In a wirelessly-powered communication network (WPCN), an energy access point (E-AP) supplies the energy needs of the network nodes through radio frequency wave transmission, and the nodes store their received energy in their batteries for possible data transmission. In this paper, we propose an online control policy for energy transfer from the E-AP to the wireless nodes and for data transfer among the nodes. With our proposed control policy, all data queues of the nodes are stable, while the average energy consumption of the network is shown to be within a bounded gap of the minimum energy required for stabilizing the network. Our proposed policy is designed using a quadratic Lyapunov function to capture the limitations on the energy consumption of the nodes imposed by their battery levels. We show that under the proposed control policy, the backlog level in the data queues and the stored energy level in the batteries fluctuate in small intervals around some constant levels. Consequently, by imposing a negligible average data drop rate, the data buffer size and the battery capacity of the nodes can be significantly reduced.

In this paper, we propose efficient wireless power transfer (WPT) policies for various practical scenarios in wirelessly powered communication networks (WPCNs). First, we consider WPT from an energy access point (E-AP) to multiple energy receivers (E-Rs). We formulate the problem of maximizing the total average received power of the E-Rs subject to power constraints of the E-AP, which is a non-convex stochastic optimization problem. Using eigenvalue decomposition techniques, we derive a closed-form expression for the optimal policy, which requires the distribution of the channel state information (CSI) in the network. We then propose a near-optimal policy that does not require this knowledge and prove that its optimality gap can be decreased at the cost of increment in its convergence time. Next, we consider fairness among the E-Rs and propose a quality of service (QoS) aware fair policy that provides fairness and guarantees the required QoS of each E-R. Finally, we study a WPCN where the E-Rs utilize their received energy to transmit information to the E-AP. We maximize a generic fair network utility under the E-Rs' QoS constraints and the E-AP's power constraints. Numerical results show a significant improvement of O(log N) in the total throughput compared to the state-of-theart baselines.

In a wireless powered communication network, an energy access point (EAP) supplies the energy needs of the network nodes through radio frequency wave transmission, and the nodes store the received energy in their batteries for their future data transmission. In this paper, we propose an online stochastic policy that jointly controls energy transmission from the EAP to the nodes and data transfer among the nodes. Our proposed policy is designed using a quadratic Lyapunov function to capture the limitations on the energy consumption of the nodes imposed by their battery level. The proposed policy is adaptive to different channel statistics of the network. We provide theoretical analysis for the performance of the proposed policy and show that it stabilizes the network. Moreover, the average energy consumption of the network under this policy is within a bounded gap of the minimum energy level required for stabilizing the network.

Radio frequency wireless power transfer (RF- WPT) is an emerging technology that enables transferring energy from a power source to wireless devices over the air. In this paper, we aim at finding {the optimal policy for wireless power transfer (WPT) from an energy access point} (E-AP) to multiple energy receivers (E-Rs) that maximizes the wireless power transfer efficiency. For this purpose, in the first part of the paper, we formulate the problem of maximizing the total average received power of the E-Rs subject to the average and peak power constraints of the E-AP. The formulated problem is a non-convex stochastic optimization problem, and is highly non-trivial. Using some stochastic optimization techniques, we tackle the aforementioned challenges to solve the problem and derive a closed-form expression for the optimal solution, which requires the CSI distribution. In addition, we propose a heuristic algorithm that does not require any explicit information on the CSI distribution. We prove that the proposed algorithm attains a near-optimal solution within a guaranteed gap to the optimal solution. In the second part of the paper, we focus on the problem of considering fairness among the E-Rs and propose two fair policies, namely Max- Min Fair (MMF) policy and Quality-of-service- aware Proportional Fair (QPF) policy. MMF policy maximizes the minimum received power among the E-Rs. Moreover, QPF policy balances the received power levels of different E-Rs as much as possible, while guaranteeing the required minimum QoS for each of them. Various numerical results demonstrate the significant performance of the proposed solutions.

Radio frequency wireless power transfer (RF-WPT) is an emerging technology that enables transferring energy from an energy access point (E-AP) to multiple energy receivers (E-Rs), in a wireless manner. In this paper, we focus on the problem of WPT from a battery-operated E-AP to multiple E-Rs. To save the battery life of the E-AP and at the same time, provide the required quality of service (QoS) for the E-Rs, we aim at finding a proper power transfer policy that minimizes the average transmitted power of the E-AP while providing the required received power of each E-R. We formulate this problem with a stochastic optimisation problem, which is non-convex and has no closed-form expression for the objective function. First, we focus on the special case of single E-R, and derived a closed-form expression for the optimal solution which requires the CSI distribution. Next, for the general case of multiple E-Rs, we propose a Min Drift Plus Penalty (MDPP) algorithm which does not require any information on the CSI distribution. We prove that the proposed algorithm attains a near-optimal solution within a guaranteed gap to the optimal solution. Numerical results demonstrate the significant performance of the proposed solutions.

Providing backhaul connectivity for macro and pico base stations (BSs) constitutes a significant share of infrastructure costs in future heterogeneous networks (HetNets). To address this issue, the emerging idea of flexible backhaul is proposed. Under this architecture, not all the pico BSs are connected to the backhaul, resulting in a significant reduction in the infrastructure costs. In this regard, pico BSs without backhaul connectivity need to communicate with their nearby BSs in order to have indirect accessibility to the backhaul. This makes the radio resource management (RRM) in such networks more complex and challenging. In this paper, we address the problem of cross-layer RRM in HetNets with flexible backhaul. We formulate this problem as a two-timescale non-convex stochastic optimization, which jointly optimizes flow control, routing, interference mitigation, and link scheduling in order to maximize a generic network utility. By exploiting a hidden convexity of this non-convex problem, we propose an iterative algorithm which converges to the global optimal solution. The proposed algorithm benefits from low complexity and low signaling, which makes it scalable. Moreover, due to the proposed two-timescale design, it is robust to the backhaul signaling latency as well. Simulation results demonstrate the significant performance gain of the proposed solution over various baselines.

In this paper, we consider the problem of cross-layer radio resource management in heterogeneous networks (HetNets) with flexible backhaul, which aims at minimizing the total transmit power of base stations (BSs) while guaranteeing the average end-to-end data rate requirement of each data flow. We formulate the problem as a two-time scale stochastic optimisation, where the long-timescale control variables are flow control, routing control and interference coordination, while the short-timescale control variable is instantaneous beamforming within each cell. Using a stochastic cutting plane (SCP) method, we propose a cross-layer queue-state information (QSI) aware radio resource management (RRM) solution in which the long-term controls are updated centrally at radio resource management server (RRMS) without needing to know the global statistical information of the network, and the short-term control variables are updated locally at each BS with only the local instantaneous QSI and channel-state information (CSI) available at each cell. Simulation results show the significant performance gain of our proposed algorithm compared to various baselines.

In this paper, we focus on the problem of hierarchical cross-layer dynamic resource allocation for heterogeneous networks with flexible backhaul. We formulate the radio resource management problem as a two timescale non-convex stochastic optimisation problem which jointly optimizes flow control, routing control, interference mitigation and link scheduling in order to maximize a generic network utility. We propose an iterative hierarchical control structure where the long-term controls are adaptive to large scale fading and the short-term control is adaptive to the local CSI within a pico or macro BS, to find the optimal solution. The proposed solution benefits from low complexity and requires low signalling and message passing among different nodes, which makes it scalable. Moreover, due to the proposed two-timescale hierarchical design, it is robust to the backhaul signalling latency as well. Simulation results demonstrate the significant performance gain of the proposed solution over various baselines.

One of the main advantages of utilizing flexible backhaul in future HetNets is that it can provide better user experience through flexible resource allocation. For this purpose, it is important to provide the required quality of service (QoS) in such networks. In this paper, we consider the problem of cross-layer radio resource management in HetNets with flexible backhaul which guarantees minimum total transmit power of BSs as well as the average end-to-end data rate requirement of each data flow. The problem is formulated as a two-timescale stochastic optimisation, where the long-timescale control variables are flow control, routing control and interference mitigation, while the short-timescale control variable is instantaneous beamforming within each cell. Using stochastic cutting plane, we propose an online cross-layer hierarchical algorithm in which the long-term controls are updated centrally at radio resource management server (RRMS) without needing to know the global statistical information of the network, and the short-term control variables are updated locally at each BS with only the local instantaneous CSI available at each cell. The proposed algorithm has low complexity and signalling overhead. Moreover, simulation results show the significant gains of our proposed algorithm over various baselines.

Energy-aware routing is a promising technique for reducing energy consumption in future networks. Under this scheme, traffic loads are aggregated over a subset of the network links, allowing other links to be turned off to save energy. Since the capacity of links is the main limiting constraint in this problem, to further improve energy saving, the idea of using redundancy elimination (RE) in energy-aware routing has been proposed. As performing RE in routers consumes some energy, it should be specified, which routers should perform RE and which links should be deactivated so that the total energy consumption of the network is minimized. As a result, the problem of energy-aware routing with redundancy elimination, which is known to be NP-hard, arises. In this paper, we first model the problem as a mixed integer linear program (MILP). Since this problem is NP-hard, we propose an efficient heuristic solution. For this purpose, we apply Lagrangian relaxation to the problem and then prove that the obtained formulation is totally unimodular. Under this property, we can relax the integer variables to efficiently determine the solution in polynomial time. Simulation results show the advantages of our proposed heuristic solution over previous ones in terms of approximately twice the energy saving, as well as a lower number of active RE-routers. Furthermore, we show that our method can be applied to the generic energy-aware routing problem (i.e., without RE) as well.

In this paper, we consider a cellular cognitive radio network that uses opportunistic spectrum access for its transmission in the downlink direction from base stations toward cognitive radio users. Since the spectrum is licensed to a primary network, proper resource management should be employed in the secondary network to protect primary users against excessive interference while providing acceptable utilization for secondary users. We use game theory to propose a distributed method for joint frequency and power allocation in the downlink of cognitive radio networks. Earlier works in this area were based on centralized optimization-based approaches, whereas our proposed method solves the problem of joint resource allocation in a distributed way at lower complexity and less message passing. Comparing the proposed decentralized method with a centralized method shows that our proposed joint resource allocation scheme results in significantly less power consumption while supporting more secondary users.