1,894 research outputs found
Joint Scheduling and ARQ for MU-MIMO Downlink in the Presence of Inter-Cell Interference
User scheduling and multiuser multi-antenna (MU-MIMO) transmission are at the
core of high rate data-oriented downlink schemes of the next-generation of
cellular systems (e.g., LTE-Advanced). Scheduling selects groups of users
according to their channels vector directions and SINR levels. However, when
scheduling is applied independently in each cell, the inter-cell interference
(ICI) power at each user receiver is not known in advance since it changes at
each new scheduling slot depending on the scheduling decisions of all
interfering base stations. In order to cope with this uncertainty, we consider
the joint operation of scheduling, MU-MIMO beamforming and Automatic Repeat
reQuest (ARQ). We develop a game-theoretic framework for this problem and build
on stochastic optimization techniques in order to find optimal scheduling and
ARQ schemes. Particularizing our framework to the case of "outage service
rates", we obtain a scheme based on adaptive variable-rate coding at the
physical layer, combined with ARQ at the Logical Link Control (ARQ-LLC). Then,
we present a novel scheme based on incremental redundancy Hybrid ARQ (HARQ)
that is able to achieve a throughput performance arbitrarily close to the
"genie-aided service rates", with no need for a genie that provides
non-causally the ICI power levels. The novel HARQ scheme is both easier to
implement and superior in performance with respect to the conventional
combination of adaptive variable-rate coding and ARQ-LLC.Comment: Submitted to IEEE Transactions on Communications, v2: small
correction
Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation
New communication standards need to deal with machine-to-machine
communications, in which users may start or stop transmitting at any time in an
asynchronous manner. Thus, the number of users is an unknown and time-varying
parameter that needs to be accurately estimated in order to properly recover
the symbols transmitted by all users in the system. In this paper, we address
the problem of joint channel parameter and data estimation in a multiuser
communication channel in which the number of transmitters is not known. For
that purpose, we develop the infinite factorial finite state machine model, a
Bayesian nonparametric model based on the Markov Indian buffet that allows for
an unbounded number of transmitters with arbitrary channel length. We propose
an inference algorithm that makes use of slice sampling and particle Gibbs with
ancestor sampling. Our approach is fully blind as it does not require a prior
channel estimation step, prior knowledge of the number of transmitters, or any
signaling information. Our experimental results, loosely based on the LTE
random access channel, show that the proposed approach can effectively recover
the data-generating process for a wide range of scenarios, with varying number
of transmitters, number of receivers, constellation order, channel length, and
signal-to-noise ratio.Comment: 15 pages, 15 figure
Active Queue Management for Fair Resource Allocation in Wireless Networks
This paper investigates the interaction between end-to-end flow control and MAC-layer scheduling on wireless links. We consider a wireless network with multiple users receiving information from a common access point; each user suffers fading, and a scheduler allocates the channel based on channel quality,but subject to fairness and latency considerations. We show that the fairness property of the scheduler is compromised by the transport layer flow control of TCP New Reno. We provide a receiver-side control algorithm, CLAMP, that remedies this situation. CLAMP works at a receiver to control a TCP sender by setting the TCP receiver's advertised window limit, and this allows the scheduler to allocate bandwidth fairly between the users
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