Shiwei Huang

Energy Efficiency and Spectral-Efficiency Tradeoff in Amplify-and-Forward Relay Networks

The tradeoff between energy efficiency (EE) and spectral efficiency (SE) in amplify-and-forward relay networks is studied. Three kinds of transmission strategies are considered: 1) noncooperative transmission; 2) relay transmission without a direct link; and 3) relay transmission with a direct link. The tradeoff between EE and SE is formulated as an optimization problem in which the objective is to maximize EE while satisfying the SE requirement. In the noncooperative transmission strategy, the optimization problem is solved by seeking the optimal source transmission power Ps. It is proven that EE is a strictly quasi-concave function of Ps. In the relay transmission with/without a direct link, the optimization problem is solved by jointly seeking the optimal source transmission power Ps and the optimal relay amplification gain β. It is proven that EE is a strictly quasi-concave function of either Ps or β. An optimal but highly complex 1-D searching (ODS) method and a near-optimal but lower complexity alternate optimization (AOP) method are proposed. Simulation results show that the ODS results are consistent with the 2-D exhaustive searching results. The AOP method can achieve an EE close to that obtained by 2-D exhaustive searching. Moreover, the transmission strategies are compared in terms of EE under the same SE constraint.

Relay selection for average throughput maximization in buffer-aided relay networks

In this paper, the relay selection issue in buffer-aided relay networks is studied. Different from traditional relay networks, buffer occupancy state has great effects on system performance. Keeping buffers always close to full or empty may lead to potential buffer overflow or data unavailability, which will ultimately affect system throughput and transmission delay jitter. In order to avoid this negative situation, we propose a new threshold-based selection strategy which can achieve the transmission capacity balance between input and output links of every relay buffer. Simulation results show that the proposed strategy can effectively prevent buffers from becoming always close to full or empty and, thus, result in great performance improvement compared to the counterpart.

Transmit Power Optimization for Amplify-and-Forward Relay Networks With Reduced Overheads

Transmit power optimization or power control plays an important role in implementing wireless relay networks since it can significantly improve system performance, such as transmission rate, power consumption, etc. However, power optimization introduces considerable control channel overheads due to exchanging channel state information (CSI) and optimization results. In this paper, we aim to design a new power optimization scheme to reduce overheads while avoiding a large performance loss. We consider a typical three-node amplify-and-forward relay network consisting of a source, a relay, and a destination. The power of source and that of relay are optimized to minimize the total power consumption under the constraint of a minimum transmission rate or nonviolation probability. We first define and analyze two schemes based on traditional routines, which are called Strategies I and II. In Strategy I, transmit power of both source and relay is optimized based on instantaneous CSI, whereas in Strategy II, transmit power is based on statistical CSI. We then propose a new partial CSI strategy, which is called Strategy III, where source power is based on statistical CSI, whereas relay power is based on instantaneous CSI. Strategy III is formulated and solved by the two-stage stochastic programming method. With this new strategy, the control channel overhead can be reduced by 50% compared with Strategy I, and at the same time, the potential significant performance degradation as in Strategy II can be avoided. Simulation results show that the proposed strategy results in near-optimal performance when the relay is located close to the source.

An Analysis Framework for Buffer-Aided Relaying Under Time-Correlated Fading Channels

In this paper, we propose a framework to analyze the performance of buffer-aided (BA) relaying under time-correlated fading channels in terms of the queueing behavior of packets at the relays buffer. Unlike independent and identically distributed (i.i.d.) fading, correlated fading brings challenges in performance analysis since the state transition probabilities of buffer occupancy become time variant. To overcome this issue, we first establish an aggregate quasi-birth-death (QBD) Markov chain integrating both the buffer occupancy process and the channel fading process, then analyze the stationary distribution of the aggregate chain, and finally extract the stationary distribution of buffer occupancy from it. Using the stationary distribution of buffer occupancy, system throughput, end-to-end delay, and outage probability are derived. Numerical results verify our analyses and show that the throughput of BA relaying under correlated fading channels can approach the one under i.i.d. fading only for loose delay constraints or high fading margins. For stringent delay constraints and low fading margins, correlated fading causes great degradation of throughput. In particular, a throughput loss of about 16% under an infinite buffer (28% under a finite buffer) is observed for the requirement of an average delay of 20 slots and a fading margin of 5 dB. This means that the designs based on i.i.d. fading are not always feasible for correlated fading. According to these observations, some insights on performance degradation and guidelines on redesigning improved policies under correlated fading are provided.

Dynamic load-balancing spectrum decision for cognitive radio networks with multi-class services

In this paper, we study dynamic load-balancing spectrum decision for cognitive radio networks that dynamically distributes packets from a secondary user (SU) to different available primary channels. Different from the existing works in the literature, we consider two different classes of services at the SU, delay sensitive (DS) and best effort (BE) services, while assigning a higher priority to the DS services. We propose a new queueing analytical model to address this priority issue and analyze the delay performance for the two services separately. Based on the analytical results, two Markov decision processes (MDPs) are formulated with objectives to minimize the average delay of both services, while guaranteeing the priority of the DS services. Simulation results show that the proposed MDP scheme greatly improved the delay performance for both DS and BE services, compared to the shortest queue scheme.

An Incentive Mechanism Integrating Joint Power, Channel and Link Management for Social-Aware D2D Content Sharing and Proactive Caching

In this paper, a downlink cellular traffic offloading framework with social-aware device-to-device (D2D) content sharing and proactive caching is studied. In the considered system model, each user equipment (UE) is intelligent to determine which content(s) to request/cache and to share according to its own preference. As the central controller, the base station (BS) can establish cellular transmissions and/or incentivize D2D communications for content dissemination (including proactive caching). By taking into account wireless features, social characteristics, and device intelligence, we formulate a welfare maximization problem integrating power control, channel allocation, link scheduling, and reward design. To solve this complicated problem, we propose a novel mechanism which consists of a newly developed optimization approach, called basis transformation method, for the joint resource management, and a specially devised pricing scheme for the reward determination. Theoretical and simulation results examine the desired properties of our proposed mechanism, and demonstrate its superiority in improving social welfare, network capacity, and utility of the BS.

A Sequential Posted Price Mechanism for D2D Content Sharing Communications

In this paper, the incentive mechanism design issue for device-to-device (D2D) content-sharing communications is discussed. In literature, most of works are based on auction/game theory, where all content owners first report their ask prices/costs towards the base station (BS) which finally decides only one winner from them to transmit data towards the content requester. One disadvantage of these works is that content owners may be frequently activated to provide auction/game information (such as prices/costs), leading to high energy consumption, but finally may not win to gain benefit. To address this, we propose a sequential posted price mechanism where the BS sends offers with posted prices to content owners in sequence and activates only one owner each time. The BS stops sending new offers as long as there is already an owner accepting an offer or when the BS finds the expected cost of sending a new offer is larger than the cost of direct transmission. The optimal posted prices and offer- stopping rule of the BS are derived by the backward principle of dynamic programming. Simulation results show that the proposed mechanism can effectively limit the proportion of content owners being activated while the BS maintains an acceptable expected cost.

Priority-aware scheduling for coexisting wireless body area networks (invited paper)

In this paper, priority-aware scheduling to the coexistence of multiple wireless body area networks (WBANs) is addressed. The scheduling is formulated as a nonlinear optimization problem where the objective is to maximize system throughput while guaranteeing the priorities among WBANs. To overcome the inherent complexity issue, a new method, called recursive decomposition algorithm (RDA), is proposed to solve the original nonlinear problem by decomposing it into several linear subproblems. Each subproblem is then solved by a column generation method in order to avoid the large number of variables. Since the pricing problem in the column generation method is a 0-1 integer programming which is known to be NP-complete, a heuristic weight-greedy algorithm with a much lower time complexity is proposed. The simulation results show that the proposed RDA can effectively guarantee the priorities among WBANs and can obtain near-optimal throughput performance even in the extremely high user density region.

An Analytical Framework for IEEE 802.15.6-Based Wireless Body Area Networks With Instantaneous Delay Constraints and Shadowing Interruptions

In this paper, a novel queueing analytical framework is proposed to evaluate the performance of IEEE 802.15.6-based carrier-sense multiple access with collision avoidance (CSMA/CA) scheduling in wireless body area networks with joint consideration of instantaneous delay constraints and body shadowing effects. Specifically, we develop an absorbing Markov chain to model the medium access process with error controls that is defined by the IEEE standard and design a random time-limited single vacation to describe the potential body shadowing interruption process. To guarantee the timeliness of received packets and avoid energy waste for transmitting valueless packets, an instantaneous delay constraint is carefully considered, which is then characterized by an overdeadline packet dropping process with a predefined waiting deadline. In our analysis, a Markovian arrival process is also adopted to capture the correlation of arrival traffic, and all other random processes are modeled by phase-type distributions, which make our framework more general and comprehensive. To address the inherent complexity of the original model, based on the transient queueing analysis, we develop a buffer-overflowing queueing principle to approximate the overdeadline packet dropping principle by solving a buffer length optimization problem. After that, we construct a multidimensional discrete-time Markov chain to analyze the stationary distribution through the matrix-geometric method. Performance measures, including the average delay, waiting time distribution, and packet transmission failure probability, are derived. The accuracy of our proposed analytical framework is validated by extensive simulations.

Dynamic Load-Balancing Spectrum Decision for Heterogeneous Services Provisioning in Multi-Channel Cognitive Radio Networks

In this paper, we study dynamic load-balancing spectrum decision for a cognitive radio network (CRN) that dynamically distributes packets from the secondary user (SU) to different available primary channels. We consider two different classes of services at the SU, i.e., delay sensitive (DS) and best effort (BE) services, and assign a higher priority to the DS services. We apply priority queuing model to address this priority issue in the CRN. Based on the queuing model, two Markov decision processes (MDPs) are formulated with objectives to minimize the average delay of both services while guaranteeing the priority of the DS services. Reinforcement learning is applied to find the optimal solutions when the traffic and channel characteristics are unknown. To address the computational complexity issue in the MDP solutions, we propose a myopic method based on the estimated packet sojourn time, which is derived by formulating a phase type distribution. Simulation results demonstrate the effectiveness of all proposed algorithms for load-balancing spectrum decision. It also shows that the proposed myopic scheme can achieve significant reduction on computational complexity with a cost on the delay performance of low priority BE services.