159 research outputs found
Analysis of Second-order Statistics Based Semi-blind Channel Estimation in CDMA Channels
The performance of second order statistics (SOS) based semi-blind channel
estimation in long-code DS-CDMA systems is analyzed. The covariance matrix of
SOS estimates is obtained in the large system limit, and is used to analyze the
large-sample performance of two SOS based semi-blind channel estimation
algorithms. A notion of blind estimation efficiency is also defined and is
examined via simulation results.Comment: To be presented at the 2005 Conference on Information Sciences and
System
Impact of Channel Estimation Errors on Multiuser Detection via the Replica Method
For practical wireless DS-CDMA systems, channel estimation is imperfect due
to noise and interference. In this paper, the impact of channel estimation
errors on multiuser detection (MUD) is analyzed under the framework of the
replica method. System performance is obtained in the large system limit for
optimal MUD, linear MUD and turbo MUD, and is validated by numerical results
for finite systems.Comment: To appear in the EURASIP Journal on Wireless Communication and
Networking - Special Issue on Advanced Signal Processing Algorithms for
Wireless Communication
Adaptive Channel Recommendation For Opportunistic Spectrum Access
We propose a dynamic spectrum access scheme where secondary users recommend
"good" channels to each other and access accordingly. We formulate the problem
as an average reward based Markov decision process. We show the existence of
the optimal stationary spectrum access policy, and explore its structure
properties in two asymptotic cases. Since the action space of the Markov
decision process is continuous, it is difficult to find the optimal policy by
simply discretizing the action space and use the policy iteration, value
iteration, or Q-learning methods. Instead, we propose a new algorithm based on
the Model Reference Adaptive Search method, and prove its convergence to the
optimal policy. Numerical results show that the proposed algorithms achieve up
to 18% and 100% performance improvement than the static channel recommendation
scheme in homogeneous and heterogeneous channel environments, respectively, and
is more robust to channel dynamics
Multi-agent Q-Learning of Channel Selection in Multi-user Cognitive Radio Systems: A Two by Two Case
Resource allocation is an important issue in cognitive radio systems. It can
be done by carrying out negotiation among secondary users. However, significant
overhead may be incurred by the negotiation since the negotiation needs to be
done frequently due to the rapid change of primary users' activity. In this
paper, a channel selection scheme without negotiation is considered for
multi-user and multi-channel cognitive radio systems. To avoid collision
incurred by non-coordination, each user secondary learns how to select channels
according to its experience. Multi-agent reinforcement leaning (MARL) is
applied in the framework of Q-learning by considering the opponent secondary
users as a part of the environment. The dynamics of the Q-learning are
illustrated using Metrick-Polak plot. A rigorous proof of the convergence of
Q-learning is provided via the similarity between the Q-learning and
Robinson-Monro algorithm, as well as the analysis of convergence of the
corresponding ordinary differential equation (via Lyapunov function). Examples
are illustrated and the performance of learning is evaluated by numerical
simulations.Comment: submitted to 2009 IEEE International Conference on Systems, Man, and
Cybernetics; the results of general n by m case will be published soo
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