Problem Of Joint Power Control Research Articles

Robust Design of Multicell D2D Communication Under Partial CSI

We consider device-to-device (D2D) communication underlaid in a cellular network to share the uplink resource of cellular users (CUs). It is a key the emerging Internet of Things to support vehicle-to-everything communication networks. In a multicell scenario, both D2D pairs and CUs may cause significant significant intercell interference (ICI) to the neighboring cells. Furthermore, due to substantial signaling overhead, we assume only partial channel state information (CSI) of D2D links at the base station. We consider joint power control, beamforming, and CU-D2D matching problem, assuming partial CSI from D2D pairs under the general Nakagami fading model. We formulate a joint receive beamforming and robust power control optimization problem for a CU-D2D pair to expected sum rate under the power budget, while meeting the minimum SINR requirements and worst case ICI limits at neighboring cells in a probabilistic sense. We propose an efficient algorithm that combines an iterative D2D feasibility check and a ratio-of-expectation approximation. A performance upper bound is also developed for benchmarking. For multiple CUs and D2D pairs, due to orthogonal channelization within each cell, we first focus on the problem of joint power control and beamforming for a CU-D2D pair and show how our proposed solution can be leveraged to find a solution for this general problem. The complexity analysis of the proposed approach is also provided. Simulation results show that the proposed algorithm gives performance close to the upper bound.

Deep Reinforcement Learning-Based Dynamic Resource Management for Mobile Edge Computing in Industrial Internet of Things

Nowadays, driven by the rapid development of smart mobile equipments and 5G network technologies, the application scenarios of Internet of Things (IoT) technology are becoming increasingly widespread. The integration of IoT and industrial manufacturing systems forms the industrial IoT (IIoT). Because of the limitation of resources, such as the computation unit and battery capacity in the IIoT equipments (IIEs), computation-intensive tasks need to be executed in the mobile edge computing (MEC) server. However, the dynamics and continuity of task generation lead to a severe challenge to the management of limited resources in IIoT. In this article, we investigate the dynamic resource management problem of joint power control and computing resource allocation for MEC in IIoT. In order to minimize the long-term average delay of the tasks, the original problem is transformed into a Markov decision process (MDP). Considering the dynamics and continuity of task generation, we propose a deep reinforcement learning-based dynamic resource management (DDRM) algorithm to solve the formulated MDP problem. Our DDRM algorithm exploits the deep deterministic policy gradient and can deal with the high-dimensional continuity of the action and state spaces. Extensive simulation results demonstrate that the DDRM can reduce the long-term average delay of the tasks effectively.

Joint resource allocation algorithm based on multi-objective optimization for wireless sensor networks

With the limitations of the network resources and battery energy of wireless sensors, the competition of resources in the process of communication will increase the network energy consumption and reduce the Quality of Service (QoS), resulting in that the application of Multi-Radio Multi-Channel (MRMC) Wireless Sensor Networks (WSNs) face many challenges. In this paper, we concentrate on the resource allocation of joint time slot assignment, channel allocation and power control for MRMC WSNs. Due to the diversity of research objectives and the computational complexity of the non-convex problem, this paper develops a two-stage resource allocation optimization algorithm by analyzing the interdependence of various resources. Specifically, to exchange information with conflict-free transmission among all sensors, a graph coloring algorithm for time slot assignment is designed firstly. Then based on the first stage of this algorithm, the problem of joint power control and channel allocation is studied and formulated as a multi-objective optimization problem to achieve the trade-off between energy efficiency and network capacity maximization under the constraints of link interference and load balance. Multi-objective hybrid particle swarm optimization is introduced to obtain the Pareto optimal solutions. The simulation results show that the proposed algorithm significantly performs better in terms of achieving the trade-off of multi-performance.

Joint Power Control and Time Switching for SWIPT Systems With Heterogeneous QoS Requirements

The problem of joint power control and time switching is studied for a simultaneous wireless information and power transferring system that supports wireless nodes (WNs) with heterogeneous traffic. Using the stochastic optimization theory, a dynamic algorithm, which can trade average power consumption for WNs’ delay, is proposed to allocate the transmission power and time switching factor jointly. A control parameter is defined in the dynamic algorithm to facilitate the tradeoff.

A Data Rate and Concurrency Balanced Approach for Broadcast in Wireless Mesh Networks

In this paper, we address the problem of joint power control and scheduling for minimizing broadcast delay in wireless mesh networks. Given a set of mesh routers and a routing tree, we aim to assign power for relay nodes and compute an optimal transmission schedule such that the total delay for a packet broadcast from the root to all the routers is minimized. We consider rate adaptation in our scheme. This is a difficult issue. High power enables high data rate but causes high interference, whereas low power allows more concurrent transmissions at the expense of data rate. We study the tradeoff between data rate and concurrency and propose a balanced method. We introduce a metric called standard deviation of average remaining broadcast time to determine the priority of the two parameters. When this metric is greater than a threshold, the nodes will take the data-rate-first approach to increase the data rate; otherwise, the concurrency-first approach will be used to increase the number of concurrent transmissions. Theoretical analysis is given to show the upper and lower bounds of this metric. Simulations have demonstrated that our proposed method can reduce the broadcast delay significantly as compared with existing methods.

Joint power control and spectrum sensing for capacity maximisation in spectrum sharing systems

We study the problem of joint power control and spectrum sensing for maximising the capacity at the secondary user while protecting the primary user's transmissions in spectrum sharing cognitive radio systems. Power control regulates the transmission power of the secondary user and spectrum sensing regulates the sensing time and the sensing threshold that care for the primary user's protection. This problem is a capacity maximisation problem that we formulate and solve using an iterative greedy algorithm due to its complex form. The solution of the proposed algorithm leads to the global optimal solution that represents the optimal triplet values of transmission power, sensing time and threshold. The obtained results show the potential capacity maximisation that is achieved by the proposed joint design as long as the primary user's protection is provided. Finally the convergence behaviour of the proposed algorithm is assessed in terms of the needed iterations for enhancing the capacity of spectrum sharing systems.

Joint power control and beamforming for cognitive radio networks

We consider a secondary usage of spectrum scenario where a secondary network coexists and/or shares the radio spectrum with a primary network to which the spectrum is licensed. The secondary network uses cognitive radio technology to make opportunistic access of the unused and/or underutilized portions of the spectrum. For such a system, we study the problem of joint power control and beamforming with the objective of minimizing the total transmitted power of the cognitive network such that the received interferences at primary users remain below a threshold level as well as the secondary users who are admitted in the system are guaranteed with their signal to interference plus noise ratio (SINR) requirements. Two iterative algorithms, one based on weighted least square (WLS) approach and the other based on admission control for secondary users, are provided to meet the strict interference protection requirement of the primary users. A numerical study is performed to show the convergence behavior of the iterative algorithms.