Algorithms for Wireless Capacity

Goussevskaia, Olga; Halldórsson, Magnús M.; Wattenhofer, Roger

doi:10.1109/tnet.2013.2258036

Cited by 53 publications

(73 citation statements)

References 52 publications

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“…Coefficients A y B are related to Ornstein-Uhlenbeck diffusion process as we will develop below. As we introduced above, other authors like [7] and [12] In [7] Ornstein-Uhlenbeck process is also used to explain RSSI signal tendency, and they called their method the Modified Ornstein-Uhlenbeck jump diffusion process (MOUjd), and its behaviour is described by (1).…”

Section: The Proposed Prediction Modelmentioning

confidence: 99%

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RSSI Prediction in WiFi Considering Realistic Heterogeneous Restrictions

Suárez

Santana

Macias-López

et al. 2014

NPA

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Received Signal Strength Indicator is an indication of power level being received by a wireless device. This parameter for Wireless Fidelity has never been standardized, so any manufacturer implements it measuring as he knows better doing. This is a problem because different scenarios can be found based in several elements like: the particular Wireless Fidelity Network Interface implementation, the driver, the operating system and software monitoring implementation. This makes the utilization of the Received Signal Strength Indicator to implement higher level functions in wireless networks (like roaming management or localization of terminals) be a headache in realistic scenarios. This heterogeneity of devices, drivers… makes that the application of the Received Signal Strength Indicator be not general. In this paper we present the comparison of our new model of Received Signal Strength Indicator prediction in several realistic and heterogeneous scenarios. The experimental results indicate that we obtain in general, better results than the other models.

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Section: The Proposed Prediction Modelmentioning

confidence: 99%

“…But others factors influence these values. RSS is related to Signal-toInterference-plus-Noise Ratio (SINR) [1] that includes the RSS and the interference caused by other nodes plus noise. That is, RSSI is sensitive to the channel noise, interference, reflections and attenuations, and it suffers from the antenna variability.…”

Section: Introductionmentioning

confidence: 99%

RSSI Prediction in WiFi Considering Realistic Heterogeneous Restrictions

Suárez

Santana

Macias-López

et al. 2014

NPA

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“…Moreover, several problems in the domain, such as finding the maximum set of links that can simultaneously transmit (the LINK CAPACITY problem), have been proved to be computationally hard in an unconstrained SINR model [11]. To facilitate algorithmic analysis under the more realistic MB-SINR model, we introduce a metricity parameter ζ that reflects how well signal decay resembles a metric space.…”

Section: Metricitymentioning

confidence: 99%

“…One reason is that such fading is too unpredictable to expect any algorithm to utilize that to obtain better solutions than otherwise, and thus it is also not fair to compare with such a strong adversary. The other reason is that with arbitrary fading patterns, we are back in the abstract SINR model, for which very strong inapproximability results hold [11].…”

Section: Link Capacity With Multiple Frequenciesmentioning

confidence: 99%

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Extending wireless algorithm design to arbitrary environments via metricity

Gudmundsdottir

Ásgeirsson

Bodlaender

et al. 2014

Proceedings of the 17th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

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Efficient spectrum use in wireless sensor networks through spatial reuse requires effective models of packet reception at the physical layer in the presence of interference. Despite recent progress in analytic and simulations research into worst-case behavior from interference effects, these efforts generally assume geometric path loss and isotropic transmission, assumptions which have not been borne out in experiments.Our paper aims to provide a methodology for grounding theoretical results into wireless interference in experimental reality. We develop a new framework for wireless algorithms in which distancebased path loss is replaced by an arbitrary gain matrix, typically obtained by measurements of received signal strength (RSS). We experimentally evaluate the framework in two indoors testbeds with 20 and 60 motes, and confirm superior predictive performance in packet reception rate for a gain matrix model over a geometric distance-based model.At the heart of our approach is a new parameter ζ called metricity which indicates how close the gain matrix is to a distance metric, effectively measuring the complexity of the environment. A powerful theoretical feature of this parameter is that all known SINR scheduling algorithms that work in general metric spaces carry over to arbitrary gain matrices and achieve equivalent performance guarantees in terms of ζ as previously obtained in terms of the path loss constant. Our experiments confirm the sensitivity of ζ to the nature of the environment. Finally, we show analytically and empirically how multiple channels can be leveraged to improve metricity and thereby performance. We believe our contributions will facilitate experimental validation for recent advances in algorithms for physical wireless interference models.

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Wireless link scheduling via parallel genetic algorithm

Saad

El-Moursy

Alfawaz

et al. 2021

Concurrency and Computation

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With the advent of fifth generation (5G) systems and the Internet‐of‐Things (IoT), the number of interconnected wireless devices is increasing significantly. Protocols that allow these deceives to interconnect peer‐to‐peer through wireless links are becoming of interest. The major challenge is the inevitable interference among the simultaneously activated wireless links. Given a set of wireless links, this article addresses the non‐deterministic polynomial‐time (NP) hard problem of selecting the maximum subset of links that can be simultaneously activated at their respective signal‐to‐interference‐plus‐noise‐ratio (SINR) targets. The contribution of this article is two‐fold. First, we introduce a new genetic algorithm (GA) constraint‐handling mechanism, and prove analytically that finding optimal link schedules is guaranteed. Second, we develop a novel parallelized GA to solve the problem. Through serial algorithm analysis, we utilize data decomposition as well as exploratory decomposition in order to achieve significant running time speedup, which scales well with problem size. Our numerical results for openMP parallelization illustrate 6.5× and 5.4× reduction in computation time as compared to the serial versions of the GA and hybrid genetic algorithm (HGA), respectively. Moreover, the parallelization of the GA and HGA result in a speedup of 10.4× and 5.4×, respectively, using master‐slave multithreading.

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Algorithms for Wireless Capacity

Cited by 53 publications

References 52 publications

RSSI Prediction in WiFi Considering Realistic Heterogeneous Restrictions

RSSI Prediction in WiFi Considering Realistic Heterogeneous Restrictions

Extending wireless algorithm design to arbitrary environments via metricity

Wireless link scheduling via parallel genetic algorithm

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