44 research outputs found
On the Benefits of Edge Caching for MIMO Interference Alignment
In this contribution, we jointly investigate the benefits of caching and
interference alignment (IA) in multiple-input multiple-output (MIMO)
interference channel under limited backhaul capacity. In particular, total
average transmission rate is derived as a function of various system parameters
such as backhaul link capacity, cache size, number of active
transmitter-receiver pairs as well as the quantization bits for channel state
information (CSI). Given the fact that base stations are equipped both with
caching and IA capabilities and have knowledge of content popularity profile,
we then characterize an operational regime where the caching is beneficial.
Subsequently, we find the optimal number of transmitter-receiver pairs that
maximizes the total average transmission rate. When the popularity profile of
requested contents falls into the operational regime, it turns out that caching
substantially improves the throughput as it mitigates the backhaul usage and
allows IA methods to take benefit of such limited backhaul.Comment: 20 pages, 5 figures. A shorter version is to be presented at 16th
IEEE International Workshop on Signal Processing Advances in Wireless
Communications (SPAWC'2015), Stockholm, Swede
Joint Channel Probing and Proportional Fair Scheduling in Wireless Networks
The design of a scheduling scheme is crucial for the efficiency and
user-fairness of wireless networks. Assuming that the quality of all user
channels is available to a central controller, a simple scheme which maximizes
the utility function defined as the sum logarithm throughput of all users has
been shown to guarantee proportional fairness. However, to acquire the channel
quality information may consume substantial amount of resources. In this work,
it is assumed that probing the quality of each user's channel takes a fraction
of the coherence time, so that the amount of time for data transmission is
reduced. The multiuser diversity gain does not always increase as the number of
users increases. In case the statistics of the channel quality is available to
the controller, the problem of sequential channel probing for user scheduling
is formulated as an optimal stopping time problem. A joint channel probing and
proportional fair scheduling scheme is developed. This scheme is extended to
the case where the channel statistics are not available to the controller, in
which case a joint learning, probing and scheduling scheme is designed by
studying a generalized bandit problem. Numerical results demonstrate that the
proposed scheduling schemes can provide significant gain over existing schemes.Comment: 26 pages, 8 figure
Channel probing in communication systems: Myopic policies are not always optimal
We consider a multi-channel communication system in which a transmitter has access to a large number of channels, but does not know the state of these channels. We model channel state using an ON/OFF Markovian model, and allow the transmitter to probe one of the channels at predetermined probing intervals to decide over which channel to transmit. For models in which the transmitter must send over the probed channel, it has been shown that a myopic policy that probes the channel most likely to be ON is optimal. In this work, we allow the transmitter to select a channel over which to transmit that is not necessarily the one it probed. We show that the myopic policy is not optimal, and propose a simple alternative probing policy, which achieves a higher per-slot expected throughput. Finally, we consider the case where there is a fixed cost associated with probing and derive optimal probing intervals.National Science Foundation (U.S.) (Grant CNS1217048)National Science Foundation (U.S.) (CNS-0915988)United States. Army Research Office. Multidisciplinary University Research Initiative (Grant W911NF-08-1-0238
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
Optimal channel probing in communication systems: The two-channel case
We consider a multi-channel communication system in which a transmitter has access to two channels, but does not know the state of either channel. We model the channel state using an ON/OFF Markovian model, and allow the transmitter to probe one of the channels 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 that probes the channel most likely to be ON is optimal. In this work, we allow the transmitter to select a channel over which to transmit that is not necessarily the one it probed. We show that in the case where the two channels are i.i.d, all probing policies yield equal reward. We extend this problem to dynamically choose when to probe based on the results of previous probes, and characterize the optimal policy, as well as provide a LP in terms of state action frequencies to find the optimal policy.National Science Foundation (U.S.) (Grant CNS-0915988)National Science Foundation (U.S.) (Grant CNS-1217048)United States. Army Research Office. Multidisciplinary University Research Initiative (Grant W911NF-08-1-0238
Optimal Bandwidth and Power Allocation for Sum Ergodic Capacity under Fading Channels in Cognitive Radio Networks
This paper studies optimal bandwidth and power allocation in a cognitive
radio network where multiple secondary users (SUs) share the licensed spectrum
of a primary user (PU) under fading channels using the frequency division
multiple access scheme. The sum ergodic capacity of all the SUs is taken as the
performance metric of the network. Besides all combinations of the peak/average
transmit power constraints at the SUs and the peak/average interference power
constraint imposed by the PU, total bandwidth constraint of the licensed
spectrum is also taken into account. Optimal bandwidth allocation is derived in
closed-form for any given power allocation. The structures of optimal power
allocations are also derived under all possible combinations of the
aforementioned power constraints. These structures indicate the possible
numbers of users that transmit at nonzero power but below their corresponding
peak powers, and show that other users do not transmit or transmit at their
corresponding peak power. Based on these structures, efficient algorithms are
developed for finding the optimal power allocations.Comment: 28 pages, 6 figures, submitted to the IEEE Trans. Signal Processing
in June 201
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
To Stay Or To Switch: Multiuser Dynamic Channel Access
In this paper we study opportunistic spectrum access (OSA) policies in a
multiuser multichannel random access cognitive radio network, where users
perform channel probing and switching in order to obtain better channel
condition or higher instantaneous transmission quality. However, unlikely many
prior works in this area, including those on channel probing and switching
policies for a single user to exploit spectral diversity, and on probing and
access policies for multiple users over a single channel to exploit temporal
and multiuser diversity, in this study we consider the collective switching of
multiple users over multiple channels. In addition, we consider finite
arrivals, i.e., users are not assumed to always have data to send and demand
for channel follow a certain arrival process. Under such a scenario, the users'
ability to opportunistically exploit temporal diversity (the temporal variation
in channel quality over a single channel) and spectral diversity (quality
variation across multiple channels at a given time) is greatly affected by the
level of congestion in the system. We investigate the optimal decision process
in this case, and evaluate the extent to which congestion affects potential
gains from opportunistic dynamic channel switching