A distributed joint scheduling and power control algorithm for multicasting in wireless ad hoc networks

In this paper, we investigate a joint scheduling and power control for a multi-cell cognitive radio network (CRN) in coexistence with a cellular primary radio network (PRN). The PRN uses a set of licensed orthogonal channels for transmission. This set of channels is also accessed in an opportunistic manner by a set of cognitive radio base stations (CR-BSs) to support secondary users (SUs). The problem is formulated to maximize the spectrum utilization of SUs without causing excessive interference to active primary users (PUs) of the PRN. In addition, all the served SUs must meet a certain QoS. A centralized solution for joint scheduling and power control is derived to give the global accessing decision for all unserved SUs. With the assumption that the knowledge of all subscribers is available, the coordinator of the CRN can use the joint scheduling and power control algorithm to maximize the spectrum utilization of served SUs by solving a mixed-integer linear programming (MILP) with an NP-hard complexity. To avoid the NP-hard complexity, we propose a sub-optimal heuristic algorithm that can be obtained at a much lower complexity based on the coloring interference graph among unserved SUs effected by served SUs and active PUs. Simulation results show that the proposed algorithm can yield the performance close to the optimal solution's.

In this paper, we investigate a joint scheduling and power control for an infrastructure-based cognitive radio network (CRN) in coexistence with a cellular primary radio network (PRN). The PRN uses a set of licensed orthogonal channels for transmission. This set of channels is also accessed in an opportunistic manner by a set of cognitive radio base stations (CR-BSs) to support secondary users (SUs). The problem is formulated to maximize the spectrum utilization of SUs without causing excessive interference to active primary users (PUs) of the PRN. In addition, all the served SUs must meet a certain QoS. A centralized solution for joint scheduling and power control is derived to give the global accessing decision for all unserved SUs. With the assumption that the knowledge of all subscribers is available, the coordinator of the CRN can use the joint scheduling and power control algorithm to maximize the spectrum utilization of served SUs by solving a mixed-integer linear programming (MILP) with an NP-hard complexity. To avoid the NP-hard complexity, we propose a sub-optimal heuristic greedy algorithm that can be obtained at a much lower complexity based on the coloring interference graph among unserved SUs effected by served SUs and active PUs. Its superior performance oveover other related algorithms is demonstrated through simulations.

We consider the problem of joint routing, scheduling and power control in multi-hop wireless networks. We use a linear relation between link capacity and signal to interference noise ratio in our formulation. In a previous work, using a duality approach, the optimal link scheduling and power control that minimizes the total average transmission power is found. We formulate this problem as a linear programming problem with exponential number of constraints. To cope with the exponential number of constraints, we propose an iterative algorithm based on the cutting plane method. The separation oracle for the cutting plane algorithm turns out to be an element-wise concave optimization problem that can be effectively solved using branch and bound algorithm. We extend the same method to find the optimal routing scheduling and power control. Simulation results show that this methodology is more efficient and scalable compare to the previously proposed algorithm.

Joint scheduling and power control schemes have previously been proposed to reduce power dissipation in wireless ad hoc networks. However, instead of power consumption, throughput is a more important performance concern for some emerging multihop wireless networks, such as wireless mesh networks. This paper examines joint link scheduling and power control with the objective of throughput improvement. The MAximum THroughput link Scheduling with Power Control (MATH-SPC) problem is first formulated and then a mixed integer linear programming (MILP) formulation is presented to provide optimal solutions. However, simply maximizing the throughput may lead to a severe bias on bandwidth allocation among links. To achieve a good tradeoff between throughput and fairness, a new parameter called the demand satisfaction factor (DSF) to characterize the fairness of bandwidth allocation and formulate the MAximum Throughput fAir link Scheduling with Power Control (MATA-SPC) problem is defined. An MILP formulation and an effective polynomial-time heuristic algorithm, namely, the serial linear programming rounding (SLPR) heuristic, to solve the MATA-SPC problem are also presented. Numerical results show that bandwidth can be fairly allocated among all links/flows by solving the MILP formulation or by using the heuristic algorithm at the cost of a minor reduction of network throughput. In addition, extensions to end-to-end throughput and fairness and multiradio wireless multihop networks are discussed.

International audience

Traditional beamforming and power control algo-rithms in cognitive radio (CR) are based on the assumption of perfect channel state information (CSI) however; this may lead to performance degradation in realistic systems.In this paper, the problem of joint beamforming and power control is investigated in underlay CR networks with imperfect CSI.Our objective is to maximize the sum utility of secondary users (SUs) under the primary users (PUs) interference power constraints and the transmission power constraint of SUs.First, the joint beamforming and power control problem is formulated under game theory framework, where the SUs compete with each other over the beamforming vectors and transmission power level made available by the PUs.Moreover, the channel uncertainty is described using ellipsoid sets and the interference power constraints can be converted into robust interference power constraints.Besides, Nash equilibrium (NE) is considered as the solution of this game.Finally, simulation results show that the proposed scheme can converge to a locally optimal pair of beamforming vector and transmission power level in the presence of channel uncertainty.

Throughput is an important performance consideration for multihop wireless networks. In this paper, we study the joint link scheduling and power control problem, focusing on maximizing the network throughput. We formulate the maximum throughput link scheduling with power control (MATH-SPC) problem, and present a mixed integer linear programming (MILP) formulation to provide optimal solutions. However, simply maximizing the throughput leads to a severe bias on bandwidth allocation among all links. In order to enhance both throughput and fairness, we define a new parameter, the demand satisfaction factor (DSF), to characterize the fairness of bandwidth allocation. We formulate the maximum throughput fair link scheduling with power control (MATA-SPC) problem and present an MILP formulation for this problem. We also present an effective polynomial time heuristic algorithm, namely, the serial LP rounding (SLPR) heuristic. Our numerical results show that bandwidth can be fairly allocated among all links/flows by solving our MATA-SPC formulation or using our heuristic algorithm at the cost of a minor reduction of network throughput

In this paper, we study joint link scheduling and power control with successive interference cancellation (SIC), aiming at maximizing the α-utility. The joint link scheduling and power control with SIC (PCSIC) problem is formulated to be a mixed-integer non-linear programming (MINLP), which is NP-hard. In order to solve the problem, we first decompose the MINLP into three sub-problem and then propose an iterative algorithm. We compare our strategy with the scheme without power control from the perspective of system throughput, fairness index and energy consumption. Numerical results show the noticeable performance improvement of the proposed strategy.

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.

We address the problem of joint downlink beamforming and power control in a frequency flat multi-antenna broadcast channel. In this context, a common optimization strategy is to maximize the system throughput under a certain power constraint. This, however, does not guarantee a fair sharing of throughput among the users, which is required in case that the receivers cannot cooperate. Thus, we propose a different approach, which always assumes optimally balanced data rates. If the problem is feasible, i.e., desired target rates can be reached, spare degrees of freedom can be used in order to minimize the total transmission power. This strategy can even be applied if only spatial covariance channel information is available. The authors have developed a theoretical framework based upon uplink/downlink duality, feasibility considerations, and bounds on the maximal eigenvalue of the resulting coupling matrix. An overview of the results is given and they are discussed in the context of existing literature. Finally, a theoretical benchmark is provided for the case of perfect channel knowledge. Then, beamforming can be combined with Costa (1983) pre-coding, as proposed by Caire and Shamai (see 36th Asilomar Conf. on Signals, Systems and Computers Asilomar Pacific Grove, CA, Nov. 2001). This leads to a closed-form solution of the problem.

Ad hoc network is an extensive research area in wireless communications nowadays, and is particularly studied under energy constraints, since energy consumption of the network should be minimized. The physical layer (power control), link layer (scheduling), and network layer (routing) are known to have great impact on network performance as well as on power consumption. In this paper, we present an optimization problem that there may exist a power vector that minimizes the overall transmission power of the network which at the same time supports a particular data rate on each link and satisfies the SINR and maximum transmission power constraints at each node. We also formulate an optimization problem where we use both the energy consumed of each node in the routing path and the delay associated on each link as link costs. We provide numerical examples for our schemes and relationships between the parameters. An overview of our intended routing algorithm for ad hoc networks is also given as a preliminary step towards our distributed joint scheduling, power control and routing algorithm for ad hoc networks using cross layer approach.

This paper considers the open problem of joint power control, beamforming, and base station assignment to achieve an optimal SIR assignment in uplink cellular networks. The adaptive beamforming and adaptive base station assignment strategies in addition to power control will broaden the feasible SIR region. The problem is challenging, since the feasible SIR region is the union of regions from all possible combinations of beamformer and base station assignment, which is generally not log-convex. By using concave interference function property, the Pareto boundary of the feasible SIR region of joint power control, beamforming, and base station assignment is characterized. Since the optimal SIR assignment solution must lie on the Pareto boundary of the feasible SIR region to achieve the highest possible efficiency, the optimization problem can be reduced to the optimization along the Pareto optimal boundary. Thus instead of searching the operating point over the whole feasible SIR region, a distributed greedy algorithm that searches a possible optimal solution on the Pareto boundary is developed. From the simulation, we show that the performance gain obtained from joint power control, beamforming, and base station assignment is quite significant.

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

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.