2,100 research outputs found
General analytical framework for cooperative sensing and access trade-off optimization
In this paper, we investigate the joint cooperative spectrum sensing and
access design problem for multi-channel cognitive radio networks. A general
heterogeneous setting is considered where the probabilities that different
channels are available, SNRs of the signals received at secondary users (SUs)
due to transmissions from primary users (PUs) for different users and channels
can be different. We assume a cooperative sensing strategy with a general
a-out-of-b aggregation rule and design a synchronized MAC protocol so that SUs
can exploit available channels. We analyze the sensing performance and the
throughput achieved by the joint sensing and access design. Based on this
analysis, we develop algorithms to find optimal parameters for the sensing and
access protocols and to determine channel assignment for SUs to maximize the
system throughput. Finally, numerical results are presented to verify the
effectiveness of our design and demonstrate the relative performance of our
proposed algorithms and the optimal ones.Comment: arXiv admin note: text overlap with arXiv:1404.167
Joint Cooperative Spectrum Sensing and MAC Protocol Design for Multi-channel Cognitive Radio Networks
In this paper, we propose a semi-distributed cooperative spectrum sen sing
(SDCSS) and channel access framework for multi-channel cognitive radio networks
(CRNs). In particular, we c onsider a SDCSS scheme where secondary users (SUs)
perform sensing and exchange sensing outcomes with ea ch other to locate
spectrum holes. In addition, we devise the p -persistent CSMA-based cognitive
MAC protocol integrating the SDCSS to enable efficient spectrum sharing among
SUs. We then perform throughput analysis and develop an algorithm to determine
the spectrum sensing and access parameters to maximize the throughput for a
given allocation of channel sensing sets. Moreover, we consider the spectrum
sensing set optimization problem for SUs to maxim ize the overall system
throughput. We present both exhaustive search and low-complexity greedy
algorithms to determine the sensing sets for SUs and analyze their complexity.
We also show how our design and analysis can be extended to consider reporting
errors. Finally, extensive numerical results are presented to demonstrate the
sig nificant performance gain of our optimized design framework with respect to
non-optimized designs as well as the imp acts of different protocol parameters
on the throughput performance.Comment: accepted for publication EURASIP Journal on Wireless Communications
and Networking, 201
Sensing Throughput Optimization in Fading Cognitive Multiple Access Channels With Energy Harvesting Secondary Transmitters
The paper investigates the problem of maximizing expected sum throughput in a
fading multiple access cognitive radio network when secondary user (SU)
transmitters have energy harvesting capability, and perform cooperative
spectrum sensing. We formulate the problem as maximization of sum-capacity of
the cognitive multiple access network over a finite time horizon subject to a
time averaged interference constraint at the primary user (PU) and almost sure
energy causality constraints at the SUs. The problem is a mixed integer
non-linear program with respect to two decision variables namely spectrum
access decision and spectrum sensing decision, and the continuous variables
sensing time and transmission power. In general, this problem is known to be NP
hard. For optimization over these two decision variables, we use an exhaustive
search policy when the length of the time horizon is small, and a heuristic
policy for longer horizons. For given values of the decision variables, the
problem simplifies into a joint optimization on SU \textit{transmission power}
and \textit{sensing time}, which is non-convex in nature. We solve the
resulting optimization problem as an alternating convex optimization problem
for both non-causal and causal channel state information and harvested energy
information patterns at the SU base station (SBS) or fusion center (FC). We
present an analytic solution for the non-causal scenario with infinite battery
capacity for a general finite horizon problem.We formulate the problem with
causal information and finite battery capacity as a stochastic control problem
and solve it using the technique of dynamic programming. Numerical results are
presented to illustrate the performance of the various algorithms
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