Throughput Optimization in FDD MU-MISO Wireless Powered Communication Networks

In this paper, we consider a frequency-division duplexing (FDD) multiple-user multiple-input-single-output (MU-MISO) wireless-powered communication network (WPCN) consisting of one hybrid data-and-energy access point (HAP) with multiple antennas which coordinates energy/information transfer to/from several single-antenna wireless devices (WD). Typically, in such a system, wireless energy transfer (WET) requires such techniques as energy beamforming (EB) for efficient transfer of energy to the WDs. Yet, efficient EB can only be accomplished if channel state information (CSI) is available to the transmitter, which, in FDD systems is only achieved through uplink (UL) feedback. Therefore, while in our scheme we use the downlink (DL) channels for WET only, the UL channel frames are split into two phases: the CSI feedback phase during which the WDs feed CSI back to the HAP and the WIT phase where the HAP performs wireless information transmission (WIT) via space-division-multiple-access (SDMA). To ensure rate fairness among the WDs, this paper maximizes the minimum WIT data rate among the WDs. Using an iterative solution, the original optimization problem can be relaxed into two sub-problems whose convexity conditions are derived. Finally, the behavior of this system when the number of HAP antennas increases is analyzed. Simulation results verify the truthfulness of our analysis

On Green Energy Powered Cognitive Radio Networks

Green energy powered cognitive radio (CR) network is capable of liberating the wireless access networks from spectral and energy constraints. The limitation of the spectrum is alleviated by exploiting cognitive networking in which wireless nodes sense and utilize the spare spectrum for data communications, while dependence on the traditional unsustainable energy is assuaged by adopting energy harvesting (EH) through which green energy can be harnessed to power wireless networks. Green energy powered CR increases the network availability and thus extends emerging network applications. Designing green CR networks is challenging. It requires not only the optimization of dynamic spectrum access but also the optimal utilization of green energy. This paper surveys the energy efficient cognitive radio techniques and the optimization of green energy powered wireless networks. Existing works on energy aware spectrum sensing, management, and sharing are investigated in detail. The state of the art of the energy efficient CR based wireless access network is discussed in various aspects such as relay and cooperative radio and small cells. Envisioning green energy as an important energy resource in the future, network performance highly depends on the dynamics of the available spectrum and green energy. As compared with the traditional energy source, the arrival rate of green energy, which highly depends on the environment of the energy harvesters, is rather random and intermittent. To optimize and adapt the usage of green energy according to the opportunistic spectrum availability, we discuss research challenges in designing cognitive radio networks which are powered by energy harvesters

Resource Allocation and Fairness in Wireless Powered Cooperative Cognitive Radio Networks

We integrate a wireless powered communication network with a cooperative cognitive radio network, where multiple secondary users (SUs) powered wirelessly by a hybrid access point (HAP) help a primary user relay the data. As a reward for the cooperation, the secondary network gains the spectrum access where SUs transmit to HAP using time division multiple access. To maximize the sum-throughput of SUs, we present a secondary sum-throughput optimal resource allocation (STORA) scheme. Under the constraint of meeting target primary rate, the STORA scheme chooses the optimal set of relaying SUs and jointly performs the time and energy allocation for SUs. Specifically, by exploiting the structure of the optimal solution, we find the order in which SUs are prioritized to relay primary data. Since the STORA scheme focuses on the sum-throughput, it becomes inconsiderate towards individual SU throughput, resulting in low fairness. To enhance fairness, we investigate three resource allocation schemes, which are (i) equal time allocation, (ii) minimum throughput maximization, and (iii) proportional time allocation. Simulation results reveal the trade-off between sum-throughput and fairness. The minimum throughput maximization scheme is the fairest one as each SU gets the same throughput, but yields the least SU sum-throughput.Comment: Accepted in IEEE Transactions on Communication

GATE: Greening At The Edge

Dramatic data traffic growth, especially wireless data, is driving a significant surge in energy consumption in the last mile access of the telecommunications infrastructure. The growing energy consumption not only escalates the operators' operational expenditures (OPEX) but also leads to a significant rise of carbon footprints. Therefore, enhancing the energy efficiency of broadband access networks is becoming a necessity to bolster social, environmental, and economic sustainability. This article provides an overview on the design and optimization of energy efficient broadband access networks, analyzes the energy efficient design of passive optical networks, discusses the enabling technologies for next generation broadband wireless access networks, and elicits the emerging technologies for enhancing the energy efficiency of the last mile access of the network infrastructure.Comment: 7 Pages, 12 Figures, Submitted to IEEE Wireless Communication

Deep Reinforcement Learning for Time Scheduling in RF-Powered Backscatter Cognitive Radio Networks

In an RF-powered backscatter cognitive radio network, multiple secondary users communicate with a secondary gateway by backscattering or harvesting energy and actively transmitting their data depending on the primary channel state. To coordinate the transmission of multiple secondary transmitters, the secondary gateway needs to schedule the backscattering time, energy harvesting time, and transmission time among them. However, under the dynamics of the primary channel and the uncertainty of the energy state of the secondary transmitters, it is challenging for the gateway to find a time scheduling mechanism which maximizes the total throughput. In this paper, we propose to use the deep reinforcement learning algorithm to derive an optimal time scheduling policy for the gateway. Specifically, to deal with the problem with large state and action spaces, we adopt a Double Deep-Q Network (DDQN) that enables the gateway to learn the optimal policy. The simulation results clearly show that the proposed deep reinforcement learning algorithm outperforms non-learning schemes in terms of network throughput

Stackelberg Game for Distributed Time Scheduling in RF-Powered Backscatter Cognitive Radio Networks

In this paper, we study the transmission strategy adaptation problem in an RF-powered cognitive radio network, in which hybrid secondary users are able to switch between the harvest-then-transmit mode and the ambient backscatter mode for their communication with the secondary gateway. In the network, a monetary incentive is introduced for managing the interference caused by the secondary transmission with imperfect channel sensing. The sensing-pricing-transmitting process of the secondary gateway and the transmitters is modeled as a single-leader-multi-follower Stackelberg game. Furthermore, the follower sub-game among the secondary transmitters is modeled as a generalized Nash equilibrium problem with shared constraints. Based on our theoretical discoveries regarding the properties of equilibria in the follower sub-game and the Stackelberg game, we propose a distributed, iterative strategy searching scheme that guarantees the convergence to the Stackelberg equilibrium. The numerical simulations show that the proposed hybrid transmission scheme always outperforms the schemes with fixed transmission modes. Furthermore, the simulations reveal that the adopted hybrid scheme is able to achieve a higher throughput than the sum of the throughput obtained from the schemes with fixed transmission modes

Throughput Maximization for Two-way Relay Channels with Energy Harvesting Nodes: The Impact of Relaying Strategies

In this paper, we study the two-way relay channel with energy harvesting nodes. In particular, we find transmission policies that maximize the sum-throughput for two-way relay channels when the relay does not employ a data buffer. The relay can perform decode-and-forward, compress-and-forward, compute-and-forward or amplify-and-forward relaying. Furthermore, we consider throughput improvement by dynamically choosing relaying strategies, resulting in hybrid relaying strategies. We show that an iterative generalized directional water-filling algorithm solves the offline throughput maximization problem, with the achievable sum-rate from an individual or hybrid relaying scheme. In addition to the optimum offline policy, we obtain the optimum online policy via dynamic programming. We provide numerical results for each relaying scheme to support the analytic findings, pointing out to the advantage of adapting the instantaneous relaying strategy to the available harvested energy.Comment: accepted for publication in IEEE Transactions on Communications, April 19, 201

Uplink Time Scheduling with Power Level Modulation in Wireless Powered Communication Networks

In this paper, we propose downlink signal design and optimal uplink scheduling for the wireless powered communication networks (WPCNs). Prior works give attention to resource allocation in a static channel because users are equipped with only energy receiver and users cannot update varying uplink schedulling. For uplink scheduling, we propose a downlink signal design scheme, called a power level modulation, which conveys uplink scheduling information to users. First, we design a downlink energy signal using power level modulation. Hybrid-access point (H-AP) allocates different power level in each subslot of the downlink energy signal according to channel condition and users optimize their uplink time subslots for signal transmission based on the power levels of their received signals. Further, we formulate the sum throughput maximization problem for the proposed scheme by determining the uplink and downlink time allocation using convex optimization problem. Numerical results confirm that the throughput of the proposed scheme outperforms that of the conventional schemes

Joint Link Scheduling and Brightness Control for Greening VLC-based Indoor Access Networks

Demands for broadband wireless access services is expected to outstrip the spectrum capacity in the near-term - "spectrum crunch". Deploying additional femotocells to address this challenge is cost-inefficient, due to the backhaul challenge and the exorbitant system maintenance. According to an Alcatel-Lucent report, most of the mobile Internet access traffic happens indoor. Leveraging power line communication and the available indoor infrastructure, visible light communication (VLC) can be utilized with small one-time cost. VLC also facilitates the great advantage of being able to jointly perform illumination and communications, and little extra power beyond illumination is required to empower communications, thus rendering wireless access with small power consumption. In this study, we investigate the problem of minimizing total power consumption of a general multi-user VLC indoor network while satisfying users' traffic demands and maintaining an acceptable level of illumination. We utilize the column generation method to obtain an $\epsilon$-bounded solution. Several practical implementation issues are integrated with the proposed algorithm, including different configurations of light source and ways of resolving the interference among VLC links. Through extensive simulations, we show that our approach reduces the power consumption of the state-of-art VLC-based scheduling algorithms by more than 60\% while maintaining the required illumination

RF-Powered Cognitive Radio Networks: Technical Challenges and Limitations

The increasing demand for spectral and energy efficient communication networks has spurred a great interest in energy harvesting (EH) cognitive radio networks (CRNs). Such a revolutionary technology represents a paradigm shift in the development of wireless networks, as it can simultaneously enable the efficient use of the available spectrum and the exploitation of radio frequency (RF) energy in order to reduce the reliance on traditional energy sources. This is mainly triggered by the recent advancements in microelectronics that puts forward RF energy harvesting as a plausible technique in the near future. On the other hand, it is suggested that the operation of a network relying on harvested energy needs to be redesigned to allow the network to reliably function in the long term. To this end, the aim of this survey paper is to provide a comprehensive overview of the recent development and the challenges regarding the operation of CRNs powered by RF energy. In addition, the potential open issues that might be considered for the future research are also discussed in this paper.Comment: 8 pages, 2 figures, 1 table, Accepted in IEEE Communications Magazin