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This paper proposes a link adaptation method for IEEE 802.11n, which can foresightedly co-optimize the modulation and coding scheme (MCS) in the PHY layer and the frame size in the MAC layer. The link adaptation method employs Markov decision process (MDP) for modeling this crosslayer design. By solving the MDP model with a reinforcement learning which does not require a prior knowledge about the...
MIMO systems with large number (tens) of antennas are getting increased attention. In particular, low-complexity near-optimal detection in such large-MIMO systems has been an interesting topic of recent research. In this context, our new contributions in this paper are two-fold. First, we propose a variant of Markov chain Monte Carlo (MCMC) algorithm which i) alleviates the stalling problem encountered...
We consider the two-way relay channel with random access for the cases of symmetric and asymmetric channel statistics in the low SNR regime. We propose three different schemes implementing different physical layer techniques for collision recovery and channel adaptation and obtain analytical throughput expressions. We compare the proposed schemes with several benchmarks in order to study their bandwidth...
We derive a Markov chain Monte Carlo algorithm for data detection in multi-antenna systems. The proposed algorithm is based on the Rao-Blackwellization principle, and is parameterized by a value eta that can be interpreted as a system temperature. We demonstrate by simulations that systems with increased temperature (eta>1) significantly outperform the straightforward choice of eta=1. Due to its...
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