12,981 research outputs found
Adaptive Modulation in Multi-user Cognitive Radio Networks over Fading Channels
In this paper, the performance of adaptive modulation in multi-user cognitive
radio networks over fading channels is analyzed. Multi-user diversity is
considered for opportunistic user selection among multiple secondary users. The
analysis is obtained for Nakagami- fading channels. Both adaptive continuous
rate and adaptive discrete rate schemes are analysed in opportunistic spectrum
access and spectrum sharing. Numerical results are obtained and depicted to
quantify the effects of multi-user fading environments on adaptive modulation
operating in cognitive radio networks
Adaptive Modulation and Coding and Cooperative ARQ in a Cognitive Radio System
In this paper, a joint cross-layer design of adaptive modulation and coding
(AMC) and cooperative automatic repeat request (C-ARQ) scheme is proposed for a
secondary user in a shared-spectrum environment. First, based on the
statistical descriptions of the channel, closed-form expressions of the average
spectral efficiency (SE) and the average packet loss rate (PLR) are presented.
Then, the cross-layer scheme is designed, with the aim of maximizing the
average SE while maintaining the average PLR under a prescribed level. An
optimization problem is formed, and a sub-optimal solution is found: the target
packet error rates (PER) for the secondary system channels are obtained and the
corresponding sub-optimal AMC rate adaptation policy is derived based on the
target PERs. Finally, the average SE and the average PLR performance of the
proposed scheme are presented
Spectrum Sharing in Wireless Networks via QoS-Aware Secondary Multicast Beamforming
Secondary spectrum usage has the potential to considerably increase spectrum utilization. In this paper, quality-of-service (QoS)-aware spectrum underlay of a secondary multicast network is considered. A multiantenna secondary access point (AP) is used for multicast (common information) transmission to a number of secondary single-antenna receivers. The idea is that beamforming can be used to steer power towards the secondary receivers while limiting sidelobes that cause interference to primary receivers. Various optimal formulations of beamforming are proposed, motivated by different ldquocohabitationrdquo scenarios, including robust designs that are applicable with inaccurate or limited channel state information at the secondary AP. These formulations are NP-hard computational problems; yet it is shown how convex approximation-based multicast beamforming tools (originally developed without regard to primary interference constraints) can be adapted to work in a spectrum underlay context. Extensive simulation results demonstrate the effectiveness of the proposed approaches and provide insights on the tradeoffs between different design criteria
Spectral Efficiency of Multi-User Adaptive Cognitive Radio Networks
In this correspondence, the comprehensive problem of joint power, rate, and
subcarrier allocation have been investigated for enhancing the spectral
efficiency of multi-user orthogonal frequency-division multiple access (OFDMA)
cognitive radio (CR) networks subject to satisfying total average transmission
power and aggregate interference constraints. We propose novel optimal radio
resource allocation (RRA) algorithms under different scenarios with
deterministic and probabilistic interference violation limits based on a
perfect and imperfect availability of cross-link channel state information
(CSI). In particular, we propose a probabilistic approach to mitigate the total
imposed interference on the primary service under imperfect cross-link CSI. A
closed-form mathematical formulation of the cumulative density function (cdf)
for the received signal-to-interference-plus-noise ratio (SINR) is formulated
to evaluate the resultant average spectral efficiency (ASE). Dual decomposition
is utilized to obtain sub-optimal solutions for the non-convex optimization
problems. Through simulation results, we investigate the achievable performance
and the impact of parameters uncertainty on the overall system performance.
Furthermore, we present that the developed RRA algorithms can considerably
improve the cognitive performance whilst abide the imposed power constraints.
In particular, the performance under imperfect cross-link CSI knowledge for the
proposed `probabilistic case' is compared to the conventional scenarios to show
the potential gain in employing this scheme
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