7,722 research outputs found
Channel Probing in Opportunistic Communication Systems
We consider a multi-channel communication system in which a transmitter has access to M channels, but does not know the state of any of the channels. We model the channel state using an ON/OFF Markov process, and allow the transmitter to probe a single channel at predetermined probing intervals to decide over which channel to transmit. For models in which the transmitter must transmit over the probed channel, it has been shown that a myopic policy probing the channel most likely to be ON is optimal. In this paper, we allow the transmitter to select a channel over which to transmit that is potentially different from the probed channel. For a system of two channels, we show that the choice of which channel to probe does not affect the throughput. For a system with many channels, we show that a probing policy that probes the channel that is the second-most likely to be ON results in higher throughput. We extend the channel probing problem to dynamically choose when to probe based on probing history, and characterize the optimal probing policy for various scenarios
Optimal Relay Selection with Non-negligible Probing Time
In this paper an optimal relay selection algorithm with non-negligible
probing time is proposed and analyzed for cooperative wireless networks. Relay
selection has been introduced to solve the degraded bandwidth efficiency
problem in cooperative communication. Yet complete information of relay
channels often remain unavailable for complex networks which renders the
optimal selection strategies impossible for transmission source without probing
the relay channels. Particularly when the number of relay candidate is large,
even though probing all relay channels guarantees the finding of the best
relays at any time instant, the degradation of bandwidth efficiency due to
non-negligible probing times, which was often neglected in past literature, is
also significant. In this work, a stopping rule based relay selection strategy
is determined for the source node to decide when to stop the probing process
and choose one of the probed relays to cooperate with under wireless channels'
stochastic uncertainties. This relay selection strategy is further shown to
have a simple threshold structure. At the meantime, full diversity order and
high bandwidth efficiency can be achieved simultaneously. Both analytical and
simulation results are provided to verify the claims.Comment: 8 pages. ICC 201
Throughput Optimal Scheduling with Dynamic Channel Feedback
It is well known that opportunistic scheduling algorithms are throughput
optimal under full knowledge of channel and network conditions. However, these
algorithms achieve a hypothetical achievable rate region which does not take
into account the overhead associated with channel probing and feedback required
to obtain the full channel state information at every slot. We adopt a channel
probing model where fraction of time slot is consumed for acquiring the
channel state information (CSI) of a single channel. In this work, we design a
joint scheduling and channel probing algorithm named SDF by considering the
overhead of obtaining the channel state information. We first analytically
prove SDF algorithm can support fraction of of the full rate
region achieved when all users are probed where depends on the
expected number of users which are not probed. Then, for homogenous channel, we
show that when the number of users in the network is greater than 3, , i.e., we guarantee to expand the rate region. In addition, for
heterogenous channels, we prove the conditions under which SDF guarantees to
increase the rate region. We also demonstrate numerically in a realistic
simulation setting that this rate region can be achieved by probing only less
than 50% of all channels in a CDMA based cellular network utilizing high data
rate protocol under normal channel conditions.Comment: submitte
On Myopic Sensing for Multi-Channel Opportunistic Access: Structure, Optimality, and Performance
We consider a multi-channel opportunistic communication system where the
states of these channels evolve as independent and statistically identical
Markov chains (the Gilbert-Elliot channel model). A user chooses one channel to
sense and access in each slot and collects a reward determined by the state of
the chosen channel. The problem is to design a sensing policy for channel
selection to maximize the average reward, which can be formulated as a
multi-arm restless bandit process. In this paper, we study the structure,
optimality, and performance of the myopic sensing policy. We show that the
myopic sensing policy has a simple robust structure that reduces channel
selection to a round-robin procedure and obviates the need for knowing the
channel transition probabilities. The optimality of this simple policy is
established for the two-channel case and conjectured for the general case based
on numerical results. The performance of the myopic sensing policy is analyzed,
which, based on the optimality of myopic sensing, characterizes the maximum
throughput of a multi-channel opportunistic communication system and its
scaling behavior with respect to the number of channels. These results apply to
cognitive radio networks, opportunistic transmission in fading environments,
and resource-constrained jamming and anti-jamming.Comment: To appear in IEEE Transactions on Wireless Communications. This is a
revised versio
Efficient Cooperative Anycasting for AMI Mesh Networks
We have, in recent years, witnessed an increased interest towards enabling a
Smart Grid which will be a corner stone to build sustainable energy efficient
communities. An integral part of the future Smart Grid will be the
communications infrastructure which will make real time control of the grid
components possible. Automated Metering Infrastructure (AMI) is thought to be a
key enabler for monitoring and controlling the customer loads. %RPL is a
connectivity enabling mechanism for low power and lossy networks currently
being standardized by the IETF ROLL working group. RPL is deemed to be a
suitable candidate for AMI networks where the meters are connected to a
concentrator over multi hop low power and lossy links. This paper proposes an
efficient cooperative anycasting approach for wireless mesh networks with the
aim of achieving reduced traffic and increased utilisation of the network
resources. The proposed cooperative anycasting has been realised as an
enhancement on top of the Routing Protocol for Low Power and Lossy Networks
(RPL), a connectivity enabling mechanism in wireless AMI mesh networks. In this
protocol, smart meter nodes utilise an anycasting approach to facilitate
efficient transport of metering data to the concentrator node. Moreover, it
takes advantage of a distributed approach ensuring scalability
Low Power, Low Delay: Opportunistic Routing meets Duty Cycling
Traditionally, routing in wireless sensor networks consists of
two steps: First, the routing protocol selects a next hop,
and, second, the MAC protocol waits for the intended destination
to wake up and receive the data. This design makes
it difficult to adapt to link dynamics and introduces delays
while waiting for the next hop to wake up.
In this paper we introduce ORW, a practical opportunistic
routing scheme for wireless sensor networks. In a dutycycled
setting, packets are addressed to sets of potential receivers
and forwarded by the neighbor that wakes up first
and successfully receives the packet. This reduces delay and
energy consumption by utilizing all neighbors as potential
forwarders. Furthermore, this increases resilience to wireless
link dynamics by exploiting spatial diversity. Our results
show that ORW reduces radio duty-cycles on average
by 50% (up to 90% on individual nodes) and delays by 30%
to 90% when compared to the state of the art
Distributed Opportunistic Scheduling For Ad-Hoc Communications Under Noisy Channel Estimation
Distributed opportunistic scheduling is studied for wireless ad-hoc networks,
where many links contend for one channel using random access. In such networks,
distributed opportunistic scheduling (DOS) involves a process of joint channel
probing and distributed scheduling. It has been shown that under perfect
channel estimation, the optimal DOS for maximizing the network throughput is a
pure threshold policy. In this paper, this formalism is generalized to explore
DOS under noisy channel estimation, where the transmission rate needs to be
backed off from the estimated rate to reduce the outage. It is shown that the
optimal scheduling policy remains to be threshold-based, and that the rate
threshold turns out to be a function of the variance of the estimation error
and be a functional of the backoff rate function. Since the optimal backoff
rate is intractable, a suboptimal linear backoff scheme that backs off the
estimated signal-to-noise ratio (SNR) and hence the rate is proposed. The
corresponding optimal backoff ratio and rate threshold can be obtained via an
iterative algorithm. Finally, simulation results are provided to illustrate the
tradeoff caused by increasing training time to improve channel estimation at
the cost of probing efficiency.Comment: Proceedings of the 2008 IEEE International Conference on
Communications, Beijing, May 19-23, 200
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