Do you want to publish a course? Click here

Leader-Contention-Based User Matching for 802.11 Multiuser MIMO Networks

102   0   0.0 ( 0 )
 Added by Kate Ching-Ju Lin
 Publication date 2014
and research's language is English




Ask ChatGPT about the research

In multiuser MIMO (MU-MIMO) LANs, the achievable throughput of a client depends on who are transmitting concurrently with it. Existing MU-MIMO MAC protocols however enable clients to use the traditional 802.11 contention to contend for concurrent transmission opportunities on the uplink. Such a contention-based protocol not only wastes lots of channel time on multiple rounds of contention, but also fails to maximally deliver the gain of MU-MIMO because users randomly join concurrent transmissions without considering their channel characteristics. To address such inefficiency, this paper introduces MIMOMate, a leader-contention-based MU-MIMO MAC protocol that matches clients as concurrent transmitters according to their channel characteristics to maximally deliver the MU-MIMO gain, while ensuring all users to fairly share concurrent transmission opportunities. Furthermore, MIMOMate elects the leader of the matched users to contend for transmission opportunities using traditional 802.11 CSMA/CA. It hence requires only a single contention overhead for concurrent streams, and can be compatible with legacy 802.11 devices. A prototype implementation in USRP-N200 shows that MIMOMate achieves an average throughput gain of 1.42x and 1.52x over the traditional contention-based protocol for 2-antenna and 3-antenna AP scenarios, respectively, and also provides fairness for clients.



rate research

Read More

Traditional concept of cognitive radio is the coexistence of primary and secondary user in multiplexed manner. we consider the opportunistic channel access scheme in IEEE 802.11 based networks subject to the interference mitigation scenario. According to the protocol rule and due to the constraint of message passing, secondary user is unaware of the exact state of the primary user. In this paper, we have proposed an online algorithm for the secondary which assist determining a backoff counter or the decision of being idle for utilizing the time/frequency slot unoccupied by the primary user. Proposed algorithm is based on conventional reinforcement learning technique namely Q-Learning. Simulation has been conducted in order to prove the strength of this algorithm and also results have been compared with our contemporary solution of this problem where secondary user is aware of some states of primary user.
Interactions among selfish users sharing a common transmission channel can be modeled as a non-cooperative game using the game theory framework. When selfish users choose their transmission probabilities independently without any coordination mechanism, Nash equilibria usually result in a network collapse. We propose a methodology that transforms the non-cooperative game into a Stackelberg game. Stackelberg equilibria of the Stackelberg game can overcome the deficiency of the Nash equilibria of the original game. A particular type of Stackelberg intervention is constructed to show that any positive payoff profile feasible with independent transmission probabilities can be achieved as a Stackelberg equilibrium payoff profile. We discuss criteria to select an operating point of the network and informational requirements for the Stackelberg game. We relax the requirements and examine the effects of relaxation on performance.
In this paper we build upon the recent observation that the 802.11 rate region is log-convex and, for the first time, characterise max-min fair rate allocations for a large class of 802.11 wireless mesh networks. By exploiting features of the 802.11e/n MAC, in particular TXOP packet bursting, we are able to use this characterisation to establish a straightforward, practically implementable approach for achieving max-min throughput fairness. We demonstrate that this approach can be readily extended to encompass time-based fairness in multi-rate 802.11 mesh networks.
In this paper, a novel spectrum association approach for cognitive radio networks (CRNs) is proposed. Based on a measure of both inference and confidence as well as on a measure of quality-of-service, the association between secondary users (SUs) in the network and frequency bands licensed to primary users (PUs) is investigated. The problem is formulated as a matching game between SUs and PUs. In this game, SUs employ a soft-decision Bayesian framework to detect PUs signals and, eventually, rank them based on the logarithm of the a posteriori ratio. A performance measure that captures both the ranking metric and rate is further computed by the SUs. Using this performance measure, a PU evaluates its own utility function that it uses to build its own association preferences. A distributed algorithm that allows both SUs and PUs to interact and self-organize into a stable match is proposed. Simulation results show that the proposed algorithm can improve the sum of SUs rates by up to 20 % and 60 % relative to the deferred acceptance algorithm and random channel allocation approach, respectively. The results also show an improved convergence time.
In this paper we characterise the maximal convex subsets of the (non-convex) rate region in 802.11 WLANs. In addition to being of intrinsic interest as a fundamental property of 802.11 WLANs, this characterisation can be exploited to allow the wealth of convex optimisation approaches to be applied to 802.11 WLANs.
comments
Fetching comments Fetching comments
Sign in to be able to follow your search criteria
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا