Throughput Optimization Problem Research Articles

Throughput optimization in wireless local networks with inter-AP interference via a joint-association control, rate control, and contention resolution

The dense deployment of wireless access points (APs) either in wireless local area networks (WLANs) or in wireless mesh networks facilitates greatly ubiquitous Internet access, however, due to the limited number of orthogonal frequency channels allotted to the IEEE802.11-based networks, this also induces the inevitable problem of inter-AP interference. In this paper, we study the problem of determining the optimal association in multi-cell or extended wireless networks in the presence of hidden terminals and inter-AP collisions. Unlike most previous work in this area, which deals with networks without inter-AP interference, we first reveal that association control alone is not sufficient to achieve fair throughput allocation and load balancing across APs, then advocate a solution based on the joint association control, rate control and fair contention resolution as a means to improving network performance. Based on this, we formulate a cross-layer association control, throughput optimization and contention resolution problem whose objective is to allocate downlink throughput according to the proportional fairness principle. As the problem turns out to be a non-convex mixed integer programming problem, known to be NP-hard, we relax it first into a continuous problem, then transform the resultant into a convex problem and finally propose a distributed algorithm to solve it. We then design a simple yet effective approximation algorithm to recover an optimal solution that fulfills the discrete integral association constraints. The algorithm yields the optimal association, the maximum achievable rate for each downlink flow as well as each AP’s optimal average backoff time. Using these results as settings in a network, we can achieve the optimal operation point without any scheduling. Numerical experiments and simulation results show that our algorithm converges rapidly and works effectively.

Throughput Optimization in Multihop Wireless Networks with Multipacket Reception and Directional Antennas

Recent advances in the physical layer have enabled the simultaneous reception of multiple packets by a node in wireless networks. We address the throughput optimization problem in wireless networks that support multipacket reception (MPR) capability. The problem is modeled as a joint routing and scheduling problem, which is known to be NP-hard. The scheduling subproblem deals with finding the optimal schedulable sets, which are defined as subsets of links that can be scheduled or activated simultaneously. We demonstrate that any solution of the scheduling subproblem can be built with \vert E\vert + 1 or fewer schedulable sets, where \vert E\vert is the number of links of the network. This result is in contrast with previous works that stated that a solution of the scheduling subproblem is composed of an exponential number of schedulable sets. Due to the hardness of the problem, we propose a polynomial time scheme based on a combination of linear programming and approximation algorithm paradigms. We illustrate the use of the scheme to study the impact of design parameters on the performance of MPR-capable networks, including the number of transmit interfaces, the beamwidth, and the receiver range of the antennas.

Load-aware multicast routing metrics in multi-radio multi-channel wireless mesh networks

Efficient multicast routing metric is critical for one-to-many communications in wireless mesh networks. The existing unicast routing metrics do not perform well in multicast due to the distinctive differences between unicast and multicast routing in frame exchange manners in the link layer. It is thus necessary to propose specialized routing metrics for multicast. The focus of this paper is to investigate multicast routing metrics in multi-radio multi-channel wireless mesh networks. We first solve multicast throughput optimization problem in concurrent multicast flows and show that the throughput can be improved by seeking the multicast route with lower channel congestion degree. Then, the multicast routing metrics and protocol are designed by considering this aspect. The main contribution of our work is to propose two load-aware multicast routing metrics named FLMM and FLMM R. Both metrics account for channel diversity, interference and wireless broadcast advantage. FLMM aids in finding multicast route that are better in terms of reduced intra-flow and inter-flow interference and exploits channel diversity to improve bandwidth usage and network throughput. Compared with FLMM, FLMM R further considers the unreliability of MAC multicast. The effectiveness of the two metrics is empirically examined through simulations. Finally, we present the further work from two aspects: the joint multicast routing and channel assignment problem for optimal multicast and the practicality of proposed metrics.

Prediction-Based Throughput Optimization for Dynamic Spectrum Access

Cognitive radio (CR) for dynamic spectrum sensing and access has been a hot research topic in recent years. To avoid collision with the primary users, secondary users need to sense the channels before transmitting on them, which is referred to as sensing time overhead. Our previous work shows that the spectral correlations between the channels within the same service are sufficiently high for accurate prediction, which can further be used to reduce the sensing time. With such motivation, in this paper, we propose a new definition, i.e., channel availability vector (CAV), to characterize the state information of a group of licensed channels by introducing spectrum prediction while focusing on the scenario of a single secondary user with multiple channels and leverage it by formulating the throughput optimization problem as a Markov decision process, which is further solved by our optimal sensing scheme and verified with the real spectrum measurement data. The results show that our prediction-based sensing scheme outperforms one existing work.

Cross-Layer Design for TCP Performance Improvement in Cognitive Radio Networks

In cognitive radio (CR) networks, the end-to-end transmission-control protocol (TCP) performance experienced by secondary users is a very important factor that evaluates the secondary user perceived quality of service (QoS). Most previous works in CR networks ignore the TCP performance. In this paper, we take a cross-layer design approach to jointly consider the spectrum sensing, access decision, physical-layer modulation and coding scheme, and data-link layer frame size in CR networks to maximize the TCP throughput in CR networks. The wireless channel and the primary network usage are modeled as a finite-state Markov process. Due to the miss detection and the estimation error experienced by secondary users, the system state cannot be directly observed. Consequently, we formulate the cross-layer TCP throughput optimization problem as a partially observable Markov decision process (POMDP). Simulation results show that the design parameters in CR networks have a significant impact on the TCP throughput, and the TCP throughput can be substantially improved if the low-layer parameters in CR networks are optimized jointly.

Cross-Layer Throughput Optimization in Cognitive Radio Networks with SINR Constraints

Recently, there have been some research works in the design of cross-layer protocols for cognitive radio (CR) networks, where the Protocol Model is used to model the radio interference. In this paper we consider a multihop multi-channel CR network. We use a more realisticSignal-to-Interference-plus-Noise Ratio (SINR)model for radio interference and study the following cross-layer throughput optimization problem: (1) Given a set of secondary users with random but fixed location, and a set of traffic flows, what is the max-min achievable throughput? (2) To achieve the optimum, how to choose the set of active links, how to assign the channels to each active link, and how to route the flows? To the end, we present a formal mathematical formulation with the objective of maximizing the minimum end-to-end flow throughput. Since the formulation is in the forms ofmixed integer nonlinear programming (MINLP), which is generally a hard problem, we develop a heuristic method by solving a relaxation of the original problem, followed by rounding and simple local optimization. Simulation results show that the heuristic approach performs very well, that is, the solutions obtained by the heuristic are very close to the global optimum obtained via LINGO.

Asymmetric cooperation among wireless relays with linear precoding

Wireless relays extend coverage, improve spectral efficiency, and enhance reliability and rates of wireless cellular communication systems. In this work, we introduce the fundamental notion of asymmetric cooperation among cooperating relays in cellular downlinks - different relays are party to different but overlapping knowledge about the messages transmitted from the base station. We argue that asymmetric cooperation arises naturally in most two-phase protocols in which the base station first transmits information to multiple relays that then cooperatively forward the information to the recipient mobile stations in the cell. For a system in which two relays are of the decode-and-forward type and cooperate using linear precoding to communicate with two mobile stations, we formulate the general, but complicated, throughput optimization problem and derive several results that considerably simplify the optimization. We show that under different channel configurations and fairness criteria, asymmetric cooperation is often the throughput-maximizing option. Under typical configurations, a 20-30% throughput enhancement is achieved compared to conventional full-cooperation systems.

Multi-cell uplink-downlink beamforming throughput duality based on Lagrangian duality with per-base station power constraints

Despite significant research efforts in beamforming, the maximum achievable downlink throughput with beamforming in a multi-cell environment is not available due to difficulty in finding optimal downlink beamforming. Thus, to reformulate the problem into a more solvable form, we derive dual uplink throughput optimization problem to multi-cell downlink beam- forming throughput maximization with per-base station (BS) power constraints based on Lagrangian duality. The optimal downlink beamforming is shown to be a minimum mean squared error (MMSE) beamforming in the dual uplink. It is also shown that the dual uplink problem achieves the same optimal throughput as the primal downlink problem.

Optimal Routing for Wireless Mesh Networks With Dynamic Traffic Demand

Wireless mesh networks have attracted increasing attention and deployment as a high-performance and low-cost solution to last-mile broad-band Internet access. Traffic routing plays a critical role in determining the performance of a wireless mesh network. To investigate the best routing solution, existing work proposes to formulate the mesh network routing problem as an optimization problem. In this problem formulation, traffic demand is usually implicitly assumed as static and known a priori. Contradictorily, recent studies of wireless network traces show that the traffic demand, even being aggregated at access points, is highly dynamic and hard to estimate. Thus, in order to apply the optimization-based routing solution into practice, one must take into account the dynamic and unpredictable nature of wireless traffic demand. This paper presents an integrated framework for wireless mesh network routing under dynamic traffic demand. This framework consists of two important components: traffic estimation and routing optimization. By studying the traces collected at wireless access points, we first present a traffic estimation method which predicts future traffic demand based on its historical data using time-series analysis. This method provides not only the mean value of the future traffic demand estimation but also its statistical distribution. We further investigate the optimal routing strategies for wireless mesh network which take these two forms of traffic demand estimations as inputs. The goal is to balance the traffic load so that minimum congestion will be incurred. This routing objective could be transformed into the throughput optimization problem where the throughput of aggregated flows is maximized subject to fairness constraints that are weighted by the traffic demands. Based on linear programming, we present two routing algorithms which consider the mean value and the statistical distribution of the predicted traffic demands, respectively. The trace-driven simulation study demonstrates that our integrated traffic estimation and routing optimization framework can effectively incorporate the traffic dynamics in mesh network routing.

Opportunistic medium access for wireless networking adapted to decentralized CSI

Relative to a centralized operation, opportunistic medium access capitalizing on decentralized multiuser diversity in a channel-aware homogeneous slotted Aloha system with analog-amplitude channels has been shown to incur only partial loss in throughput due to contention. In this context, we provide sufficient conditions for stability as well as upper bounds on average queue sizes, and address three equally important questions. The first one is whether there exist decentralized scheduling algorithms for homogeneous users with higher throughputs than available ones. We prove that binary scheduling maximizes the sum-throughput. The second issue pertains to heterogeneous systems where users may have different channel statistics. Here we establish that binary scheduling not only maximizes the sum of the logs of the average throughputs, but also asymptotically guarantees fairness among users. The last issue we address is extending the results to finite state Markov chain (FSMC) channels. We provide a convex formulation of the corresponding throughput optimization problem, and derive a simple binary-like access strategy

Joint routing and topology formation in multihop UWB networks

This paper addresses the throughput optimization problem in multihop ultra-wideband (UWB) networks by jointly considering network topology formation and routing. Given a spatial distribution of UWB devices and traffic requirement, we want to form piconets and select paths to maximize the network throughput. Although there have been several works considering the problem of selecting paths to achieve the optimal throughput in multihop wireless networks, to the best of our knowledge, none of them takes the topology formation into the consideration. In this paper, we use Boolean matrices to model role assignment in UWB networks and formulate the throughput optimization problem as a nonlinear programming (NLP) problem. Since the throughput optimization problem is NP-hard, we give an upper bound of the optimal throughput by relaxing some constraints and using pseudo-Boolean optimization to linearize the NLP. We prove that the solution of the upper bound is at most three times of the optimal throughput. Based on the topology formed by solving the upper bound, we formulate a lower bound of the optimal throughput as a linear programming problem and use column generation to solve the lower bound. Numerical results show that the lower bound is very close to the upper bound. Simulation results demonstrate the effectiveness of the scheme.

Optimal ACK mechanisms of the IEEE 802.15.3 MAC for ultra-wideband systems

Ultra-wideband (UWB) transmission is an emerging wireless technology for future short-range indoor and outdoor multimedia applications. To coordinate the access to the wireless medium among the competing devices, the IEEE 802.15.3 medium access control (MAC) is proposed for short-range high-speed wireless personal area networks (WPANs) in the IEEE 802.15.3a task group. In the MAC, three acknowledgment (ACK) mechanisms are adopted during channel time allocation for error control over the error-prone wireless channel: No-ACK, Immediate-ACK (Imm-ACK), and Delayed-ACK (Dly-ACK). Frames received with errors can be retransmitted in the Imm-ACK and Dly-ACK mechanisms. However, how to optimally use these ACK mechanisms during channel time allocation is still an open issue. In this paper, we investigate how to configure the ACK mechanism parameters in order to achieve optimal throughput performance. We first formulate the throughput optimization problem for a contention-free channel time allocation under error channel condition. We then apply the three ACK mechanisms in the contention access period, to optimize the channel throughput. Simulation results demonstrate the effectiveness of our investigation.