SLEEP mode techniques for small cell deployments

With the exponential increase in mobile internet traffic driven by a new generation of wireless devices, future cellular networks face a great challenge to meet this overwhelming demand of network capacity. At the same time, the demand for higher data rates and the ever-increasing number of wireless users led to rapid increases in power consumption and operating cost of cellular networks. One potential solution to address these issues is to overlay small cell networks with macrocell networks as a means to provide higher network capacity and better coverage. However, the dense and random deployment of small cells and their uncoordinated operation raise important questions about the energy efficiency implications of such multi-tier networks. Another technique to improve energy efficiency in cellular networks is to introduce active/sleep (on/off) modes in macrocell base stations. In this paper, we investigate the design and the associated tradeoffs of energy efficient cellular networks through the deployment of sleeping strategies and small cells. Using a stochastic geometry based model, we derive the success probability and energy efficiency in homogeneous macrocell (single-tier) and heterogeneous K-tier wireless networks under different sleeping policies. In addition, we formulate the power consumption minimization and energy efficiency maximization problems, and determine the optimal operating regimes for macrocell base stations. Numerical results confirm the effectiveness of switching off base stations in homogeneous macrocell networks. Nevertheless, the gains in terms of energy efficiency depend on the type of sleeping strategy used. In addition, the deployment of small cells generally leads to higher energy efficiency but this gain saturates as the density of small cells increases. In a nutshell, our proposed framework provides an essential understanding on the deployment of future green heterogeneous networks.

Mobile communication is facing new challenges to the soaring traffic demand of numerous user devices; thus, the notion of the small cell has been proposed and realized in recent years. However, licensed spectrum has been occupied by various underlying access technologies, so the deployment of small cells needs a sophisticated planning algorithm. In this article, we provide an overview of reconfigurable radio and small cell technologies, then introduce the tentative network architecture for 5G. Two planning approaches (i.e., genetic-based and graphbased) are proposed that accommodate cognitive radio technology to improve user throughput by eliminating communication interference. Since cognitive radio networking provides frequency allocation with cognition cycle for better spectral efficiency, we tackle the deployment of ultradense small cells and consider the coordination of unlicensed spectrum at the same time. Results show that the proposed algorithms with spectrum cognition improve network performance in terms of throughput and signal-to-interference- plus-noise ratio. Specifically, the genetic- based algorithm increases 232 percent in throughput and 150 percent in signal-to-interference- plus-noise ratio compared to the graphbased algorithm. Finally, we conclude this article by discussing potential challenges and opportunities.

Stochastic geometry analysis of coverage probability in energy efficient dense heterogeneous network with sleep control mechanism

In this paper, a power saving scheme is investigated for macrocell-assisted small cell networks in the downlink, in which small cells are selected to stay in sleep or wake-up modes in order to mitigate excessive power consumption and the small cells operating in the sleep mode are eligible to off-load their served users to the macrocell to preserve communications. By taking the dynamics of user arrival into account, we attempt to minimize the average total power consumption of the network, which depends on several network parameters like power consumption of base stations, available bandwidth, user load in the cells, cell size, user rate requirement, rate outage probability and noise power density, while ensuring the average number of dropping off-loaded users to a satisfactory level. The design problem is then formulated as a constrained Markov decision process and solved via linear programming. A randomized strategy is proposed to accomplish the optimal sleep/wake-up policy for small cells. Simulation results confirm the effectiveness of the proposed scheme, as compared with the one without adopting sleep/wake-up mechanisms, and help us to capture the impact of the network parameters on the entire power saving.

Heterogeneous small cell networks (HSCN), as a promising paradigm to increase end-user data rates and improve the overall capacity, is expected to be a key cellular architecture in 5G wireless networks. However, energy consumed in HSCN is considerable due to the massive use of small cells. In this paper, we investigate the energy consumption issue which stems from the enormous number of running small cell base stations (SBSs) deploying in the HSCN. We first propose power consumption models so as to characterize the active state and the idle state of SBSs, respectively. Then two sleep modes for SBSs tier, i.e. random sleep mode and load-awareness dynamic sleep mode, are proposed. The random sleep is designed based on a binomial distribution of the SBS operation probability. Through the analysis on activeness of SBSs, we define the operation probability for the SBS applying the proposed dynamic sleep mode is associated to its traffic load level. The closed-form expressions of success probability for coverage, which is used to decide whether an active user can connect to a SBS successfully, are derived for the proposed sleep modes. Energy consumption minimizations are presented for the two proposed sleep modes under the success probability constraint. Simulation results prove the effectiveness of the proposed two sleep modes. Different energy saving gains can be achieved via using of the energy saving strategy. The superior of the dynamic sleep mode by comparing the random sleep is also verified in terms of energy consumption, success probability and power efficiency.

The deployment of low-power small cells is envisaged as the main driver to accommodate the mobile broadband traffic growth in cellular networks. Depending on the spatial distribution of the user traffic, a densification of the small cells may be required in confined areas. However, deploying more and more cells in given areas may imply an increase of the inter-cell interference among the small cells. This study aims at investigating if the inter-cell interference among outdoor small cells may represent an impairment to the user experience, and evaluates if and in what conditions the interference coordination is worthwhile compared to the universal frequency reuse. Results show that the inter-cell interference depends on the small cell deployment in the urban environment (e.g. streets and squares) and on the network load condition. In case of deployment along urban streets, the inter-cell interference does not affect the user throughput and no interference coordination is required. On the other hand, if deployed in open areas (e.g. city squares) the interference coordination enhances both average and coverage user throughput in case of high network load condition.

The effect of LH on the intracellular free Ca2+ concentration ([Ca2+]i) was investigated in highly purified small and large bovine luteal cell populations. Luteal cells were obtained from midcycle corpora lutea dispersed with collagenase and separated by flow cytometry into large and small cells. Resting levels of Ca2+ were higher (P less than 0.05) in the large than small cells [314 +/- 25 nM (n = 5) vs. 186 +/- 13 nM (mean +/- SE; n = 13) for large and small cells, respectively]. LH rapidly increased [Ca2+]i in both small and large cells loaded with the fluorescent Ca2+ probe fura-2. In the small cells, [Ca2+]i was immediately increased 2- to 6-fold (from 176 +/- 8 to 468 +/- 8 nM; n = 5) after adding LH. The LH induced [Ca2+]i rise occurred in two phases: an initial peak due to intracellular Ca2+ mobilization and a secondary rise due to Ca2+ influx from extracellular sources. Preincubation of the small cells with EGTA reduced the initial phase and abolished the secondary rise in [Ca2+]. Both forskolin and 8-bromo-cAMP increased [Ca2+]i in the small cells. In contrast, only a single phase of [Ca2+]i rise was observed in LH-treated large cells, and the response was 1.5- to 2-fold greater than the resting Ca2+ levels [314 +/- 25 vs. 435 +/- 60 nM (n = 4), for resting vs. LH-treated values, respectively]. The addition of both LH and prostaglandin F2 alpha (PGF2 alpha) to the large cells resulted in increases in [Ca2+]i that were greater than those induced by each hormone separately (2.0-fold for LH and 2.7-fold for PGF2 alpha vs. 7- to 9-fold in the presence of both hormones). These findings demonstrate that LH induces rapid increases in intracellular [Ca2+]i that differ in magnitude and profile between the small and large bovine luteal cells. Furthermore, LH and PGF2 alpha interacted to promote increases in [Ca2+]i in the large cells, that were higher than the sum of [Ca2+]i induced by each hormone separately.

We develop a framework for downlink heteroge- neous cellular networks with line-of-sight (LoS) and non-line- of- sight (NLoS) transmissions. Using stochastic geometry, we derive a tight approximation of the achievable downlink rate that enables us to compare the performance between densifying small cells and expanding base station (BS) antenna arrays. Interestingly, we find that adding small cells into a sparse network improves the achievable rate much faster than expanding antenna arrays at the macro BS. However, when the small cell density exceeds a critical threshold, the spacial densification will lose its benefits and further impair the network capacity. To this end, we present the optimal small cell density that maximizes the rate as practical deployment guidance. In contrast, expanding macro BS antenna array can always increase the capacity until reaching an upper bound caused by pilot contamination, and this bound also surpasses the peak rate obtained from deployment of small cells. Therefore, small cells are preferred for low rate requirements due to the rapid rate gain, and the massive MIMO is preferred for higher rate requirements due to better achievable rate.

Small cells are low power nodes, which have lower transmit power than the macro eNodeB. They are regarded as one of the important solutions in long term evolution advanced (LTE-A) to deal with the dramatically increased mobile data traffic. Small cells deployments in LTE-A release 12 with 4 small cells in a cluster suffer from stronger inter-cell interference than heterogeneous network (HetNet) in the previous release of LTE-A, where one small cell exists in a cluster. We investigate the joint least squares channel estimation (JLSCE) over demodulation reference signals (DMRS) spreading in time and frequency resources in LTE-A systems. Furthermore, different receiver structures using least square channel estimation (LSCE) and JLSCE are investigated in small cell deployments with the presence of strong inter-cell interference. Our results show the linear minimum mean square error (LMMSE)-interference rejection combining (IRC) with LSCE is the most efficient receiver structure compared to the enhanced (e)LMMSE-IRC with JLSCE, interference cancellation (IC) with eLMMSE-IRC and maximum ratio combining (MRC) receiver structures for signal detection in small cell deployments.

For the upcoming fifth generation of wireless mobile communication networks, heterogeneous cellular architectures with a dense deployment of low-power small cells have been proposed to satisfy increasing data demands. Energy harvesting small cells, which operate with renewable energy sources such as solar energy and the potential for power storage, can be utilized to decrease the power consumption of the network if their activities are carefully scheduled based on the forecasted demand. In this work, we propose a novel approach for small cell energy management and activity scheduling, where not only the small cell activity but also the duration of time-slots in which the schedule is applied, are optimized. Cell load balancing is performed by solving separate mixed integer optimization problems, one each for activity optimization and for timescale optimization. Simulation results show that the proposed scheme is successful in finding an optimized small cell activity schedule for given energy levels and demand forecasts, and that there is a significant benefit achievable through the proposed timescale optimization approach.

The low power radio access nodes, also known as small cells, are always applied to compensate the coverage of macro cells so as to increase the network capacity. The radio spectrum condition is greatly affected by the buildings and there will be many communication dead zones in the metropolitan area environment if only macro cells are deployed. Generally, the communication dead zone is always small and in irregular shape, therefore, the deployment of small cells is one of the most convincible ways to deal with it. However, the interference, necessary overlapping for handoff, coverage improvement, and cost are correlated to one another. And it is hard to determine the number and locations of small cells to be deployed. In this paper, we propose the Particle Swarm Optimization (PSO) based small cell placement algorithm to effectively deploy the small cells in the communication dead zone of metropolitan area. The fitness value in the PSO process of the proposed scheme is tuned by the adaption function to compromise the coverage maximization and necessary overlapping of small cells. A practical area of Taipei City was adopted as an experimental example to investigate the performance of the proposed scheme. The experimental results illustrate that the proposed scheme places small cells in proper locations so that the coverage can be effectively improved.

In next generation networks, ultra-dense small cell networks are emerging to deal with exponential data traffic increasing. With the deployment of small cells (SCs) becoming increasingly dense, these cells may be under-utilized during quite a few period of time. Depending on this circumstance, we aim to maximize the network energy efficiency by deactivating such under-utilized cells, subjecting to the rate fairness constraints among users. To overcome this issue, we propose an energy efficient SCs on/off approach that allows SCs to activate or deactivate based on estimated traffic load and UEs' preference requirements. Small cells are coupled into clustered based on distance firstly, and then UE-preference aware network selection scheme is performed, after which the SCs on/off pattern is derived iteratively in each formed cluster. For the network selection scheme, the optimal user association is obtained according to the SINR, estimated traffic load, traffic type and delay. Due to the complexity of finding the optimal SCs on/off pattern, we also give a practical suboptimal greedy SCs on/off algorithm with polynomial computational complexity. Simulation results demonstrate that energy expenditure can be reduced by 72% at most. Comparing to optimal search and traditional algorithms, the greedy algorithm achieves a comparable performance with much lower complexity.

Dense deployment of small cells is seen as one of the major approaches for addressing the traffic demands in next-generation wireless networks. However, dense deployment of large number of small cells necessitates effective techniques for placing under-loaded small cells into sleep mode, so as to save energy. Such techniques should be low complexity and should not also compromise quality of service of users such as short access delay, while they can also result in significant energy savings for delay-tolerant network traffic. In this study, we introduce energy efficient, low-complexity techniques for load-based sleep mode optimization in densely deployed 5G small cell networks. We define a new analytic model to characterize the distribution of the traffic load of a small cell using a Gamma distribution, find its distribution parameters, and verify the validity of the model using computer simulations. We also compare the throughput of various sleep mode techniques as a function of different delay tolerance levels, where our simulation results show that the proposed technique achieves the highest throughput.

The fifth generation of mobile systems will rely on a dense deployment of small cells in order to meet its ambitious Key Performance Indicator targets. However, this dense deployment will result in a significant increase in the energy consumption of 5G networks, and thus, OPEX costs. Several small cell sleeping algorithms have been proposed in the literature but they require complex algorithms to decide when to switch on/off the small cells. On the basis of a distributed self-organizing approach, in this paper, we propose a low-cost, low-complexity small cell sleep scheduling algorithm to minimize the energy consumption of small cells in 5G and beyond networks. Our control utilizes a motion detection circuit to toggle the small cell between sleep and active modes based on the presence of a user. We evaluate the performance the algorithm on an experimental testbed and the results show that our algorithm achieves up to 20% reduction in energy consumption when compared to ‘always on’ approach.

Due to their low cost and easy deployment, small cells provide a viable and cost-effective way of improving the cellular coverage and capacity both for homes and enterprises, both in metropolitan and rural areas. Stimulated by their attractive features and advantages, the ongoing development and deployment of small cells by manufacturers and mobile network operators have seen a surge in recent years. Together with macro-cells, they form, what are called Heterogeneous Networks or HetNets. However, the successful rollout and operation of small cells are still facing significan issues. In this paper the need for, challenges and solutions of small cell deployments are analyzed. This analysis is conducted with respect to self-organizing features, interference coordination, energy efficiency and spectrum efficiency. The analysis is complemented with numerical results based on system simulations in Macro-only and HetNet scenarios and also on real measurements performed on an mobile operator network. Results show the clear improvement that a HetNet brings in term of user throughput and also the amunt of spectrum waste that is present in nowadays' operator networks.