21,750 research outputs found
Cognitive Radio Networks: Realistic or Not?
A large volume of research has been conducted in the cognitive radio (CR)
area the last decade. However, the deployment of a commercial CR network is yet
to emerge. A large portion of the existing literature does not build on real
world scenarios, hence, neglecting various important interactions of the
research with commercial telecommunication networks. For instance, a lot of
attention has been paid to spectrum sensing as the front line functionality
that needs to be completed in an efficient and accurate manner to enable an
opportunistic CR network architecture. This is necessary to detect the
existence of spectrum holes without which no other procedure can be fulfilled.
However, simply sensing (cooperatively or not) the energy received from a
primary transmitter cannot enable correct dynamic spectrum access. For example,
the low strength of a primary transmitter's signal does not assure that there
will be no interference to a nearby primary receiver. In addition, the presence
of a primary transmitter's signal does not mean that CR network users cannot
access the spectrum since there might not be any primary receiver in the
vicinity. Despite the existing elegant and clever solutions to the DSA problem
no robust, implementable scheme has emerged. In this paper, we challenge the
basic premises of the proposed schemes. We further argue that addressing the
technical challenges we face in deploying robust CR networks can only be
achieved if we radically change the way we design their basic functionalities.
In support of our argument, we present a set of real-world scenarios, inspired
by realistic settings in commercial telecommunications networks, focusing on
spectrum sensing as a basic and critical functionality in the deployment of
CRs. We use these scenarios to show why existing DSA paradigms are not amenable
to realistic deployment in complex wireless environments.Comment: Work in progres
Decentralized Dynamic Hop Selection and Power Control in Cognitive Multi-hop Relay Systems
In this paper, we consider a cognitive multi-hop relay secondary user (SU)
system sharing the spectrum with some primary users (PU). The transmit power as
well as the hop selection of the cognitive relays can be dynamically adapted
according to the local (and causal) knowledge of the instantaneous channel
state information (CSI) in the multi-hop SU system. We shall determine a low
complexity, decentralized algorithm to maximize the average end-to-end
throughput of the SU system with dynamic spatial reuse. The problem is
challenging due to the decentralized requirement as well as the causality
constraint on the knowledge of CSI. Furthermore, the problem belongs to the
class of stochastic Network Utility Maximization (NUM) problems which is quite
challenging. We exploit the time-scale difference between the PU activity and
the CSI fluctuations and decompose the problem into a master problem and
subproblems. We derive an asymptotically optimal low complexity solution using
divide-and-conquer and illustrate that significant performance gain can be
obtained through dynamic hop selection and power control. The worst case
complexity and memory requirement of the proposed algorithm is O(M^2) and
O(M^3) respectively, where is the number of SUs
Distributed Game Theoretic Optimization and Management of Multichannel ALOHA Networks
The problem of distributed rate maximization in multi-channel ALOHA networks
is considered. First, we study the problem of constrained distributed rate
maximization, where user rates are subject to total transmission probability
constraints. We propose a best-response algorithm, where each user updates its
strategy to increase its rate according to the channel state information and
the current channel utilization. We prove the convergence of the algorithm to a
Nash equilibrium in both homogeneous and heterogeneous networks using the
theory of potential games. The performance of the best-response dynamic is
analyzed and compared to a simple transmission scheme, where users transmit
over the channel with the highest collision-free utility. Then, we consider the
case where users are not restricted by transmission probability constraints.
Distributed rate maximization under uncertainty is considered to achieve both
efficiency and fairness among users. We propose a distributed scheme where
users adjust their transmission probability to maximize their rates according
to the current network state, while maintaining the desired load on the
channels. We show that our approach plays an important role in achieving the
Nash bargaining solution among users. Sequential and parallel algorithms are
proposed to achieve the target solution in a distributed manner. The
efficiencies of the algorithms are demonstrated through both theoretical and
simulation results.Comment: 34 pages, 6 figures, accepted for publication in the IEEE/ACM
Transactions on Networking, part of this work was presented at IEEE CAMSAP
201
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