Grid Energy Consumption and QoS Tradeoff in Hybrid Energy Supply Wireless Networks

Hybrid energy supply (HES) wireless networks have recently emerged as a new paradigm to enable green networks, which are powered by both the electric grid and harvested renewable energy. In this paper, we will investigate two critical but conflicting design objectives of HES networks, i.e., the grid energy consumption and quality of service (QoS). Minimizing grid energy consumption by utilizing the harvested energy will make the network environmentally friendly, but the achievable QoS may be degraded due to the intermittent nature of energy harvesting. To investigate the tradeoff between these two aspects, we introduce the total service cost as the performance metric, which is the weighted sum of the grid energy cost and the QoS degradation cost. Base station assignment and power control is adopted as the main strategy to minimize the total service cost, while both cases with non-causal and causal side information are considered. With non-causal side information, a Greedy Assignment algorithm with low complexity and near-optimal performance is proposed. With causal side information, the design problem is formulated as a discrete Markov decision problem. Interesting solution structures are derived, which shall help to develop an efficient monotone backward induction algorithm. To further reduce complexity, a Look-Ahead policy and a Threshold-based Heuristic policy are also proposed. Simulation results shall validate the effectiveness of the proposed algorithms and demonstrate the unique grid energy consumption and QoS tradeoff in HES networks.Comment: 14 pages, 7 figures, to appear in IEEE Transactions on Wireless Communication

From 4G to 5G: Self-organized Network Management meets Machine Learning

In this paper, we provide an analysis of self-organized network management, with an end-to-end perspective of the network. Self-organization as applied to cellular networks is usually referred to Self-organizing Networks (SONs), and it is a key driver for improving Operations, Administration, and Maintenance (OAM) activities. SON aims at reducing the cost of installation and management of 4G and future 5G networks, by simplifying operational tasks through the capability to configure, optimize and heal itself. To satisfy 5G network management requirements, this autonomous management vision has to be extended to the end to end network. In literature and also in some instances of products available in the market, Machine Learning (ML) has been identified as the key tool to implement autonomous adaptability and take advantage of experience when making decisions. In this paper, we survey how network management can significantly benefit from ML solutions. We review and provide the basic concepts and taxonomy for SON, network management and ML. We analyse the available state of the art in the literature, standardization, and in the market. We pay special attention to 3rd Generation Partnership Project (3GPP) evolution in the area of network management and to the data that can be extracted from 3GPP networks, in order to gain knowledge and experience in how the network is working, and improve network performance in a proactive way. Finally, we go through the main challenges associated with this line of research, in both 4G and in what 5G is getting designed, while identifying new directions for research.Comment: 23 pages, 3 figures, Surve

Dynamic Cross-Layer Beamforming in Hybrid Powered Communication Systems With Harvest-Use-Trade Strategy

The application of renewable energy is a promising solution to realize the Green Communications. However, if the cellular systems are solely powered by the renewable energy, the weather dependence of the renewable energy arrival makes the systems unstable. On the other hand, the proliferation of the smart grid facilitates the loads with two-way energy trading capability. Hence, a hybrid powered cellular system, which combines the smart grid with the base stations, can reduce the grid energy expenditure and improve the utilization efficiency of the renewable energy. In this paper, the long-term grid energy expenditure minimization problem is formulated as a stochastic optimization model. By leveraging the stochastic optimization theory, we reformulate the stochastic optimization problem as a \mbox{per-frame} grid energy plus weighted penalized packet rate minimization problem, which is NP-hard. As a result, two suboptimal algorithms, which jointly consider the effects of the channel quality and the packet reception failure, are proposed based on the successive approximation beamforming (SABF) technique and the \mbox{zero-forcing} beamforming (ZFBF) technique. The convergence properties of the proposed suboptimal algorithms are established, and the corresponding computational complexities are analyzed. Simulation results show that the proposed SABF algorithm outperforms the ZFBF algorithm in both grid energy expenditure and packet delay. By tuning a control parameter, the grid energy expenditure can be traded for the packet delay under the proposed stochastic optimization model.Comment: accepted by IEEE Trans. Wireless Commu

Group Sparse Beamforming for Green Cloud-RAN

A cloud radio access network (Cloud-RAN) is a network architecture that holds the promise of meeting the explosive growth of mobile data traffic. In this architecture, all the baseband signal processing is shifted to a single baseband unit (BBU) pool, which enables efficient resource allocation and interference management. Meanwhile, conventional powerful base stations can be replaced by low-cost low-power remote radio heads (RRHs), producing a green and low-cost infrastructure. However, as all the RRHs need to be connected to the BBU pool through optical transport links, the transport network power consumption becomes significant. In this paper, we propose a new framework to design a green Cloud-RAN, which is formulated as a joint RRH selection and power minimization beamforming problem. To efficiently solve this problem, we first propose a greedy selection algorithm, which is shown to provide near- optimal performance. To further reduce the complexity, a novel group sparse beamforming method is proposed by inducing the group-sparsity of beamformers using the weighted $\ell_1/\ell_2$-norm minimization, where the group sparsity pattern indicates those RRHs that can be switched off. Simulation results will show that the proposed algorithms significantly reduce the network power consumption and demonstrate the importance of considering the transport link power consumption

Optimal Pricing and Load Sharing for Energy Saving in Communications Cooperation

In this paper, we propose a pricing mechanism for the uplink communication cooperation to save the energy of mobile terminals (MTs) in wireless cellular network. Under the uncertainties of the other MTs' channel and battery conditions, a source MT in low battery level or bad channel condition is allowed to select and pay another MT in proximity to help forward its data package to the base station (BS). We formulate the source MT's pricing and load sharing problem as an optimization problem to minimize its expected energy cost. When the source MT cannot split its data package for a certain multimedia application, we motivate the selected relay MT to forward the whole data package by obtaining the optimal pricing through a dichotomous search algorithm. When the source MT can split the data package, we jointly optimize the pricing and load sharing with the relay MT and propose an alternating optimization algorithm that achieves near-optimal solution. Extensive numerical results are provided to show that our proposed pricing mechanism can significantly decrease the source MT's expected energy cost, and load sharing is more cost-efficient when the size of the data package is large and the average number of helping MTs is small

Provisioning Green Energy for Base Stations in Heterogeneous Networks

Cellular networks are among the major energy hoggers of communication networks, and their contributions to the global energy consumption increase rapidly due to the surges of data traffic. With the development of green energy technologies, base stations (BSs) can be powered by green energy in order to reduce the on-grid energy consumption, and subsequently reduce the carbon footprints. However, equipping a BS with a green energy system incurs additional capital expenditure (CAPEX) that is determined by the size of the green energy generator, the battery capacity, and other installation expenses. In this paper, we introduce and investigate the green energy provisioning (GEP) problem which aims to minimize the CAPEX of deploying green energy systems in BSs while satisfying the QoS requirements of cellular networks. The GEP problem is challenging because it involves the optimization over multiple time slots and across multiple BSs. We decompose the GEP problem into the weighted energy minimization problem and the green energy system sizing problem, and propose a green energy provisioning solution consisting of the provision cost aware traffic load balancing algorithm and the binary energy system sizing algorithm to solve the sub-problems and subsequently solve the GEP problem. We validate the performance and the viability of the proposed green energy provisioning solution through extensive simulations, which also conform to our analytically results

A Lyapunov Optimization Approach for Green Cellular Networks with Hybrid Energy Supplies

Powering cellular networks with renewable energy sources via energy harvesting (EH) has recently been proposed as a promising solution for green networking. However, with intermittent and random energy arrivals, it is challenging to provide satisfactory quality of service (QoS) in EH networks. To enjoy the greenness brought by EH while overcoming the instability of the renewable energy sources, hybrid energy supply (HES) networks that are powered by both EH and the electric grid have emerged as a new paradigm for green communications. In this paper, we will propose new design methodologies for HES green cellular networks with the help of Lyapunov optimization techniques. The network service cost, which addresses both the grid energy consumption and achievable QoS, is adopted as the performance metric, and it is optimized via base station assignment and power control (BAPC). Our main contribution is a low-complexity online algorithm to minimize the long-term average network service cost, namely, the Lyapunov optimization-based BAPC (LBAPC) algorithm. One main advantage of this algorithm is that the decisions depend only on the instantaneous side information without requiring distribution information of channels and EH processes. To determine the network operation, we only need to solve a deterministic per-time slot problem, for which an efficient inner-outer optimization algorithm is proposed. Moreover, the proposed algorithm is shown to be asymptotically optimal via rigorous analysis. Finally, sample simulation results are presented to verify the theoretical analysis as well as validate the effectiveness of the proposed algorithm.Comment: 15 pages, 8 figures, to appear in IEEE Journal on Selected Areas in Communication

Kernel-Based Adaptive Online Reconstruction of Coverage Maps With Side Information

In this paper, we address the problem of reconstructing coverage maps from path-loss measurements in cellular networks. We propose and evaluate two kernel-based adaptive online algorithms as an alternative to typical offline methods. The proposed algorithms are application-tailored extensions of powerful iterative methods such as the adaptive projected subgradient method and a state-of-the-art adaptive multikernel method. Assuming that the moving trajectories of users are available, it is shown how side information can be incorporated in the algorithms to improve their convergence performance and the quality of the estimation. The complexity is significantly reduced by imposing sparsity-awareness in the sense that the algorithms exploit the compressibility of the measurement data to reduce the amount of data which is saved and processed. Finally, we present extensive simulations based on realistic data to show that our algorithms provide fast, robust estimates of coverage maps in real-world scenarios. Envisioned applications include path-loss prediction along trajectories of mobile users as a building block for anticipatory buffering or traffic offloading.Comment: IEEE Transactions on Vehicular Technology; revised and extended version with new simulation scenari

Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues

As a promising paradigm to reduce both capital and operating expenditures, the cloud radio access network (C-RAN) has been shown to provide high spectral efficiency and energy efficiency. Motivated by its significant theoretical performance gains and potential advantages, C-RANs have been advocated by both the industry and research community. This paper comprehensively surveys the recent advances of C-RANs, including system architectures, key techniques, and open issues. The system architectures with different functional splits and the corresponding characteristics are comprehensively summarized and discussed. The state-of-the-art key techniques in C-RANs are classified as: the fronthaul compression, large-scale collaborative processing, and channel estimation in the physical layer; and the radio resource allocation and optimization in the upper layer. Additionally, given the extensiveness of the research area, open issues and challenges are presented to spur future investigations, in which the involvement of edge cache, big data mining, social-aware device-to-device, cognitive radio, software defined network, and physical layer security for C-RANs are discussed, and the progress of testbed development and trial test are introduced as well.Comment: 27 pages, 11 figure

Energy Efficiency of Downlink Transmission Strategies for Cloud Radio Access Networks

This paper studies the energy efficiency of the cloud radio access network (C-RAN), specifically focusing on two fundamental and different downlink transmission strategies, namely the data-sharing strategy and the compression strategy. In the data-sharing strategy, the backhaul links connecting the central processor (CP) and the base-stations (BSs) are used to carry user messages -- each user's messages are sent to multiple BSs; the BSs locally form the beamforming vectors then cooperatively transmit the messages to the user. In the compression strategy, the user messages are precoded centrally at the CP, which forwards a compressed version of the analog beamformed signals to the BSs for cooperative transmission. This paper compares the energy efficiencies of the two strategies by formulating an optimization problem of minimizing the total network power consumption subject to user target rate constraints, where the total network power includes the BS transmission power, BS activation power, and load-dependent backhaul power. To tackle the discrete and nonconvex nature of the optimization problems, we utilize the techniques of reweighted $\ell_1$ minimization and successive convex approximation to devise provably convergent algorithms. Our main finding is that both the optimized data-sharing and compression strategies in C-RAN achieve much higher energy efficiency as compared to the non-optimized coordinated multi-point transmission, but their comparative effectiveness in energy saving depends on the user target rate. At low user target rate, data-sharing consumes less total power than compression, however, as the user target rate increases, the backhaul power consumption for data-sharing increases significantly leading to better energy efficiency of compression at the high user rate regime.Comment: 14 pages, 8 figures, accepted by JSAC Energy-Efficient Techniques for 5G Wireless Communication Systems special issu