Slow Adaptive OFDMA Systems Through Chance Constrained Programming

Adaptive OFDMA has recently been recognized as a promising technique for providing high spectral efficiency in future broadband wireless systems. The research over the last decade on adaptive OFDMA systems has focused on adapting the allocation of radio resources, such as subcarriers and power, to the instantaneous channel conditions of all users. However, such "fast" adaptation requires high computational complexity and excessive signaling overhead. This hinders the deployment of adaptive OFDMA systems worldwide. This paper proposes a slow adaptive OFDMA scheme, in which the subcarrier allocation is updated on a much slower timescale than that of the fluctuation of instantaneous channel conditions. Meanwhile, the data rate requirements of individual users are accommodated on the fast timescale with high probability, thereby meeting the requirements except occasional outage. Such an objective has a natural chance constrained programming formulation, which is known to be intractable. To circumvent this difficulty, we formulate safe tractable constraints for the problem based on recent advances in chance constrained programming. We then develop a polynomial-time algorithm for computing an optimal solution to the reformulated problem. Our results show that the proposed slow adaptation scheme drastically reduces both computational cost and control signaling overhead when compared with the conventional fast adaptive OFDMA. Our work can be viewed as an initial attempt to apply the chance constrained programming methodology to wireless system designs. Given that most wireless systems can tolerate an occasional dip in the quality of service, we hope that the proposed methodology will find further applications in wireless communications

SCOPE: Scalable Composite Optimization for Learning on Spark

Many machine learning models, such as logistic regression~(LR) and support vector machine~(SVM), can be formulated as composite optimization problems. Recently, many distributed stochastic optimization~(DSO) methods have been proposed to solve the large-scale composite optimization problems, which have shown better performance than traditional batch methods. However, most of these DSO methods are not scalable enough. In this paper, we propose a novel DSO method, called \underline{s}calable \underline{c}omposite \underline{op}timization for l\underline{e}arning~({SCOPE}), and implement it on the fault-tolerant distributed platform \mbox{Spark}. SCOPE is both computation-efficient and communication-efficient. Theoretical analysis shows that SCOPE is convergent with linear convergence rate when the objective function is convex. Furthermore, empirical results on real datasets show that SCOPE can outperform other state-of-the-art distributed learning methods on Spark, including both batch learning methods and DSO methods

Enhancing teleportation of quantum Fisher information by partial measurements

The purport of quantum teleportation is to completely transfer information from one party to another distant partner. However, from the perspective of parameter estimation, it is the information carried by a particular parameter, not the information of total quantum state that needs to be teleported. Due to the inevitable noise in environment, we propose two schemes to enhance quantum Fisher information (QFI) teleportation under amplitude damping noise with the technique of partial measurements. We find that post partial measurement can greatly enhance the teleported QFI, while the combination of prior partial measurement and post partial measurement reversal could completely eliminate the effect of decoherence. We show that, somewhat consequentially, enhancing QFI teleportation is more economic than that of improving fidelity teleportation. Our work extends the ability of partial measurements as a quantum technique to battle decoherence in quantum information processing.Comment: Revised version, minor changes, accepted by Phys. Rev.

B\to X_s\gamma, X_s l^+ l^- decays and constraints on the mass insertion parameters in the MSSM

In this paper, we study the upper bounds on the mass insertion parameters $(\delta^{q}_{AB})_{ij}$ in the minimal supersymmetric standard model (MSSM). We found that the information from the measured branching ratio of $B \to X_s l^+ l^-$ decay can help us to improve the upper bounds on the mass insertions parameters \left (\delta^{u,d}_{AB})_{3j,i3}. Some regions allowed by the data of $Br(B \to X_s \gamma)$ are excluded by the requirement of a SM-like $C_{7\gamma}(m_b)$ imposed by the data of $Br(B \to X_s l^+ l^-)$.Comment: 16 pages, 5 eps figure files, typos remove