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Minghua Chen

Publications and source records attributed to Minghua Chen.

111 records · Page 7Linked to original sources

Analysis of Frequency-Agile CSMA Wireless Networks

This paper proposes and analyzes the performance of a simple frequency-agile CSMA MAC protocol. In this MAC, a node carrier-senses multiple frequency channels simultaneously, and it takes the first opportunity to transmit on any one of the channels when allowed by the CSMA backoff mechanism. We show that the frequency-agile MAC can effectively 1) boost throughput and 2) remove temporal starvation. Furthermore, the MAC can be implemented on the existing multiple-frequency setup in Wi-Fi using multi-radio technology, and it can co-exist with the legacy MAC using single radio. This paper provides exact stationary throughput analysis for regular 1D and thin-strip 2D CSMA networks using a "transfer-matrix" approach. In addition, accurate approximations are given for 2D grid networks. Our closed-form formulas accurately quantify the throughput gain of frequency-agile CSMA. To characterize temporal starvation, we use the metric of "mean residual access time" (MRAT). Our simulations and closed-form approximations indicate that the frequency-agile MAC can totally eliminate temporal starvation in 2D grid networks, reducing its MRAT by orders of magnitude. Finally, this paper presents a "coloring theorem" to justify the use of the frequency-agile MAC in general network topologies. Our analysis and theorem suggest that with enough frequency channels, the frequency-agile MAC can effectively decouple the detrimental interactions between neighboring links responsible for low throughput and starvation.

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Capacity of Large-scale CSMA Wireless Networks

In the literature, asymptotic studies of multi-hop wireless network capacity often consider only centralized and deterministic TDMA (time-division multi-access) coordination schemes. There have been fewer studies of the asymptotic capacity of large-scale wireless networks based on CSMA (carrier-sensing multi-access), which schedules transmissions in a distributed and random manner. With the rapid and widespread adoption of CSMA technology, a critical question is that whether CSMA networks can be as scalable as TDMA networks. To answer this question and explore the capacity of CSMA networks, we first formulate the models of CSMA protocols to take into account the unique CSMA characteristics not captured by existing interference models in the literature. These CSMA models determine the feasible states, and consequently the capacity of CSMA networks. We then study the throughput efficiency of CSMA scheduling as compared to TDMA. Finally, we tune the CSMA parameters so as to maximize the throughput to the optimal order. As a result, we show that CSMA can achieve throughput as $Ω(\frac{1}{\sqrt{n}})$, the same order as optimal centralized TDMA, on uniform random networks. Our CSMA scheme makes use of an efficient backbone-peripheral routing scheme and a careful design of dual carrier-sensing and dual channel scheme. We also address the implementation issues of our CSMA scheme.

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Passive network tomography for erroneous networks: A network coding approach

Passive network tomography uses end-to-end observations of network communication to characterize the network, for instance to estimate the network topology and to localize random or adversarial glitches. Under the setting of linear network coding this work provides a comprehensive study of passive network tomography in the presence of network (random or adversarial) glitches. To be concrete, this work is developed along two directions: 1. Tomographic upper and lower bounds (i.e., the most adverse conditions in each problem setting under which network tomography is possible, and corresponding schemes (computationally efficient, if possible) that achieve this performance) are presented for random linear network coding (RLNC). We consider RLNC designed with common randomness, i.e., the receiver knows the random code-books all nodes. (To justify this, we show an upper bound for the problem of topology estimation in networks using RLNC without common randomness.) In this setting we present the first set of algorithms that characterize the network topology exactly. Our algorithm for topology estimation with random network errors has time complexity that is polynomial in network parameters. For the problem of network error localization given the topology information, we present the first computationally tractable algorithm to localize random errors, and prove it is computationally intractable to localize adversarial errors. 2. New network coding schemes are designed that improve the tomographic performance of RLNC while maintaining the desirable low-complexity, throughput-optimal, distributed linear network coding properties of RLNC. In particular, we design network codes based on Reed-Solomon codes so that a maximal number of adversarial errors can be localized in a computationally efficient manner even without the information of network topology.

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