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Multi-Objective and Financial Portfolio Optimization of p-Persistent Carrier Sense Multiple Access Protocols with Multi-Packet Reception

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Optimization in the Natural Sciences (EmC-ONS 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 499))

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Abstract

This paper revisits the study of wireless carrier-sense multiple access (CSMA) protocols enabled with multi-packet reception (MPR) capabilities. This study employs a new paradigm in the literature of random access based on multi-objective and financial portfolio optimization tools. Under this new optimization framework, each packet transmission is regarded not only as a network resource, but also as a financial asset with different values of return and risk (or variance of the return). The objective of this network-financial optimization is to find the transmission policy that simultaneously optimizes network metrics (such as throughput and efficient power consumption), as well as economic metrics (such as fairness, return and risk). Two transmission models are considered for performance evaluation: a Bernoulli transmission model that facilitates analytic derivations, and a Markov model that considers the backlog states of the network and that facilitates dynamic stability analysis. This work is focused on the characterization of the boundary (envelope) or the Pareto optimal frontier of different types of trade-off performance region. These regions include the conventional throughput and stability regions, as well as new trade-off regions such as sum-throughput vs. fairness, sum-throughput vs. power consumption, and return vs. risk. Fairness is evaluated by means of the Gini-index, which is used in the field of economics to measure population income inequality. Transmit power is directly linked to the global transmission attempt rate. In scenarios with weak MPR capabilities, the system has problems in achieving simultaneously good values of fairness and high values of sum-throughput. This is because of an underlying non-convex throughput region which is typical of protocols dominated by unresolvable collisions. On the contrary, in scenarios with strong MPR capabilities, good fairness, higher energy consumption efficiency, and high sum-throughput performances can be simultaneously achieved. Carrier-sensing is shown to improve the convexity of the throughput region in scenarios with weak MPR, thereby achieving a better trade-off between metrics, including return and risk. However, the effects of carrier-sensing are shown to disappear in scenarios with strong MPR capabilities or with underlying convex throughput regions. The combination of MPR with carrier-sensing tools helps in reducing risk in the network and to fight issues of wireless random access such as the hidden/exposed terminal problems.

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Notes

  1. 1.

    Multi-packet reception is the ability of the PHY-layer to correctly decode concurrent or contending transmissions, mainly by using signal processing tools for multiple-input multiple-output (MIMO) systems.

  2. 2.

    Asymmetrical settings is used to refer to network models where users are explicitly modelled with different channel and queuing statistics.

  3. 3.

    Stability region is loosely defined here as the set of arrival rates for which the queues of all users remain bounded or empty within an finite period of time. Throughput region can also be loosely defined as the set of achievable throughput terminal values.

  4. 4.

    A user is said to be in the backlog state when having previously transmitted a packet, the transmission was lost in a collision and the packet needs to be re-transmitted in subsequent time-slots.

  5. 5.

    Queues in a random access in general are not statistically independent, particularly in the presence of collisions. However, at low and medium traffic loads it is a good approximation commonly used in the literature [9, 16].

  6. 6.

    Strong MPR is defined as the scenario where the throughput region becomes convex. The exact definition can be found in [19].

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Acknowledgments

The research leading to these results has received funding from the ARTEMIS Joint Undertaking under grant agreement no. 621353, the Portuguese National Science Foundation FCT, and by the North Portugal Regional Operational Programme (ON.2 O Novo Norte), under the National Strategic Reference Framework (NSRF), through the European Regional Development Fund (ERDF), and by FCT, within project ref. NORTE-07-0124-FEDER-000063 (BEST-CASE, New Frontiers).

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Authors and Affiliations

  1. CISTER/INESC-TEC, ISEP, Polytechnic Institute of Porto, Porto, Portugal

    Ramiro Sámano-Robles

  2. Instituto de Telecomunicações, Campus Universitário, 3810-193, Aveiro, Portugal

    Atílio Gameiro

Authors
  1. Ramiro Sámano-Robles
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  2. Atílio Gameiro
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Correspondence to Ramiro Sámano-Robles .

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Editors and Affiliations

  1. Dept. of Mathematics, University of Aveiro, Aveiro, Portugal

    Alexander Plakhov

  2. University of Aveiro, Aveiro, Portugal

    Tatiana Tchemisova

  3. University of Aveiro, Aveiro, Portugal

    Adelaide Freitas

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Sámano-Robles, R., Gameiro, A. (2015). Multi-Objective and Financial Portfolio Optimization of p-Persistent Carrier Sense Multiple Access Protocols with Multi-Packet Reception. In: Plakhov, A., Tchemisova, T., Freitas, A. (eds) Optimization in the Natural Sciences. EmC-ONS 2014. Communications in Computer and Information Science, vol 499. Springer, Cham. https://doi.org/10.1007/978-3-319-20352-2_5

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  • DOI: https://doi.org/10.1007/978-3-319-20352-2_5

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