Cross-layer schemes for performance optimization in wireless networks

Wireless networks are undergoing rapid progress and inspiring numerous applications. As the application of wireless networks becomes broader, they are expected to not only provide ubiquitous connectivity, but also support end users with certain service guarantees. End-to-end delay is an important Quality of Service (QoS) metric in multihop wireless networks. This dissertation addresses how to minimize end-to-end delay through joint optimization of network layer routing and link layer scheduling. Two cross-layer schemes, a loosely coupled cross-layer scheme and a tightly coupled cross-layer scheme, are proposed. The two cross-layer schemes involve interference modeling in multihop wireless networks with omnidirectional antenna. In addition, based on the interference model, multicast schedules are optimized to minimize the total end-to-end delay. Throughput is another important QoS metric in wireless networks. This dissertation addresses how to leverage the spatial multiplexing function of MIMO links to improve wireless network throughput. Wireless interference modeling of a half-duplex MIMO node is presented. Based on the interference model, routing, spatial multiplexing, and scheduling are jointly considered in one optimization model. The throughput optimization problem is first addressed in constant bit rate networks and then in variable bit rate networks. In a variable data rate network, transmitters can use adaptive coding and modulation schemes to change their data rates so that the data rates are supported by the Signal to Noise and Interference Ratio (SINR). The problem of achieving maximum throughput in a millimeter-wave wireless personal area network is studied --Abstract, page iv

Cross-Layer Optimal Rate Allocation for Heterogeneous Wireless Multicast

Heterogeneous multicast is an efficient communication scheme especially for multimedia applications running over multihop networks. The term heterogeneous refers to the phenomenon when multicast receivers in the same session require service at different rates commensurate with their capabilities. In this paper, we address the problem of resource allocation for a set of heterogeneous multicast sessions over multihop wireless networks. We propose an iterative algorithm that achieves the optimal rates for a set of heterogeneous multicast sessions such that the aggregate utility for all sessions is maximized. We present the formulation of the multicast resource allocation problem as a nonlinear optimization model and highlight the cross-layer framework that can solve this problem in a distributed ad hoc network environment with asynchronous computations. Our simulations show that the algorithm achieves optimal resource utilization, guarantees fairness among multicast sessions, provides flexibility in allocating rates over different parts of the multicast sessions, and adapts to changing conditions such as dynamic channel capacity and node mobility. Our results show that the proposed algorithm not only provides flexibility in allocating resources across multicast sessions, but also increases the aggregate system utility and improves the overall system throughput by almost 30% compared to homogeneous multicast

Efficient Resource Management Mechanism for 802.16 Wireless Networks Based on Weighted Fair Queuing

Wireless Networking continues on its path of being one of the most commonly used means of communication. The evolution of this technology has taken place through the design of various protocols. Some common wireless protocols are the WLAN, 802.16 or WiMAX, and the emerging 802.20, which specializes in high speed vehicular networks, taking the concept from 802.16 to higher levels of performance. As with any large network, congestion becomes an important issue. Congestion gains importance as more hosts join a wireless network. In most cases, congestion is caused by the lack of an efficient mechanism to deal with exponential increases in host devices. This can effectively lead to very huge bottlenecks in the network causing slow sluggish performance, which may eventually reduce the speed of the network. With continuous advancement being the trend in this technology, the proposal of an efficient scheme for wireless resource allocation is an important solution to the problem of congestion. The primary area of focus will be the emerging standard for wireless networks, the 802.16 or “WiMAX”. This project, attempts to propose a mechanism for an effective resource management mechanism between subscriber stations and the corresponding base station

An Upper Bound on Multi-hop Transmission Capacity with Dynamic Routing Selection

This paper develops upper bounds on the end-to-end transmission capacity of multi-hop wireless networks. Potential source-destination paths are dynamically selected from a pool of randomly located relays, from which a closed-form lower bound on the outage probability is derived in terms of the expected number of potential paths. This is in turn used to provide an upper bound on the number of successful transmissions that can occur per unit area, which is known as the transmission capacity. The upper bound results from assuming independence among the potential paths, and can be viewed as the maximum diversity case. A useful aspect of the upper bound is its simple form for an arbitrary-sized network, which allows insights into how the number of hops and other network parameters affect spatial throughput in the non-asymptotic regime. The outage probability analysis is then extended to account for retransmissions with a maximum number of allowed attempts. In contrast to prevailing wisdom, we show that predetermined routing (such as nearest-neighbor) is suboptimal, since more hops are not useful once the network is interference-limited. Our results also make clear that randomness in the location of relay sets and dynamically varying channel states is helpful in obtaining higher aggregate throughput, and that dynamic route selection should be used to exploit path diversity.Comment: 14 pages, 5 figures, accepted to IEEE Transactions on Information Theory, 201