GA-based scheme for fair joint channel allocation and power control for underlaying D2D multicast communications

Efficient spectrum resource management in cognitive radio networks (CRNs) is a promising method that improves the utilization of spectrum resource. In particular, the power control and channel allocation are of top priorities in spectrum resource management. Nevertheless, the joint design of power control and channel allocation is an NP-hard problem and the research is still in the preliminary stage. In this paper, we propose a novel joint approach based on long short-term memory deep Q network (LSTM-DQN). Our objective is to obtain the channel allocation schemes of the access points (APs) and the power control strategies of the secondary users (SUs). Specifically, the received signal strength information (RSSI) collected by the microbase stations is used as the input of LSTM-DQN. In this way, the collection of RSSI can be shared between users. After the training is completed, the APs are capable of selecting channels with small interference while the SUs may access the authorized channels in an underlay operation mode without knowing any knowledge about the primary users (PUs). Experimental results show that the channels are allocated to the APs with a lower probability of collision. Moreover, the SUs can adjust their power control strategies quickly to avoid the harmful interference to the PUs when the environment parameters change randomly. Consequently, the overall performance of CRNs and the utilization of spectrum resources are improved significantly compared to existing popular solutions.

This paper considers a cognitive radio (CR) network comprising one cognitive base station (CBS) and a number of secondary users (SUs) that shares a set of channels with a primary network. Both uplink and downlink scenarios are considered in the CR network. The objective is to maximize the throughput of the CR network while protecting the primary transmission and maintaining fairness among the SUs. To fairly allocating resources among the SUs, minimum/maximum number of channels constraint of the SU is introduced to flexibly distribute available channels between the SUs. For both uplink and downlink scenarios, we propose a joint channel allocation and power control scheme. Additionally, a low-complexity two-phase channel allocation and power control scheme is also proposed in the downlink scenario. Simulation results are obtained to validate the performance of the proposed schemes. It is shown that the proposed schemes offer flexibility and controllability in fairly allocating channels to the SUs and yet achieve high throughput. Furthermore, it is shown that the performance gap between the joint scheme and the two-phase scheme is negligible.

This paper presents a game theoretic solution for joint channel allocation and power control in cognitive radio networks. The problem is analyzed under the physical interference model. The objective is to find a distributed solution that maximizes the network utility with limited information. We consider different criteria to define this network utility: capacity in bps and number of links fulfilling the SINR requirements. We propose a game with local information that provides similar performance to a potential game that requires global knowledge. The obtained results are compared with a heuristic genetic algorithm to show the correctness of the proposals.

For device-to-device (D2D) communication under laid in a cellular network with uplink resource sharing, both cellular and D2D pairs may cause significant inter-cell interference (ICI) at a neighboring base station (BS). In this paper, under optimal BS receive beamforming, we jointly optimize the power of a cellular user (CU) and a D2D pair for their sum rate maximization, while satisfying minimum SINR requirements and worst-case ICI limit in multiple neighboring cells. We solve this non-convex joint optimization problem in two steps. First, the necessary and sufficient condition for the D2D admissibility under given constraints is obtained. Finally, we consider joint power control of the CU and D2D transmitters. We propose a power control algorithm to maximize the sum rate. Depending on the severity of ICI that D2D and CU may cause, we categorize the feasible solution region into five cases, each of which may further include several scenarios based on minimum SINR requirements. The proposed algorithm is optimal when ICI to a single neighboring cell is considered. For multiple neighboring cells, we provide an upper bound on the performance loss by the proposed algorithm and conditions for its optimality. We further extend our consideration to the scenario of multiple CUs and D2D pairs, and formulate the joint power control and CU-D2D matching problem. We show how our proposed solution for one CU and one D2D pair can be utilized to solve this general joint optimization problem. Simulation demonstrates the effectiveness of our power control algorithm and the nearly optimal performance of the proposed approach in the setting of multiple CUs and D2D pairs.

Joint power control and channel assignment in uplink IoT Networks: A non-cooperative game and auction based approach

A joint power and rate control using game theory algorithm is proposed in heterogeneous networks environment with interference among different systems. It designs a novel revenue function based on the fairness of users' power control and the rate allocation, accompanying with the interference of the primary users caused by different users. The existence and uniqueness of the Nash equilibrium of game theory are derived by rigorous mathematical. Furthermore, simulation results show that the proposed algorithm can improve users' power control and rate allocation significantly.

Transmitter adaptation in conjunction with power control enables individual users in multi-user wireless systems to meet specific quality of service (QoS) constraints imposed in terms of target values for the users' signal-to-interference+noise-ratio (SINR) at their intended receiver. Existing algorithms for joint transmitter adaptation and power control require knowledge of channel state information (CSI) which is in general difficult to acquire in wireless environments that are subject to fading. In this paper we study the performance of incremental algorithms for joint transmitter adaptation and power control with fading channels by formally defining a probability of outage measure and evaluating it through Monte-Carlo simulations in specific scenarios.

Joint admission and power control for quality-of-service in the wireless downlink

Joint power control and spectrum allocation for cognitive radio networks via pricing

Joint mode selection and power control for D2D underlaid cellular networks

In this paper, we address the joint channel and power allocation problem for Device-to-Device (D2D) communications underlaying uplink cellular networks. We aim to maximize the sum-rate of D2D communications while fulfilling the QoS requirements for both cellular users (CUs) and D2D pairs (DUs). We first propose a heuristic channel assignment algorithm in which a cellular channel is allowed to be shared by multiple DUs and every DU can reuse multiple channels. Given this assigned channels, a price-based Stackelberg game is then formulated to obtain the sub-optimal transmission power for DUs. The simulation results demonstrate that our proposed joint channel and power allocation scheme has a superiority over a few existing resource allocation schemes for D2D communications in terms of D2D sum-rate.

This paper studies the two-hop relay broadcast channel (BC) for a relay-assisted wireless cellular network where the multiuser independent downlink signals from the base station (BS) are first transmitted to a fixed relay station (RS), and then forwarded by the RS to multiple mobile users. Assuming both the BS and RS are equipped with multiantennas, we study the joint optimization of linear beamforming and power control at the BS and RS so as to minimize their weighted sum-power consumption under the user minimum signal-to-interference-noise-ratio (SINR) - quality-of-service (QoS) - constraints. We apply two well-known criteria in the literature, namely, the ldquoSINR balancingrdquo and the ldquochannel-inversion,rdquo for the design of linear precoding in the traditional nonrelay-assisted multiantenna BC to the relay-assisted multiantenna BC. First, a convergence-ensured iterative SINR-balancing algorithm is proposed to successively in turn optimize the transmit parameters at one station (BS or RS) with those at the other station being fixed. Second, a joint BS and RS channel-inversion algorithm is proposed together with a novel technique, termed ldquoeigenmode switching,rdquo at the RS to reduce the power penalty of the channel inversion. Simulation results show that the proposed joint beamforming and power control schemes provide substantial power savings to achieve the assigned user QoS constraints.

Multicast device-to-device communications operating underlay with cellular networks is a spectral efficient technique for disseminating data to nearby receivers. However, due to the critical challenge of having an intelligent interference coordination between multicast groups along with the cellular network, it is necessary to judiciously perform resource allocation for the combined network. In this work, we present a framework for a joint channel and power allocation strategy to maximize the sum rate of the combined network while guaranteeing minimum rate to individual groups and cellular users. The objective function is augmented by an austerity function that penalizes excessive assignment of low rate channels. The formulated problem is a mixed-integer-non-convex program, which requires exponential complexity to obtain the optimal solution. To tackle this, we exploit fractional programming and integer relaxation to obtain a parametric convex approximation. Based on sequential convex approximation approach, we first propose a centralized algorithm that ensures convergence to a limit point. Next, we propose a distributed algorithm in which via dual decomposition, separable sub-problems are formulated to be solved at the respective groups in cooperation with the base station. We provide convergence guarantees of the proposed solutions and demonstrate their merits by simulations, showing improvement in network throughput.

This paper studies joint beamforming and power control in a multiuser MISO interference network with statistical channel information. Such information consists of the slow-varying covariance matrices in the beamforming network, and can be employed to reduce instantaneous feedback needs. With the outage event induced by the utilization of statistical channel information, we optimize signal transmission strategies to minimize the maximum outage probability under weighted sum power constraint to achieve outage balancing in the interference network. Under the condition of fixed beamformer, we use nonlinear Perron-Frobenius theory to present a decentralized algorithm with provable geometrically fast convergence rate to compute the optimal power. Since the joint beamformer and power optimization problem is non-convex, we examine its certainty-equivalent margin counterpart. By leveraging nonlinear Perron-Frobenius theory and the established network duality, we present a near-optimal decentralized algorithm to jointly optimize the beamformer and power. The algorithm converges quickly and the convergence rate of the algorithm is proven to be geometrical.

In this paper, we present a game-theoretic approach for the purpose of deriving the problem of joint beamforming and power control in cognitive radio (CR) multiple-input multiple-output (MIMO) broadcast channels (CR MIMO-BCs), where the primary users (PUs) coexist with the secondary users (SUs) and they share the same spectrum. The cognitive base station (CBS), which is equipped with multiple antennas, is capable of transmitting data to the SU’s multiple-antenna receiver by employing the technology of beamforming. The proposed approach is an application of separable games, which are formally stated by the subgames of beamforming and power control. Furthermore, based on the model of noncooperative separate games, separable cost functions for the parameters of beamforming and power control are also proposed, showing that these cost functions are convex. Therefore, the convex theory of a noncooperative game can be employed to investigate the best response strategies as well as existence of Nash equilibrium solutions. Finally, we propose an iterative algorithm to achieve the optimal Nash equilibrium of the proposed joint beamforming subgame and power control subgame. Numerical results verify both the convergence and the tracking properties of the proposed algorithm for variant scenarios.