Showing papers by "Shaoshi Yang published in 2017"

A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems

Abstract: Wireless sensor networks (WSNs) have attracted substantial research interest, especially in the context of performing monitoring and surveillance tasks. However, it is challenging to strike compelling tradeoffs amongst the various conflicting optimization criteria, such as the network’s energy dissipation, packet-loss rate, coverage, and lifetime. This paper provides a tutorial and survey of recent research and development efforts addressing this issue by using the technique of multi-objective optimization (MOO). First, we provide an overview of the main optimization objectives used in WSNs. Then, we elaborate on various prevalent approaches conceived for MOO, such as the family of mathematical programming-based scalarization methods, the family of heuristics/metaheuristics-based optimization algorithms, and a variety of other advanced optimization techniques. Furthermore, we summarize a range of recent studies of MOO in the context of WSNs, which are intended to provide useful guidelines for researchers to understand the referenced literature. Finally, we discuss a range of open problems to be tackled by future research.

Low-Complexity Downlink User Selection for Massive MIMO Systems

Abstract: In this paper, we propose a pair of low-complexity user selection schemes with zero-forcing precoding for multiuser massive multiple-input–multiple-output downlink systems, in which the base station is equipped with a large-scale antenna array. First, we derive approximations of the ergodic sum rates of the systems invoking the conventional random user selection (RUS) and the location-dependent user selection (LUS). Then, the optimal number of simultaneously served user equipments (UEs),i.e., $K^\ast$ , is investigated to maximize the sum rate approximations. Upon exploiting $K^\ast$ , we develop two user selection schemes, namely, $K^\ast$ -RUS and $K^\ast$ -LUS, where $K^\ast$ UEs are selected either randomly or based on their locations. Both the proposed schemes are independent of the instantaneous channel state information of small-scale fading, therefore enjoying the same extremely low computational complexity as that of the conventional RUS scheme. Moreover, both of our proposed schemes achieve significant sum rate improvement over the conventional RUS. In addition, it is worth noting that, like the conventional RUS, the $K^\ast$ -RUS achieves good fairness among UEs.

Power Allocation Optimization for Energy-Efficient Massive MIMO Aided Multi-Pair Decode-and-Forward Relay Systems

Abstract: We investigate power allocation optimization for global energy efficiency (GEE) maximization in the massive multiple-input multiple-output technique aided multi-pair one-way decode-and-forward relay systems. Assuming that the minimum mean-square error channel estimator and zero-forcing transceivers are employed at the relay, we first derive an accurate closed-form expression of the GEE of this complex system. Based on our analytical results, a non-convex power allocation optimization problem with the objective of GEE maximization is formulated under specific quality-of-service (QoS) and transmit power constraints. To solve this challenging problem, the successive convex approximation technique is invoked to transform the original optimization problem into a concave fractional programming problem, which is then efficiently solved by Dinkelbach’s method and by the Charnes–Cooper transformation-based method. In addition, as a special case, the GEE maximization problem under the assumption of using the equal power allocation strategy at both the source users and the relay is also considered. Simulation results demonstrate the accuracy of our analytical results and the effectiveness of the proposed algorithms. Furthermore, the impact of several important system parameters (i.e., the QoS constraint, the transmit power constraints at both the source users and the relay, as well as the quality of channel estimation) on the maximum GEE achieved by the proposed algorithms is also illustrated.

Joint Angle Estimation and Signal Reconstruction for Coherently Distributed Sources in Massive MIMO Systems Based on 2-D Unitary ESPRIT

Abstract: We consider the challenging problem of joint angle estimation and signal reconstruction for coherently distributed (CD) sources in massive multiple-input–multiple-output (MIMO) systems employing uniform rectangular arrays A simplified method inspired by the two-dimensional (2-D) unitary estimating signal parameters via rotational invariance technique (ESPRIT) is proposed to estimate both the central angle and the angular spread without the need for a spectrum peak search and covariance matrix matching process We first approximate the 2-D generalized steering vector expressed as a Schur-Hadamard product by a pair of one-dimensional generalized steering vectors Then, we obtain two approximate rotational invariance relationships with respect to the central angles of the CD sources using a linear approximation of the individual generalized steering vectors of the azimuth and elevation subarrays With the aid of this approximate decomposition, a new unitary ESPRIT-inspired algorithm is conceived to automatically pair the 2-D central angle estimations and a novel method capable of bypassing the high-complexity search process is proposed for angular spread estimation Furthermore, the closed-form approximate Cramer-Rao lower bounds are derived for the estimators of both the central angles and the angular spreads The complexity of the proposed estimator is also analyzed Additionally, the orthogonality of the generalized steering vectors is proved, which enables us to propose a low-complexity method to reconstruct the CD signal matrix by replacing the inversion operator with the conjugate transpose operator The simulation results demonstrate the efficiency of our proposed approach

Energy-efficient joint communication-motion planning for relay-assisted wireless robot surveillance

Abstract: In this paper, we consider a surveillance scenario where a team of sensing robots survey a sensitive area and transmit the monitored data to a remote base station through a mobile relay. In this scenario, it is challenging to autonomously adjust the position of the mobile relay for the sake of minimizing the total communication-motion energy consumption of the system, while maintaining the communication quality of the mobile sensing robots. We first derive the asymptotically optimal transmit powers of the mobile relay and of the sensing robots according to the predefined end-to-end packet error rate (PER) requirement. Then, we propose a joint communication-motion planning (JCMP) method for minimizing the total communication-motion energy consumption in both: single- and multi-sensing-robot scenarios, where the trajectories of the sensing robots are rigorously defined. We further consider the scenario where the sensing robots' trajectories are not fixed but can be optimized in restrained areas. The effectiveness of the proposed JCMP is verified by analysis and numerical results for different system configurations, showing that a substantial energy-efficiency improvement may be achieved in comparison with the benchmark that only optimizes the communication energy consumption.

Achieving Maximum Effective Capacity in OFDMA Networks Operating Under Statistical Delay Guarantee

Abstract: We propose a spectrally efficient design that guarantees the statistical delay quality-of-service (QoS) for delay-sensitive traffic in the downlink of orthogonal frequency-division multiple-access (OFDMA) networks. This design is based on the so-called effective capacity (EC) concept, which describes the maximum throughput, a system can achieve under a specific statistical delay-QoS violation probability constraint. We investigate the EC maximization problem, in which, the statistical delay profile of the traffic is characterized by the QoS-exponent $\theta $ determining the exponential decay rate of the delay-QoS violation probability. By exploiting the properties of concave programming and Slater’s condition, the Lagrangian dual decomposition method is applied and an iterative algorithm that does not depend on the instantaneous channel state information (CSI) is proposed for solving the concave problem formulated. Extensive simulations demonstrate the efficacy and robustness of the proposed iterative algorithm. Furthermore, we show that the system’s achievable EC does not depend on the specific choice of the subcarrier allocation, but rather on the number of subcarriers allocated to each user. This is, because, the EC is calculated using the channel’s statistics, instead of the instantaneous CSI, implying that the EC is more of a long term channel capacity metric.

Vehicle-Assisted Offloading on Metropolitan Streets: Enhancing Geographical Fluidity of Wireless Resources

Abstract: According to field measurement data, we show that the traffic load of cellular networks is highly nonuniform along metropolitan streets. To overcome this predicament, we propose a vehicle-assisted offloading (VAO) scheme, which exploits the vehicle queue stopping for the red light to offload data traffic from busy street intersection cells to their adjacent cells that are largely idle. The performance of VAO is theoretically analyzed by considering both the road traffic conditions and the communications between vehicles and pedestrians. Simulations with practical configurations have verified our analysis and demonstrated that the VAO is capable of achieving substantial performance gain.