5,746 research outputs found
Quality-of-service provisioning in high speed networks : routing perspectives
The continuous growth in both commercial and public network traffic with various quality-of-service (QoS) requirements is calling for better service than the current Internet\u27s best effort mechanism. One of the challenging issues is to select feasible paths that satisfy the different requirements of various applications. This problem is known as QoS routing. In general, two issues are related to QoS routing: state distribution and routing strategy. Routing strategy is used to find a feasible path that meets the QoS requirements. State distribution addresses the issue of exchanging the state information throughout the network, and can be further divided into two sub-problems: when to update and how to disseminate the state information.
In this dissertation, the issue of when to update link state information from the perspective of information theory is addressed. Based on the rate-distortion analysis, an efficient scheme, which outperforms the state of the art in terms of both protocol overhead and accuracy of link state information, is presented. Second, a reliable scheme is proposed so that, when a link is broken, link state information is still reachable to all network nodes as long as the network is connected. Meanwhile, the protocol overhead is low enough to be implemented in real networks. Third, QoS routing is NP-complete. Hence, tackling this problem requires heuristics. A common approach is to convert this problem into a shortest path or k-shortest path problem and solve it by using existing algorithms such as Bellman-Ford and Dijkstra algorithms. However, this approach suffers from either high computational complexity or low success ratio in finding the feasible paths. Hence, a new problem, All Hops k-shortest Path (AHKP), is introduced and investigated. Based on the solution to AHKP, an efficient self-adaptive routing algorithm is presented, which can guarantee in finding feasible paths with fairly low average computational complexity. One of its most distinguished properties is its progressive property, which is very useful in practice: it can self-adaptively minimize its computational complexity without sacrificing its performance. In addition, routing without considering the staleness of link state information may generate a significant percentage of false routing. Our proposed routing algorithm is capable of minimizing the impact of stale link state information without stochastic link state knowledge. Fourth, the computational complexities of existing s-approximation algorithms are linearly proportional to the adopted linear scaling factors. Therefore, two efficient algorithms are proposed for finding the optimal (the smallest) linear scaling factor such that the computational complexities are reduced. Finally, an efficient algorithm is proposed for finding the least hop(s) multiple additive constrained path for the purpose of saving network resources
QoS routing in ad-hoc networks using GA and multi-objective optimization
Much work has been done on routing in Ad-hoc networks, but the proposed routing solutions only deal with the best effort data traffic. Connections with Quality of Service (QoS) requirements, such as voice channels with delay and bandwidth constraints, are not supported. The QoS routing has been receiving increasingly intensive attention, but searching for the shortest path with many metrics is an NP-complete problem. For this reason, approximated solutions and heuristic algorithms should be developed for multi-path constraints QoS routing. Also, the routing methods should be adaptive, flexible, and intelligent. In this paper, we use Genetic Algorithms (GAs) and multi-objective optimization for QoS routing in Ad-hoc Networks. In order to reduce the search space of GA, we implemented a search space reduction algorithm, which reduces the search space for GAMAN (GA-based routing algorithm for Mobile Ad-hoc Networks) to find a new route. We evaluate the performance of GAMAN by computer simulations and show that GAMAN has better behaviour than GLBR (Genetic Load Balancing Routing).Peer ReviewedPostprint (published version
Jointly Optimal Channel Pairing and Power Allocation for Multichannel Multihop Relaying
We study the problem of channel pairing and power allocation in a
multichannel multihop relay network to enhance the end-to-end data rate. Both
amplify-and-forward (AF) and decode-and-forward (DF) relaying strategies are
considered. Given fixed power allocation to the channels, we show that channel
pairing over multiple hops can be decomposed into independent pairing problems
at each relay, and a sorted-SNR channel pairing strategy is sum-rate optimal,
where each relay pairs its incoming and outgoing channels by their SNR order.
For the joint optimization of channel pairing and power allocation under both
total and individual power constraints, we show that the problem can be
decoupled into two subproblems solved separately. This separation principle is
established by observing the equivalence between sorting SNRs and sorting
channel gains in the jointly optimal solution. It significantly reduces the
computational complexity in finding the jointly optimal solution. It follows
that the channel pairing problem in joint optimization can be again decomposed
into independent pairing problems at each relay based on sorted channel gains.
The solution for optimizing power allocation for DF relaying is also provided,
as well as an asymptotically optimal solution for AF relaying. Numerical
results are provided to demonstrate substantial performance gain of the jointly
optimal solution over some suboptimal alternatives. It is also observed that
more gain is obtained from optimal channel pairing than optimal power
allocation through judiciously exploiting the variation among multiple
channels. Impact of the variation of channel gain, the number of channels, and
the number of hops on the performance gain is also studied through numerical
examples.Comment: 15 pages. IEEE Transactions on Signal Processin
Jointly Optimal Channel Pairing and Power Allocation for Multichannel Multihop Relaying
We study the problem of channel pairing and power allocation in a
multichannel multihop relay network to enhance the end-to-end data rate. Both
amplify-and-forward (AF) and decode-and-forward (DF) relaying strategies are
considered. Given fixed power allocation to the channels, we show that channel
pairing over multiple hops can be decomposed into independent pairing problems
at each relay, and a sorted-SNR channel pairing strategy is sum-rate optimal,
where each relay pairs its incoming and outgoing channels by their SNR order.
For the joint optimization of channel pairing and power allocation under both
total and individual power constraints, we show that the problem can be
decoupled into two subproblems solved separately. This separation principle is
established by observing the equivalence between sorting SNRs and sorting
channel gains in the jointly optimal solution. It significantly reduces the
computational complexity in finding the jointly optimal solution. It follows
that the channel pairing problem in joint optimization can be again decomposed
into independent pairing problems at each relay based on sorted channel gains.
The solution for optimizing power allocation for DF relaying is also provided,
as well as an asymptotically optimal solution for AF relaying. Numerical
results are provided to demonstrate substantial performance gain of the jointly
optimal solution over some suboptimal alternatives. It is also observed that
more gain is obtained from optimal channel pairing than optimal power
allocation through judiciously exploiting the variation among multiple
channels. Impact of the variation of channel gain, the number of channels, and
the number of hops on the performance gain is also studied through numerical
examples.Comment: 15 pages. IEEE Transactions on Signal Processin
Approximation algorithm for QoS routing with multiple additive constraints
In this paper, we study the problem of computing the supported QoS from a source to a destination with multiple additive constraints. The problem has been shown to be NP-complete and many approximation algorithms have been developed. We propose a new approximation algorithm called multi-dimensional relaxation algorithm. We formally prove that our algorithm produces smaller approximation error than the existing algorithms. We further verify the performance by extensive simulations. ©2009 IEEE.published_or_final_versio
On the Power Efficiency of Sensory and Ad Hoc Wireless Networks
We consider the power efficiency of a communications channel, i.e., the maximum bit rate that can be achieved per unit power (energy rate). For additive white Gaussian noise (AWGN) channels, it is well known that power efficiency is attained in the low signal-to-noise ratio (SNR) regime where capacity is proportional to the transmit power. In this paper, we first show that for a random sensory wireless network with n users (nodes) placed in a domain of fixed area, with probability converging to one as n grows, the power efficiency scales at least by a factor of sqrt n. In other words, each user in a wireless channel with n nodes can support the same communication rate as a single-user system, but by expending only 1/(sqrt n) times the energy. Then we look at a random ad hoc network with n relay nodes and r simultaneous transmitter/receiver pairs located in a domain of fixed area. We show that as long as r ≤ sqrt n, we can achieve a power efficiency that scales by a factor of sqrt n. We also give a description of how to achieve these gains
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