1: Cell size in cellular networks is in general fixed based on the estimated traffic load. However, the traffic load can have significant spatial and temporal fluctuations, which bring both challenges and opportunities to the planning and operating of cellular networks. This article introduces a concept of cell zooming, which adaptively adjusts the cell size according to traffic load, user requirements and channel conditions. The implementation issues of cell zooming are then presented. Finally a usage case of cell zooming for energy saving is investigated. Centralized and distributed cell zooming algorithms are developed, and simulation results show that the proposed algorithms can greatly reduce the energy consumption, which leads to green cellular networks.
  @cite_5: 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.
  @cite_16: Reducing the energy consumption of a wireless cellular network is an important and urgent problem. This paper studies the effect of cell sizes on the energy consumed by the network, assuming base station technologies of today and the future. Making cell sizes too small or too large can significantly increase energy consumption. We show that the optimal cell size from an energy perspective depends on a number of factors, including base station technology, data rates, and traffic demands. Given that traffic varies significantly during a day, dynamically adjusting cell sizes can help reduce energy consumption. We propose a practical, 2-level scheme that adjusts cell sizes between two fixed values, and show an energy saving of up to 40 . The paper also proposes some self-organizing techniques to allow this dynamic cell size adjustment.
  @cite_10: 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.
  @cite_17: In this paper we study with simple analytical models the energy-aware management of cellular access networks, trying to characterize the amount of energy that can be saved by reducing the number of active cells during the periods when they are not necessary because traffic is low. When some cells are switched off, radio coverage and service provisioning are taken care of by the cells that remain active, so as to guarantee that service is available over the whole area. We show how to optimize the energy saving, first assuming that any fraction of cells can be turned off, and then accounting for the constraints resulting from the cell layout.
summary: The performance of BS sleeping has been analyzed in several algorithmic and simulation-centric studies with different scenarios, assumptions, and network parameters (see @cite_17 @cite_16 @cite_1 @cite_26 @cite_10 @cite_5 and references therein). In @cite_17 , optimal energy savings are calculated as a function of the daily traffic pattern while ignoring the wireless link factors. In @cite_16 , network energy consumption is minimized by adjusting the cell sizes when BS sleeping is performed. Nevertheless, the transmit power required by the active BSs to provide coverage to sleeping cell users, i.e., effect of cell-zooming is ignored in the energy efficiency analysis @cite_17 @cite_16 . Other interesting studies optimize network utility functions, e.g., minimize energy expenditure by optimizing the BS activity with traffic load constraints @cite_10 and minimize a weighted combination of power consumption and delay to optimize the service rate @cite_5 .