arXiv subjects

Xiaolan Liu

Publications and source records attributed to Xiaolan Liu.

21 records · Page 2Linked to original sources

arXiv2019

Resource Allocation in Wireless Powered IoT Networks

In this paper, efficient resource allocation for the uplink transmission of wireless powered IoT networks is investigated. We adopt LoRa technology as an example in the IoT network, but this work is still suitable for other communication technologies. Allocating limited resources, like spectrum and energy resources, among a massive number of users faces critical challenges. We consider grouping wireless powered IoT users into available channels first and then investigate power allocation for users grouped in the same channel to improve the network throughput. Specifically, the user grouping problem is formulated as a many to one matching game. It is achieved by considering IoT users and channels as selfish players which belong to two disjoint sets. Both selfish players focus on maximizing their own utilities. Then we propose an efficient channel allocation algorithm (ECAA) with low complexity for user grouping. Additionally, a Markov Decision Process (MDP) is used to model unpredictable energy arrival and channel conditions uncertainty at each user, and a power allocation algorithm is proposed to maximize the accumulative network throughput over a finite-horizon of time slots. By doing so, we can distribute the channel access and dynamic power allocation local to IoT users. Numerical results demonstrate that our proposed ECAA algorithm achieves near-optimal performance and is superior to random channel assignment, but has much lower computational complexity. Moreover, simulations show that the distributed power allocation policy for each user is obtained with better performance than a centralized offline scheme.

cs.IT↗

arXiv2018

Optimal Time Scheduling Scheme for Wireless Powered Ambient Backscatter Communication in IoT Network

In this paper, we investigate optimal scheme to manage time scheduling of different modules including spectrum sensing, radio frequency (RF) energy harvesting (RFH) and ambient backscatter communication (ABCom) by maximizing data transmission rate in the internet of things (IoT). We first consider using spectrum sensing with energy detection techniques to detect high power ambient RF signals, and then performing RFH and ABCom with them. Specifically, to improve the spectrum sensing efficiency, compressive sensing is used to detect the wideband RF singals. We propose a joint optimization problem of optimizing time scheduling parameter and power allocation ratio, where power allocation ratio appears because REH and ABCom work at the same time. In addition, a method to find the threshold of spectrum sensing for backscatter communication by analyzing the outage probability of backscatter communication is proposed. Numerical results demonstrate that the optimal schemes with spectrum sensing are achieved with larger transmission rates. Compressive sensing based method is confirmed to be more efficient, and that the superiorities become more obvious with the increasing of the network operation time. Moreover, the optimal scheduling parameters and power allocation ratios are obtained. Also, simulations illustrate that the threshold of spectrum sensing for backscatter communication is obtained by analyzing the outage probability of backscatter communication.

cs.IT↗

arXiv2014

Exact penalty decomposition method for zero-norm minimization based on MPEC formulation

We reformulate the zero-norm minimization problem as an equivalent mathematical program with equilibrium constraints and establish that its penalty problem, induced by adding the complementarity constraint to the objective, is exact. Then, by the special structure of the exact penalty problem, we propose a decomposition method that can seek a global optimal solution of the zero-norm minimization problem under the null space condition in [M. A. Khajehnejad et al. IEEE Trans. Signal. Process., 59(2011), pp. 1985-2001] by solving a finite number of weighted $l_1$-norm minimization problems. To handle the weighted $l_1$-norm subproblems, we develop a partial proximal point algorithm where the subproblems may be solved approximately with the limited memory BFGS (L-BFGS) or the semismooth Newton-CG. Finally, we apply the exact penalty decomposition method with the weighted $l_1$-norm subproblems solved by combining the L-BFGS with the semismooth Newton-CG to several types of sparse optimization problems, and compare its performance with that of the penalty decomposition method [Z. Lu and Y. Zhang, SIAM J. Optim., 23(2013), pp. 2448- 2478], the iterative support detection method [Y. L. Wang and W. T. Yin, SIAM J. Sci. Comput., 3(2010), pp. 462-491] and the state-of-the-art code FPC_AS [Z. W. Wen et al. SIAM J. Sci. Comput., 32(2010), pp. 1832-1857]. Numerical comparisons indicate that the proposed method is very efficient in terms of the recoverability and the required computing time.

math.OC↗