Li Ping Qian

MAPEL: Achieving global optimality for a non-convex wireless power control problem

Achieving weighted throughput maximization (WTM) through power control has been a long standing open problem in interference-limited wireless networks. The complicated coupling between the mutual interferences of links gives rise to a non-convex optimization problem. Previous work has considered the WTM problem in the high signal to interference-and-noise ratio (SINR) regime, where the problem can be approximated and transformed into a convex optimization problem through proper change of variables. In the general SINR regime, however, the approximation and transformation approach does not work. This paper proposes an algorithm, MAPEL, which globally converges to a global optimal solution of the WTM problem in the general SINR regime. The MAPEL algorithm is designed based on three key observations of the WTM problem: (1) the objective function is monotonically increasing in SINR, (2) the objective function can be transformed into a product of exponentiated linear fraction functions, and (3) the feasible set of the equivalent transformed problem is always ldquonormalrdquo, although not necessarily convex. The MAPEL algorithm finds the desired optimal power control solution by constructing a series of polyblocks that approximate the feasible SINR region in an increasing precision. Furthermore, by tuning the approximation factor in MAPEL, we could engineer a desirable tradeoff between optimality and convergence time. MAPEL provides an important benchmark for performance evaluation of other heuristic algorithms targeting the same problem. With the help of MAPEL, we evaluate the performance of several existing algorithms through extensive simulations.

Monotonic Optimization in Communication and Networking Systems

Optimization has been widely used in recent design of communication and networking systems. One major hurdle in this endeavor lies in the nonconvexity of many optimization problems that arise from practical systems. To address this issue, we observe that most nonconvex problems encountered in communication and networking systems exhibit monotonicity or hidden monotonicity structures. A systematic use of the monotonicity properties would substantially alleviate the difficulty in obtaining the global optimal solutions of the problems. This monograph provides a succinct and accessible introduction to monotonic optimization, including the formulation skills and solution algorithms. Through several application examples, we will illustrate modeling techniques and algorithm details of monotonic optimization in various scenarios. With this promising technique, many previously difficult problems can now be solved with great efficiency. With this monograph, we wish to spur new research activities in broadening the scope of application of monotonic optimization in communication and networking systems. Full text available at: http://dx.doi.org/10.1561/1300000038

Globally Optimal Distributed Power Control for Nonconcave Utility Maximization

We consider a distributed power control algorithm for infrastructureless ad hoc wireless networks, where each link distributively and asynchronously updates its transmission power with limited message passing among links. This algorithm provably converges to the set of global optimal solutions despite the non-convexity of the power control problem. In contrast with existing distributed power control algorithms, our algorithm makes no stringent assumptions on the system utility functions. In particular, the utility function is allowed to be concave or non-concave, differentiable or non-differentiable, continuous or discontinuous, and monotonic or non-monotonic.

Dynamic Cell Association for Non-Orthogonal Multiple-Access V2S Networks

To meet the growing demand of mobile data traffic in vehicular communications, the vehicle-to-small-cell (V2S) network has been emerging as a promising vehicle-to-infrastructure technology. Since the non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) can achieve superior spectral and energy efficiency, massive connectivity and low transmission latency, we introduce the NOMA with SIC to V2S networks in this paper. Due to the fast vehicle mobility and varying communication environment, it is important to dynamically allocate small-cell base stations and transmit power to vehicular users with considering the vehicle mobility in NOMA-enabled V2S networks. To this end, we present the joint optimization of cell association and power control that maximizes the long-term system-wide utility to enhance the long-term system-wide performance and reduce the handover rate. To solve this optimization problem, we first equivalently transform it into a weighted sum rate maximization problem in each time frame based on the standard gradient-scheduling framework. Then, we propose the hierarchical power control algorithm to maximize the equivalent weighted sum rate in each time frame based on the Karush–Kuhn–Tucker (KKT) optimality conditions and the idea of successive convex approximation. Finally, theoretical analysis and simulation results are provided to demonstrate that the proposed algorithm is guaranteed to converge to the optimal solution satisfying KKT optimality conditions.

Energy-Efficient Distributed User Scheduling in Relay-Assisted Cellular Networks

Relay-assisted access technique has been proposed as a promising solution to improve the energy efficiency and service quality of edge users for cellular networks. In this paper, we aim to find the optimal scheduling period, optimal power allocation, and optimal user scheduling and relay selection that minimizes the total power consumption under the constraints of minimum data rate requirements for the single-cell relay-assisted cellular network. Although we assume that every user in the network is interference-free with each other due to orthogonal resource allocation, such an optimization problem is in general a mixed-integer programming, and thus the optimal solution is difficult to achieve. To make the optimization problem tractable, we decompose the problem into the power allocation optimization subproblem and the joint user scheduling and relay selection optimization subproblem. First, we obtain the optimal scheduling period approximately equal to the ratio between the number of users and the number of relays by sequentially solving these two subproblems. Furthermore, we propose a distributed joint user scheduling and relay selection algorithm based on the duality theory and auction theory. The theoretical results show that the proposed algorithm can help every user select the optimal relay and transmission time slot in polynomial time. Simulation results further show that the proposed algorithm can guarantee the minimum scheduling duration without consuming more transmit power, in comparison with other existing algorithms.

Transmit Power Minimization for Outage-Constrained Relay Selection over Rayleigh-Fading Channels

This letter studies the relay selection scheme for dual-hop decode-and-forward (DF) relay networks over Rayleigh fading channels. In particular, we first provide the problem formulation that minimizes the total transmit power consumption under the constraints of maximum tolerable outage probability and limited power when relay k cooperates. We then derive a closed-form expression of optimal power allocation that solves this optimization problem. Based on the derived closed-form expression, we can quickly select the best relay that consumes the minimum power during the cooperation of transmission.

Optimal Throughput-Oriented Power Control by Linear Multiplicative Fractional Programming

This paper studies optimal power control for throughput maximization in wireless ad hoc networks. Optimal power control problem in ad hoc networks is known to be non-convex due to the co-channel interference between links. As a result, a global optimal solution is difficult to obtain. Previous work either simplified the problem by assuming that the signal- to-interference-and-noise-radio (SINR) of each and every link is much higher than 1, or settled for suboptimal solutions. In contrast, we propose a novel methodology to compute the global optimal power allocation in a general SINR regime. In particular, we formulate the problem into an equivalent linear multiplicative fractional programming (LMFP). A global optimization algorithm, referred to as LMFP-based power allocation (LBPA) algorithm, is proposed to solve the LMFP with reasonable computational complexity. Our analysis proves that the LBPA algorithm is guaranteed to converge to a global optimal solution. Through extensive simulations, we show that the proposed algorithm significantly improves the throughput of wireless networks compared with existing ones.

Joint Uplink Base Station Association and Power Control for Small-Cell Networks With Non-Orthogonal Multiple Access

Since non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) can achieve superior spectral-efficiency and energy-efficiency, the concept of SCN using NOMA with SIC is proposed in this paper. Due to the difference in small-cell base stations’ locations, each mobile user perceives different channel gains to different small-cell base stations. Therefore, it is important to associate a mobile user with the right base station and control its transmit power for the uplink SCN using NOMA with SIC. However, the already-challenging base station association problem is further complicated by the need of transmit power control, which is an essential component to manage co-channel interference. Despite its importance, the joint base station association and power control optimization problem that maximizes the system-wide utility and at the same time minimizes the total transmit power consumption for the maximum utility has remained largely unsolved for the uplink SCN using NOMA with SIC, mainly due to its non-convex and combinatorial nature. To solve this problem, we first present a formulation transformation that captures two interactive objectives simultaneously. Then, we propose a novel algorithm to solve the equivalently transformed optimization problem based on the coalition formation game theory and the primal decomposition theory in the framework of simulated annealing. Finally, theoretical analysis and simulation results are provided to demonstrate that the proposed algorithm is guaranteed to converge to the global optimal solution in polynomial time.

Optimal Transmission Policies for Relay Communication Networks With Ambient Energy Harvesting Relays

Ambient energy harvesting has emerged as a promising technique to improve the energy efficiency and reduce the total greenhouse gas emissions for green wireless communications. Energy management for throughput maximization under random energy arrivals has been studied extensively in energy harvesting relay communication networks with either finite-size data buffer or finite-size energy storage. However, the problem is still open when the energy harvesting relay node is subject to both finite-size data and energy storage. In this paper, we study the transmission policy of joint time scheduling and power allocation under a transmission deadline, which maximizes the end-to-end system throughput in a two-hop relay communication network where the energy harvesting relay node is equipped with finite-size data buffer and battery. In particular, we first formulate the throughput maximization problem as a convex optimization problem under an offline optimization framework, and obtain the optimal offline time scheduling and power allocation by the Karush-Kuhn-Tucker conditions based on the full knowledge of energy arrivals and channel states. Then, we formulate the throughput maximization problem as a stochastic dynamic programming problem under the online optimization framework, and obtain the optimal online time scheduling and power allocation by solving a series of convex optimizations based on the casual knowledge of energy arrivals and channel states. Finally, to reduce the computation complexity, we further propose two suboptimal online transmission policies. Numerical results show the impacts of battery capacity and buffer size on the maximum throughput of the proposed policies, as well as the balance between the spectrum efficiency and the delay sensitivity.

System Utility Maximization With Interference Processing for Cognitive Radio Networks

In spectrum underlay cognitive radio networks, secondary users (SUs) are allowed to reuse the spectrum allocated to a primary system. The interference between SUs actually carries information and can potentially be exploited to improve the network performance through information-theoretic interference processing. In this paper, we design an optimal joint power and rate control algorithm that maximizes the secondary system utility subject to the interference temperature constraints of primary users based on the capacity-approaching interference processing scheme called as the Han-Kobayashi scheme. The optimal solution is difficult to achieve because the optimization problem is in general non-convex. To make the optimization problem tractable, this paper first transforms the problem into a monotonic optimization problem through exploiting its hidden monotonicity. We then devise an effective algorithm to obtain the global optimal solution to the joint power and rate control problem in the Han-Kobayashi scheme. The key idea behind the proposed algorithm is to construct a sequence of shrinking polyblocks that approximate the upper boundary of the feasible region with increasing precision. Numerical results further show that the achieved utility of our scheme significantly outperforms the utility of conventional schemes which treat the interference between SUs as the noise.