Cloud computing has recently emerged as a new paradigm for hosting and delivering services over the Internet. Cloud computing is attractive to business owners as it eliminates the requirement for users to plan ahead for provisioning, and allows enterprises to start from the small and increase resources only when there is a rise in service demand. However, despite the fact that cloud computing offers huge opportunities to the IT industry, the development of cloud computing technology is currently at its infancy, with many issues still to be addressed. In this paper, we present a survey of cloud computing, highlighting its key concepts, architectural principles, state-of-the-art implementation as well as research challenges. The aim of this paper is to provide a better understanding of the design challenges of cloud computing and identify important research directions in this increasingly important area.
Consider the single-group multicast beamforming problem, where multiple users
receive the same data stream simultaneously from a single transmitter. The
problem is NP-hard and all existing algorithms for the problem either find
suboptimal approximate or local stationary solutions. In this paper, we propose
an efficient branch-and-bound algorithm for the problem that is guaranteed to
find its global solution. To the best of our knowledge, our proposed algorithm
is the first tailored global algorithm for the single-group multicast
beamforming problem. Simulation results show that our proposed algorithm is
computationally efficient (albeit its theoretical worst-case iteration
complexity is exponential with respect to the number of receivers) and it
significantly outperforms a state-of-the-art general-purpose global
optimization solver called Baron. Our proposed algorithm provides an important
benchmark for performance evaluation of existing algorithms for the same
problem. By using it as the benchmark, we show that two state-of-the-art
algorithms, semidefinite relaxation algorithm and successive linear
approximation algorithm, work well when the problem dimension (i.e., the number
of antennas at the transmitter and the number of receivers) is small but their
performance deteriorates quickly as the problem dimension increases.Comment: The paper has been accepted for publication in IEEE Transactions on
Signal Processing. The paper has 14 pages in double-column format.The MATLAB
codes of the proposed argument cut relaxation based branch-and-bound (ACR-BB)
algorithm in the paper for solving the single-group multicast beamforming
problem are available at
https://www.dropbox.com/s/safrgm97emdgyl9/ARC-BB.rar?dl=
In this paper, we consider a fundamental problem in modern digital communications known as multiple-input multiple-output (MIMO) detection, which can be formulated as a complex quadratic programming problem subject to unit-modulus and discrete argument constraints. Various semidefinite relaxation (SDR) based algorithms have been proposed to solve the problem in the literature. In this paper, we first show that the conventional SDR is generally not tight for the problem. Then, we propose a new and enhanced SDR and show its tightness under an easily checkable condition, which essentially requires the level of the noise to be below a certain threshold. The above results have answered an open question posed by So in [35]. Numerical simulation results show that our proposed SDR significantly outperforms the conventional SDR in terms of the relaxation gap.
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