Efficient simulation of Ricean fading within a packet simulator

Punnoose, Ratish J.; Никитин, П.В.; Stancil, Daniel D.

doi:10.1109/vetecf.2000.887108

Cited by 227 publications

(108 citation statements)

References 8 publications

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“…Link errors are caused by channel fading. A practical ns-2 extension for the well-known Ricean fading model is used [16]. The capacity of the wireless channel used in the simulation is 2 Mbps or 256 KBps.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Distributed Opportunistic Scheduling in Multihop Wireless Ad Hoc Networks

Sun¹,

Li²,

Leung³

2008

2008 IEEE International Conference on Communications

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Abstract-In this paper, we introduce a framework for distributed opportunistic scheduling in multihop wireless ad hoc networks. With the proposed framework, one can take a scheduling algorithm originally designed for infrastructure-based wireless networks and adapt it to multihop ad hoc networks. The framework includes a wireless link state estimation mechanism, a medium access control (MAC) protocols and a MAC load control mechanism. The proposed link state estimation mechanism accounts for the latest results of packet transmissions on each wireless link. To improve robustness and provide service isolation during channel errors, the MAC protocol should not make any packet retransmissions but only report the transmission result to the scheduler. We modify IEEE 802.11 to fulfill these requirements. The MAC load control mechanism improves the system robustness. With link state information and the modified IEEE 802.11 MAC, we use BGFS-EBA, an opportunistic scheduling algorithm for infrastructured wireless networks, as an example to demonstrate how such an algorithm is converted into its distributed version within the proposed framework. The simulation results show that our proposed method can provide robust outcome fairness in the presence of channel errors.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Distributed Opportunistic Scheduling in Multihop Wireless Ad Hoc Networks

Sun¹,

Li²,

Leung³

2008

2008 IEEE International Conference on Communications

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“…In the next subsection, we evaluate AdHoc Probe in Auto Rate wireless networks using NS-2 simulator with the auto rate extension described in [2]. The extension uses the Ricean fading model [20], and it supports both RBAR and OAR. In the simulation, the transmission data rate of 802.11b MAC is adapted among 2, 5.5 and 11Mbps.…”

Section: A Overview Of Auto Rate Techniquesmentioning

confidence: 99%

AdHoc Probe: end-to-end capacity probing in wireless ad hoc networks

et al. 2007

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Knowledge of end-to-end path capacity is useful for video/audio stream adaptation, network management and overlay design. Capacity estimation in wired and last-hop wireless networks has been extensively investigated, but a thorough and systematic study in ad hoc, multihop wireless networks is still lacking. Yet the rate of a wireless link can change dynamically (and rapidly) due to changes in interference, distance or energy optimization policy.Timely knowledge of path capacity is key to efficient routing, traffic management and application deployment. In this paper, we present AdHoc Probe, a packet-pair based technique, to estimate end-to-end path capacity in ad hoc wireless networks. We apply AdHoc Probe to path capacity estimation in auto rate wireless networks with variable displacement and interference; and, in remote wireless networks across the Internet. Using analysis, simulation and testbed experiments, we show AdHoc Probe can withstand mobility and is able to trace the rate adaptation of wireless networks timely and correctly. AdHoc Probe is simpler, faster and much less intrusive than current schemes.

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“…First, analytical channel models specify physical wireless signal propagation phenomena known as large-scale fading (LSF) and small-scale fading (SSF) effects. ns-2 [11], for example, includes three LSF models (free space model, two-ray ground reflection model, and shadowing model) and one SSF model (Ricean distribution) [12]. These physical models allow ns-2 to compute the average signal power of every packet arrived at a receiver and based on the comparison of the perceived signal power to the receiving threshold, determine packet drops.…”

Section: Theoretical Models For Wireless Channelmentioning

confidence: 99%

An adaptive FEC code control algorithm for mobile wireless sensor networks

Ahn

Hong

Heidemann³

2005

J. Commun. Netw.

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For better performance over a noisy channel, mobile wireless networks transmit packets with forward error correction (FEC) code to recover corrupt bits without retransmission. The static determination of the FEC code size, however, degrades their performance since the evaluation of the underlying channel state is hardly accurate and even widely varied. Our measurements over a wireless sensor network, for example, show that the average bit error rate (BER) per second or per minute continuously changes from 0 up to 10 −3 . Under this environment, wireless networks waste their bandwidth since they can't deterministically select the appropriate size of FEC code matching to the fluctuating channel BER.This paper proposes an adaptive FEC technique called adaptive FEC code control (AFECCC), which dynamically tunes the amount of FEC code per packet based on the arrival of acknowledgement packets without any specific information such as signal to noise ratio (SNR) or BER from receivers. Our simulation experiments indicate that AFECCC performs better than any static FEC algorithm and some conventional dynamic hybrid FEC/ARQ algorithms when wireless channels are modeled with two-state Markov chain, chaotic map, and traces collected from real sensor networks. Finally, AFECCC implemented in sensor motes achieves better performance than any static FEC algorithm.Index Terms: Adaptive forward error correction (FEC) algorithm, wireless mobile sensor networks.

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Efficient simulation of Ricean fading within a packet simulator

Abstract: Abstract

Cited by 227 publications

References 8 publications

Distributed Opportunistic Scheduling in Multihop Wireless Ad Hoc Networks

Distributed Opportunistic Scheduling in Multihop Wireless Ad Hoc Networks

AdHoc Probe: end-to-end capacity probing in wireless ad hoc networks

An adaptive FEC code control algorithm for mobile wireless sensor networks

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