This paper focuses on multirate IEEE 802.11 Wireless LAN employing the mandatory Distributed Coordination Function (DCF) option. Its aim is threefold. Upon starting from the multi-dimensional Markovian state transition model proposed by Malone textit
{et.al.} for characterizing the behavior of the IEEE 802.11 protocol at the Medium Access Control layer, it presents an extension accounting for packet transmission failures due to channel errors. Second, it establishes the conditions under which a network constituted by $N$ stations, each station transmitting with its own bit rate, $R^{(s)}_d$, and packet rate, $lambda_s$, can be assumed loaded. Finally, it proposes a modified Proportional Fairness (PF) criterion, suitable for mitigating the textit{rate anomaly} problem of multirate loaded IEEE 802.11 Wireless LANs, employing the mandatory DCF option. Compared to the widely adopted assumption of saturated network, the proposed fairness criterion can be applied to general loaded networks. The throughput allocation resulting from the proposed algorithm is able to greatly increase the aggregate throughput of the DCF, while ensuring fairness levels among the stations of the same order as the ones guaranteed by the classical PF criterion. Simulation results are presented for some sample scenarios, confirming the effectiveness of the proposed criterion for optimized throughput allocation.
In this paper, the authors compare the security bounds for different quantum communication protocols with the numerically evaluated losses in the transmission channel, due to the interaction between the atmosphere and the photon, which is the informa
tion carrier. The analysis is carried out using a free-source library, which can solve the radiative transfer equation for a parallel-plane atmosphere.
This article proposes a novel iterative algorithm based on Low Density Parity Check (LDPC) codes for compression of correlated sources at rates approaching the Slepian-Wolf bound. The setup considered in the article looks at the problem of compressin
g one source at a rate determined based on the knowledge of the mean source correlation at the encoder, and employing the other correlated source as side information at the decoder which decompresses the first source based on the estimates of the actual correlation. We demonstrate that depending on the extent of the actual source correlation estimated through an iterative paradigm, significant compression can be obtained relative to the case the decoder does not use the implicit knowledge of the existence of correlation.
This article deals with localization probability in a network of randomly distributed communication nodes contained in a bounded domain. A fraction of the nodes denoted as L-nodes are assumed to have localization information while the rest of the nod
es denoted as NL nodes do not. The basic model assumes each node has a certain radio coverage within which it can make relative distance measurements. We model both the case radio coverage is fixed and the case radio coverage is determined by signal strength measurements in a Log-Normal Shadowing environment. We apply the probabilistic method to determine the probability of NL-node localization as a function of the coverage area to domain area ratio and the density of L-nodes. We establish analytical expressions for this probability and the transition thresholds with respect to key parameters whereby marked change in the probability behavior is observed. The theoretical results presented in the article are supported by simulations.
