1,225 research outputs found
Cooperative Transmitter-Receiver Arrayed Communications
This thesis is concerned with array processing for wireless communications.
In particular, cooperation between the transmitter and receiver or between
systems is exploited to further improve the system performance. Based on
this idea, three technical chapters are presented in this thesis.
Initially in Chapter 1, an introduction including array processing, multiple-input multiple-output (MIMO) communication systems and the background
of cognitive radio is presented. In Chapter 2, a novel approach for estimating
the direction-of-departure (DOD) is proposed using the cooperative beamforming. This proposed approach is featured by its simplicity (beam rotation at
the transmitter) and effectiveness (illustrated in terms of channel capacity).
Chapter 3 is concerned with integration of spatio-temporal (ST) processing
into an antenna array transmitter, given a joint transmitter-receiver system
with ST processing at the receiver but spatial-only processing at the transmitter. The transmit ST processing further improves the system performance in
convergence, mean-square error (MSE) and bit error rate (BER). In Chapter
4, a basic system structure for radio coexistence problem is proposed based on
the concept of MIMO cognitive radio. Cooperation between the licensed radio
and the cognitive radio is exploited. Optimisation of the sum channel capacity
is considered as the criterion and it is solved using a multivariable water-filling
algorithm. Finally, Chapter 5 concludes this thesis and gives suggestions for
future work
Proactive Caching for Energy-Efficiency in Wireless Networks: A Markov Decision Process Approach
Content caching in wireless networks provides a substantial opportunity to
trade off low cost memory storage with energy consumption, yet finding the
optimal causal policy with low computational complexity remains a challenge.
This paper models the Joint Pushing and Caching (JPC) problem as a Markov
Decision Process (MDP) and provides a solution to determine the optimal
randomized policy. A novel approach to decouple the influence from buffer
occupancy and user requests is proposed to turn the high-dimensional
optimization problem into three low-dimensional ones. Furthermore, a
non-iterative algorithm to solve one of the sub-problems is presented,
exploiting a structural property we found as \textit{generalized monotonicity},
and hence significantly reduces the computational complexity. The result
attains close performance in comparison with theoretical bounds from
non-practical policies, while benefiting from higher time efficiency than the
unadapted MDP solution.Comment: 6 pages, 6 figures, submitted to IEEE International Conference on
Communications 201
Copula-based nonlinear quantile autoregression
Parametric copulas are shown to be attractive devices for specifying quantile autoregressive models for nonlinear time-series. Estimation of local, quantile-specific copula-based time series models offers some salient advantages over classical global parametric approaches. Consistency and asymptotic normality of the proposed quantile estimators are established under mild conditions, allowing for global misspecification of parametric copulas and marginals, and without assuming any mixing rate condition. These results lead to a general framework for inference and model specification testing of extreme conditional value-at-risk for financial time series data.
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