Power-saving heterogeneous networks through optimal small-cell scheduling

This work studies the power optimal dynamic scheduling problem in multi-channel multi-user wireless access networks. Users have quality-of-service (QoS) requirements on the minimum rates with statistical delay guarantees. Only one user is allowed to transmit over a channel in a given time slot. This work considers two scenarios: homogeneous and heterogeneous users. For the former scenario, the optimal scheduling policy can be derived, and an online scheduling algorithm for the optimal policy is proposed using online time-averaging without requiring a-priori known fading statistics. For the latter scenario, the optimal scheduling problem is combinatorially hard; hence, even when the fading statistics are available, computing the optimal policy is intractable. Consequently, this work develops a sub-optimal online scheduling algorithm with linear complexity which does not require a-priori known fading statistics. Moreover, the scheduling algorithm satisfies the QoS constraints for the users. Illustrative results demonstrate the performance of the proposed scheduling algorithms in various settings.

In heterogeneous networks, interference is one of the critical issues. In this letter, we consider a two-tier downlink network composed of macrocell base stations (MBSs) and femtocell base stations (FBSs), where the locations of MBSs and FBSs are modeled by two independent Poisson point processes (PPPs). FBSs are assumed to be cognitive and become inactive when within a certain range of active MBSs. Due to the inactivation regions, the location distribution of active FBSs no longer follows PPP, complicating the calculation of coverage probability. To circumvent this, we propose an approximation to the coverage probability for both the open access and closed access policies.

DRAM has become a crucial component in terms of system power consumption as the size of main memory increases. To improve power efficiency of DRAM devices, we need to analyze characteristics of DRAM behavior from power consumption's perspective. In this paper, we analyze the characteristics of various DRAM devices from major vendors under real system operating environment. As the size of the DRAM increases, power consumption due to activate, precharge and burst operations remains about the same, but that due to background and refresh operations increases steadily. Especially, power consumption due to the refresh operation for 3D stacked DRAMs increases by 91% at high temperatures, which strongly implies that the refresh operation will become more crucial for DRAMs in the future.

We consider a class of k-server optimal task scheduling problems partitioning and scheduling N tasks with various real-time constrains and work loads on k servers with convex task processing cost function so as to minimize the total task processing cost while still guaranteeing satisfaction of all time constraints. This class has broad expressing power for practical scheduling problems in several areas such as real-time multimedia wireless transmission , CPU energy conservation, and warehouse order processing management, et. al. Our formulation is quite general such that most previous works can be readily reduced to a special case of the presented k-server optimal task scheduling problem. We show that, when k = 1, optimal solution can be obtained in computational complexity of O(N) and the corresponding optimal scheduling problem is equivalent to finding the shortest 2D Euclidean distance between two vertices inside a well-defined 2D polygon. However, when k 2, the optimal scheduling problem can be demonstrated to be NP-hard by reducing it to a well-known NP-complete bin-packing problem. Therefore, we conclude no polynomial time algorithm exists for a general k-server optimal task scheduling problem. We then construct approximation algorithms to solve the presented k-server problem in a practical way and illustrate its performance by simulation results and analysis.

In this paper, we consider the optimization of wireless capacity-limited back-haul links in future heterogeneous networks (HetNets). We assume that the HetNet is formed with one macro-cell base station (MBS), which is associated with multiple small-cell base stations (SBSs). It is also assumed both the MBS and the SBSs are equipped with massive arrays, while all mobiles users (macro-cell and small-cell users) have single antenna. For the back-haul links, we propose to use a capacity-aware beam-forming scheme at the SBSs and MRC at the MBS. Using particle swarm optimization (PSO), each SBS seeks the optimal transmit weight vectors that maximize the back-haul up-link capacity and the access up-links signal-to-interference plus noise ratio (SINR). The performance evaluation in terms of the symbol error rate (SER) and the ergodic system capacity shows that the proposed capacity-aware back-haul link scheme achieves similar or better performance than traditional wireless back-haul links and requires considerably less computational complexity.

We develop the joint optimization schemes for traffic-offloading and resource-allocation over radio-frequency (RF) powered backscatter-based mobile wireless networks, where a macro-cell base station (BS), several small-cell access points (SAPs), and multiple energy harvesting (EH) mobile users (MUs) coexist. By optimizing MUs' network access (through traffic-offloading) and system resource-allocation, we aim to minimize the energy consumption of MUs by using the low-energy consumption of the backscatter communications. First, we develop a distributed traffic-offloading and resource-allocation scheme, when the short-range ambient backscatter (AB) communication (generally with communication range being several meters) is employed to assist MUs' data transmission. Based on our developed scheme, MUs choosing to access nearby SAPs can backscatter ambient RF signals for data transmission to reduce energy consumption. Then, we employ the long-range bistatic backscatter (BB) communication (with communication up to 270 meters) to support data transmission between MUs and the BS, and thus MUs can convey data by backscattering RF signals emitted from dedicated carrier emitters. Accordingly, we develop a joint traffic-offloading and resource-allocation scheme for the scenarios when the AB and BB communications are adopted by MUs accessing the SAPs and the BS, respectively. Finally, we validate and evaluate the performance of our developed schemes through numerical analyses.

User mobility, heterogeneity of networks and network technologies, variety of mobile devices (e.g., different operating systems, display size, CPU capabilities, battery limitations), and wide range of video-centric applications (e.g., video on demand, video games, live video streaming, video conferences, surveillance) open up the demand for user-centric solutions that adapt the video application to the underlying network conditions and device characteristics. In addition, the absence of battery improvements suitable to meet the growing power requirements and the need for green Information and Communications Technologies ICT provides strong motivation for researchers to develop energy efficient techniques to manage and reduce power consumption in next-generation wireless networks, while still meeting high user quality expectations. In this context, we propose adapt-or-handover, a hybrid multimedia delivery solution, which balances the benefits of multimedia content adaptation and of network selection to decrease power consumption in a heterogeneous wireless network environment. The proposed solution is analyzed and comparatively tested through simulations. The results show how, by using adapt-or-handover, the users benefit from up to 31% energy savings with insignificant degradation in quality, in comparison with other energy efficient solutions.

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.

With the development of fifth generation (5G) communications systems, heterogeneous networks (HetNets) have become a research hotspot. However, most of the existing work assumes that the user is static in HetNets. This hypothesis can greatly simplify system models, nevertheless it is not always accurate enough, because the user mobility has a significant effect on the performances of HetNets. Therefore, this paper studies the influence of the infinite user mobility in HetNets by using tools from stochastic geometry. The local delay and energy efficiency of HetNets based on Poisson point process (PPP) and Poisson cluster process (PCP) respectively are derived, the latter being more accurate to model the hot spot areas than the former. It was found that user mobility can reduce obviously the local delay and improve the energy efficiency under the high SIR regime. Moreover, the local delay is larger and the energy efficiency is lower under PCP than under PPP for infinite user mobility. In addition, the user mobility can reduce the function of the discontinuous transmission (DTX) scheme, which is used to improve the energy efficiency. Finally, the simulation results verify the accuracy of the numerical analyses.

Despite the constant improvement in power efficiency of macro-cell base stations, the continuous capacity expansion increases considerably the global power consumption of the mobile network. This article presents a new concept of managing the DC-to-DC converter supplying power amplifiers in base stations, targeting a factor two reduction in power consumption. Details of the concept and the practical implementation of a 500 kHz and zero to 112W buck converter are discussed. The prototype achieves 94% power efficiency and a good stability over the complete power range.

For small cell technology to significantly increase the capacity of tower-based cellular networks, mobile users will need to be actively pushed onto the more lightly loaded tiers (corresponding to, e.g., pico and femtocells), even if they offer a lower instantaneous SINR than the macrocell base station (BS). Optimizing a function of the long-term rate for each user requires (in general) a massive utility maximization problem over all the SINRs and BS loads. On the other hand, an actual implementation will likely resort to a simple biasing approach where a BS in tier j is treated as having its SINR multiplied by a factor A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sub> ≥ 1, which makes it appear more attractive than the heavily-loaded macrocell. This paper bridges the gap between these approaches through several physical relaxations of the network-wide association problem, whose solution is NP hard. We provide a low-complexity distributed algorithm that converges to a near-optimal solution with a theoretical performance guarantee, and we observe that simple per-tier biasing loses surprisingly little, if the bias values A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sub> are chosen carefully. Numerical results show a large (3.5x) throughput gain for cell-edge users and a 2x rate gain for median users relative to a maximizing received power association.

The k-SIC technology can support at most k parallel transmissions, thus it has the prominent capability of providing fast media access, which is vital for real-time industrial wireless networks. However, it suffers from high power consumption because high interference has to be overcome. In this paper, given the real-time performance requirement of an uplink network supporting k-SIC, we study how to minimize aggregate power consumption of users by power scheduling. We prove that the problem is solvable in polynomial time. A universal algorithm with complexity of O(n 3 ) is proposed for k-SIC, where n is the number of transmitters. For the special case of k = 2, another algorithm with complexity of O(L 4 ) is presented, where L is the frame length. Simulation results reveal that both the aggregate power consumption and the maximal transmit power will be exponentially declined with further relaxation of the real-time performance.

The k-SIC (successive interference cancellation) technology can support at most k parallel transmissions, and thus fast media access can be provided, which is vital for real-time industrial wireless networks. However, it suffers from high power consumption because high interference caused by parallel transmissions has to be overcome. In this paper, given the real-time performance requirements, we consider a network supporting k-SIC, and study how to minimize aggregate power consumptions of user equipments for uplink transmissions by jointly addressing power allocation and user scheduling. We show that the problem is solvable in polynomial time in the case of continuous transmit powers by an algorithm with complexity of O(nlog(n)), where n is the number of user equipments. In the case of discrete transmit powers, the problem is shown to be polynomially solvable for 2-SIC, and we also propose an approximation algorithm for k-SIC where k > 2. Experimental evaluations reveal that the real-time performances have tremendous impacts on both the aggregate power consumption and the maximum of the transmit powers of user equipments. Besides, the usability of SIC in low-power applications is also shown by experimental evaluations.

One of the major shortcomings of Centralized Radio Access Networks (C-RAN) is that the large capacity is required for fronthaul network between Remote Radio Heads (RRHs) and central office with baseband unit (BBU) pool. Possible solutions are to introduce lower-cost networking technology for fronthaul network, such as Time and Wavelength Division Multiplexing Passive Optical Network (TWDM-PON), and to introduce functional split, that moves some baseband processing functions to cell site to decrease the utilization of the fronthaul network. In this paper, we construct the mathematical model for selecting function split options of baseband processing to minimize the power consumption of TWDM-PON based fronthaul network. In detail, we formulate the optimization problem for minimizing the total power consumption of fronthaul network in terms of the capacity of TWDM-PON, the number of RRHs in each cell site, server resources, latency constraints, the amount of traffic from each RRH, physical/virtual server power consumption characteristics. Numerical examples are shown for confirming the correctness of the proposed model and for presenting the effect of resource enhancement methods on the capacity and energy efficiency of the system.

Rapidly rising energy costs and increasingly rigid environmental standards have led to an emerging trend of addressing the “energy efficiency” aspect of wireless cellular networks. In cellular networks, base stations (BSs) are power-hungry elements and absorbing them in the smart grid can significantly increase power efficiency. However, this important perspective has been ignored in the energy-efficiency research on cellular networks. In this paper, heterogeneous networks, cognitive radios, and the smart grid are jointly studied to improve energy efficiency. Multiple retailers in the smart grid provide real-time electricity prices to the heterogeneous network, and the macrocell base station (MBS) and femtocell base stations (FBSs) perform energy-efficient power allocation. To mitigate interference and guarantee quality of service (QoS), an interference price is proposed to allow the MBS to protect itself by charging the femtocell users.We formulate the decision problems of electricity price, energy-efficient power allocation and interference price as a three-level Stackelberg game. We use a homogeneous Bertrand game to model price decisions made by the retailers. A backward induction method is used to analyze the proposed game. Simulation results show that our proposed scheme can significantly reduce operational expenditure and CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions.