109 research outputs found
On the Stability of Random Multiple Access with Feedback Exploitation and Queue Priority
In this paper, we study the stability of two interacting queues under random
multiple access in which the queues leverage the feedback information. We
derive the stability region under random multiple access where one of the two
queues exploits the feedback information and backs off under negative
acknowledgement (NACK) and the other, higher priority, queue will access the
channel with probability one. We characterize the stability region of this
feedback-based random access protocol and prove that this derived stability
region encloses the stability region of the conventional random access (RA)
scheme that does not exploit the feedback information
Sparse Spectrum Sensing in Infrastructure-less Cognitive Radio Networks via Binary Consensus Algorithms
Compressive Sensing has been utilized in Cognitive Radio Networks (CRNs) to
exploit the sparse nature of the occupation of the primary users. Also,
distributed spectrum sensing has been proposed to tackle the wireless channel
problems, like node or link failures, rather than the common (centralized
approach) for spectrum sensing. In this paper, we propose a distributed
spectrum sensing framework based on consensus algorithms where SU nodes
exchange their binary decisions to take global decisions without a fusion
center to coordinate the sensing process. Each SU will share its decision with
its neighbors, and at every new iteration each SU will take a new decision
based on its current decision and the decisions it receives from its neighbors;
in the next iteration, each SU will share its new decision with its neighbors.
We show via simulations that the detection performance can tend to the
performance of majority rule Fusion Center based CRNs
Generalized Instantly Decodable Network Coding for Relay-Assisted Networks
In this paper, we investigate the problem of minimizing the frame completion
delay for Instantly Decodable Network Coding (IDNC) in relay-assisted wireless
multicast networks. We first propose a packet recovery algorithm in the single
relay topology which employs generalized IDNC instead of strict IDNC previously
proposed in the literature for the same relay-assisted topology. This use of
generalized IDNC is supported by showing that it is a super-set of the strict
IDNC scheme, and thus can generate coding combinations that are at least as
efficient as strict IDNC in reducing the average completion delay. We then
extend our study to the multiple relay topology and propose a joint generalized
IDNC and relay selection algorithm. This proposed algorithm benefits from the
reception diversity of the multiple relays to further reduce the average
completion delay in the network. Simulation results show that our proposed
solutions achieve much better performance compared to previous solutions in the
literature.Comment: 5 pages, IEEE PIMRC 201
Timely Multi-Process Estimation with Erasures
We consider a multi-process remote estimation system observing
independent Ornstein-Uhlenbeck processes. In this system, a shared sensor
samples the processes in such a way that the long-term average sum mean
square error (MSE) is minimized. The sensor operates under a total sampling
frequency constraint and samples the processes according to a
Maximum-Age-First (MAF) schedule. The samples from all processes consume random
processing delays, and then are transmitted over an erasure channel with
probability . Aided by optimal structural results, we show that the
optimal sampling policy, under some conditions, is a \emph{threshold policy}.
We characterize the optimal threshold and the corresponding optimal long-term
average sum MSE as a function of , , , and the
statistical properties of the observed processes.Comment: Accepted for publication in the Asilomar Conference on Signals,
Systems, and Computers, October 202
Sparse Reconstruction-based Detection of Spatial Dimension Holes in Cognitive Radio Networks
In this paper, we investigate a spectrum sensing algorithm for detecting
spatial dimension holes in Multiple Inputs Multiple Outputs (MIMO)
transmissions for OFDM systems using Compressive Sensing (CS) tools. This
extends the energy detector to allow for detecting transmission opportunities
even if the band is already energy filled. We show that the task described
above is not performed efficiently by regular MIMO decoders (such as MMSE
decoder) due to possible sparsity in the transmit signal. Since CS
reconstruction tools take into account the sparsity order of the signal, they
are more efficient in detecting the activity of the users. Building on
successful activity detection by the CS detector, we show that the use of a
CS-aided MMSE decoders yields better performance rather than using either
CS-based or MMSE decoders separately. Simulations are conducted to verify the
gains from using CS detector for Primary user activity detection and the
performance gain in using CS-aided MMSE decoders for decoding the PU
information for future relaying.Comment: accepted for PIMRC 201
Timely Multi-Process Estimation Over Erasure Channels With and Without Feedback: Signal-Independent Policies
We consider a multi-process remote estimation system observing
independent Ornstein-Uhlenbeck processes. In this system, a shared sensor
samples the processes in such a way that the long-term average sum mean
square error (MSE) is minimized using signal-independent sampling policies, in
which sampling instances are chosen independently from the processes' values.
The sensor operates under a total sampling frequency constraint . The
samples from all processes consume random processing delays in a shared queue
and then are transmitted over an erasure channel with probability .
We study two variants of the problem: first, when the samples are scheduled
according to a Maximum-Age-First (MAF) policy, and the receiver provides an
erasure status feedback; and second, when samples are scheduled according to a
Round-Robin (RR) policy, when there is no erasure status feedback from the
receiver. Aided by optimal structural results, we show that the optimal
sampling policy for both settings, under some conditions, is a \emph{threshold
policy}. We characterize the optimal threshold and the corresponding optimal
long-term average sum MSE as a function of , , , and the
statistical properties of the observed processes. Our results show that, with
an exponentially distributed service rate, the optimal threshold
increases as the number of processes increases, for both settings.
Additionally, we show that the optimal threshold is an \emph{increasing}
function of in the case of \emph{available} erasure status feedback,
while it exhibits the \emph{opposite behavior}, i.e., is a
\emph{decreasing} function of , in the case of \emph{absent} erasure
status feedback.Comment: Accepted for publication in the JSAIT Issue on The Role of Freshness
and Semantic Measures in the Transmission of Information for Next Generation
Networks. arXiv admin note: text overlap with arXiv:2209.1121
A Pricing-Based Cooperative Spectrum Sharing Stackelberg Game
We consider the problem of cooperative spectrum sharing among a primary user
(PU) and multiple secondary users (SUs) under quality of service (QoS)
constraints. The SUs network is controlled by the PU through a relay which gets
a revenue for amplifying and forwarding the SUs signals to their respective
destinations. The relay charges each SU a different price depending on its
received signal-to-interference and-noise ratio (SINR). The relay can control
the SUs network and maximize any desired PU utility function. The PU utility
function represents its rate, which is affected by the SUs access, and its
gained revenue to allow the access of the SUs. The SU network can be formulated
as a game in which each SU wants to maximize its utility function; the problem
is formulated as a Stackelberg game. Finally, the problem of maximizing the
primary utility function is solved through three different approaches, namely,
the optimal, the heuristic and the suboptimal algorithms.Comment: 7 pages. IEEE, WiOpt 201
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