The rapid escalation of user traffic and service innovation has made the deployment of small cell base stations essential for eventually decreasing energy consumption in future generation wireless network. An energy efficient small cell zooming strategy is proposed using weighted majority cooperative game for two-tier fifth generation (5G) mobile network. The proposed strategy is referred as ‘5G-ZOOM-Game’. Small cells ‘zoom in’ and ‘zoom out’ dynamically according to the proposed ‘5G-ZOOM-Game’ algorithm. Different frequency sets are assigned to small cells based on adjacency for reducing interference. In the proposed approach femtocells are used as small cells. The proposed algorithm is applied between two adjacent femtocells. Out of two adjacent femtocells, higher majority femtocell is selected based on weighted majority game; this femtocell zooms its coverage area. The utility function of the proposed approach is defined to connect maximum possible number of mobile devices by increasing the higher majority femtocell’s coverage area. Higher majority femtocell is chosen based on the load and minimum distance between mobile device and femtocell base station. Proposed 5G-ZOOM-Game network reduces ~ 35% of power consumption and increases signal-to-interference-plus-noise-ratio (SINR) and spectral efficiency by ~ 30% and ~ 60% respectively than the existing approaches.

5G-ZOOM-Game: small cell zooming using weighted majority cooperative game for energy efficient 5G mobile network

The widespread application of wireless mobile services and requirements of ubiquitous access have resulted in drastic growth of the mobile traffic and huge energy consumption in ultradense networks (UDNs). Therefore, energy-efficient design is very important and is becoming an inevitable trend. To improve the energy efficiency (EE) of UDNs, we present a joint optimization method considering user association and small-cell base station (SBS) on/off strategies in UDNs. The problem is formulated as a nonconvex nonlinear programming problem and is then decomposed into two subproblems: user association and SBS on/off strategies. In the user association strategy, users associate with base stations (BSs) according to their movement speeds and utility function values, under the constraints of the signal-to-interference ratio (SINR) and load balancing. In particular, taking care of user mobility, users are associated if their speed exceeds a certain threshold. The macrocell base station (MBS) considers user mobility, which prevents frequent switching between users and SBSs. In the SBS on/off strategy, SBSs are turned off according to their loads and the amount of time required for mobile users to arrive at a given SBS to further improve network energy efficiency. By turning off SBSs, negative impacts on user associations can be reduced. The simulation results show that relative to conventional algorithms, the proposed scheme achieves energy efficiency performance enhancements.

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User Association and Small-Cell Base Station On/Off Strategies for Energy Efficiency of Ultradense Networks

To efficiently reduce on-grid energy consumption, the base station (BS) sleeping strategy in the hybrid energy-powered cellular network (HybE-Net) in the Internet of Things environment is studied. In order to maximize on-grid energy saving, a BS sleeping algorithm, which adaptively and cooperatively adjusts the sleeping/non-sleeping state of BSs according to the actual traffic load and solar energy state, is proposed. Specifically, the solar energy state and dynamic characteristics of the BS in solar energy supply stage are analyzed by the approximation diffusion theory. Then, by comprehensively considering communication service quality and on-grid energy saving, BS sleeping constraints are derived mathematically. Subsequently, the BS sleeping problem in HybE-Net is formulated as the on-grid energy consumption maximization problem. To solve the optimization problem, a BS sleeping algorithm based on coalitional games is proposed. Finally, simulation results demonstrate that the proposed algorithm performs excellently in improving solar energy utilization and reducing on-grid energy consumption of the HybE-Net.

Base Station Sleeping Strategy for On-Grid Energy Saving in Cellular Networks With Hybrid Energy Supplies in IoT Environment

The global surge of connected devices and multimedia services necessitates increased capacity and coverage of communication networks. One approach to address the unprecedented rise in capacity and coverage requirement is deploying several small cells to create ultra-dense networks. This, however, exacerbates problems with energy consumption and network management due to the density and unplanned nature of the deployment. This review discusses various approaches to solving energy efficiency problems in ultra-dense networks, ranging from deployment to optimisation. Based on the review, we propose a taxonomy, summarise key findings, and discuss operational and implementation details of past research contributions. In particular, we focus on popular approaches such as machine learning, game theory, stochastic and heuristic techniques in the ultra-dense network from an energy perspective due to their promise in addressing the issue in future networks. Furthermore, we identify several challenges for improving energy efficiency in an ultra-dense network. Finally, future research directions are outlined for improving energy efficiency in ultra-dense networks in 5G and beyond 5G networks.

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Energy-Efficient Ultra-Dense 5G Networks: Recent Advances, Taxonomy and Future Research Directions

With the rapid growth of the number of cellular network users and their tendency to share various multimedia files, providing better coverage for users and guaranteeing the quality of service (QoS) to serve them by base stations (BSs) are essential. On the other side, with the increase in the number of users and BSs, the power consumption augments in the cellular network. Heterogeneous network (HetNet), which consists of a large number of small cells deployed in the coverage of macrocell, can increase the network capacity. To fulfill the users’ demands and meanwhile reduce the power consumption, we present three energy-efficient adaptive sleeping, zooming, and zooming + sleeping schemes for ultra-dense HetNets to increase the minimum energy efficiency (EE) of small cell BSs (SBSs). In the proposed sleeping technique, the SBS with the minimum EE switches to sleep mode until the minimum EE is improved. The proposed zooming technique performs the user association and power control to maximize the EE of each SBS as well as utilize the lowest possible number of SBSs. Finally, SBSs with the minimum EE switches to sleep mode after applying the zooming technique. The proposed joint zooming and sleeping method reduces the total power consumption and SBS utilization ratio about 100% and 25%, respectively, compared to the static user association scheme, in which the users connect to the nearest SBS. Also, it enhances the SE and EE of static user association about 1.5 and 1.4, respectively, which indicate its efficiency.

Adaptive energy-efficient small cell sleeping and zooming in heterogeneous cellular networks

To meet the exponential increasing high data rate demand of mobile users, heterogeneous ultra-dense networks (UDN) is widely seen as an essential technology to provide high-rate transmissions to nearby mobile users. However, the dense and random deployment of small base stations (SBSs) overlaid by macro base stations and their uncoordinated operation lead to important questions about the power consumption and aggressive frequency reuse of heterogeneous UDN. For the problem of huge power consumption and spectrum resource tension in heterogeneous UDN, a joint strategy of SBSs sleep and spectrum allocation is proposed. By using stochastic geometry, the coverage probabilities of base stations and the average ergodic rates of mobile users are derived in each tier and the whole network. In addition, we formulate the coverage probability maximization and power consumption minimization problems, and determine the optimal operating regimes for SBSs, and as well as spectrum allocation. The numerical results show that the SBSs sleep and spectrum allocation can reduce the power consumption and interference of the whole network.

Base Station Sleep and Spectrum Allocation in Heterogeneous Ultra-dense Networks

Abstract Let Y t be an autoregressive process with order one, i.e., Y t = μ + ϕ n Y t −1 + ε t , where [ ε t ] is a heavy tailed general GARCH noise with tail index α . Let $${{\hat \phi }_n}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mover> <mml:mi>ϕ</mml:mi> <mml:mo>^</mml:mo> </mml:mover> <mml:mi>n</mml:mi> </mml:msub> </mml:math> be the least squares estimator (LSE) of ϕ n For μ = 0 and α &lt; 2, it is shown by Zhang and Ling (2015) that $${{\hat \phi }_n}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mover> <mml:mi>ϕ</mml:mi> <mml:mo>^</mml:mo> </mml:mover> <mml:mi>n</mml:mi> </mml:msub> </mml:math> is inconsistent when Y t is stationary (i.e., ϕ n ≡ ϕ &lt; 1), however, Chan and Zhang (2010) showed that $${{\hat \phi }_n}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mover> <mml:mi>ϕ</mml:mi> <mml:mo>^</mml:mo> </mml:mover> <mml:mi>n</mml:mi> </mml:msub> </mml:math> is still consistent with convergence rate n when Y t is a unit-root process (i.e., ϕ n = 1) and [ ε t ] is a GARCH(1, 1) noise. There is a gap between the stationary and nonstationary cases. In this paper, two important issues will be considered: (1) what about the nearly unit root case? (2) When can ϕ be estimated consistently by the LSE? We show that when ϕ n = 1 − c/n , then $${{\hat \phi }_n}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mover> <mml:mi>ϕ</mml:mi> <mml:mo>^</mml:mo> </mml:mover> <mml:mi>n</mml:mi> </mml:msub> </mml:math> converges to a functional of stable process with convergence rate n . Further, we show that if lim n →∞ k n (1 − ϕ n ) = c for a positive constant c , then $${k_n}({\hat \phi _n} - \phi )$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>k</mml:mi> <mml:mi>n</mml:mi> </mml:msub> <mml:mo>(</mml:mo> <mml:msub> <mml:mover> <mml:mi>ϕ</mml:mi> <mml:mo>^</mml:mo> </mml:mover> <mml:mi>n</mml:mi> </mml:msub> <mml:mo>−</mml:mo> <mml:mi>ϕ</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> converges to a functional of two stable variables with tail index α /2, which means that ϕ n can be estimated consistently only when k n → ∞. 

/pdf/nearly-nonstationary-processes-under-infinite-variance-garch-3uznzsc9.pdf

Jiangpan Shi

Papers

Base Station Sleep and Spectrum Allocation in Heterogeneous Ultra-dense Networks

Nearly nonstationary processes under infinite variance GARCH noises