Device-to-Device (D2D) communication is expected to become a prominent system component in future LTE-Advanced (LTE-A) cellular networks and beyond by unlocking and propelling a plethora of new applications related to...
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ISBN:
(纸本)9781479957255
Device-to-Device (D2D) communication is expected to become a prominent system component in future LTE-Advanced (LTE-A) cellular networks and beyond by unlocking and propelling a plethora of new applications related to content and/or user locality. Towards this vision, resource management techniques for the D2D communication pairs that efficiently reuse resources in the cellular network are required. In this paper, a simple and inherently parallelized randomized D2D radio resource allocation (r-RRA) algorithm is proposed that is based on Fractional Frequency Reuse (FFR) to allocate resources to D2D users. Due to its nature, the proposed randomized scheme is amenable to both distributed and centralized based implementations. Through a wide set of numerical investigations it is shown that the proposed technique is highly competitive as it can provide on average a more than 10% increase in aggregate network throughput compared to existing works.
We present a randomized approach for wait-free locks with strong bounds on time and fairness in a context in which any process can be arbitrarily delayed. Our approach supports a tryLock operation that is given a set ...
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ISBN:
(纸本)9781450392624
We present a randomized approach for wait-free locks with strong bounds on time and fairness in a context in which any process can be arbitrarily delayed. Our approach supports a tryLock operation that is given a set of locks, and code to run when all the locks are acquired. A tryLock operation, or attempt, may fail if there is contention on the locks, in which case the code is not run. Given an upper bound kappa known to the algorithm on the point contention of any lock, and an upper bound L on the number of locks in a try-Lock's set, a tryLock will succeed in acquiring its locks and running the code with probability at least 1/(kappa L). It is thus fair. Furthermore, if the maximum step complexity for the code in any lock is T, the attempt will take O(kappa(LT)-L-2-T-2) steps, regardless of whether it succeeds or fails. The attempts are independent, thus if the tryLock is repeatedly retried on failure, it will succeed in O(kappa(LT)-L-3-T-3) expected steps, and with high probability in not much more.
In this paper, we investigate the integration of the Holder-Nikolskii classes MHpr in the randomized and quantum computation model. We develop randomized and quantum algorithms for integration of functions from this c...
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ISBN:
(纸本)9783037853122
In this paper, we investigate the integration of the Holder-Nikolskii classes MHpr in the randomized and quantum computation model. We develop randomized and quantum algorithms for integration of functions from this class and analyze their convergence rates. Comparing our result with the convergence rates in the deterministic setting, we see that quantum computing can reach an exponential speedup over deterministic classical computation and a quadratic speedup over randomized classical computation..
We consider the problem of boolean compressed sensing, which is also known as group testing. The goal is to recover a small number of defective items in a large set from a few collective binary tests. This problem can...
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ISBN:
(纸本)9781479904464
We consider the problem of boolean compressed sensing, which is also known as group testing. The goal is to recover a small number of defective items in a large set from a few collective binary tests. This problem can be formulated as a binary linear program, which is NP hard in general. To overcome the computational burden, it was recently proposed to relax the binary constraint on the variables, and apply a rounding to the solution of the relaxed linear program. In this paper, we introduce a randomized algorithm to replace the rounding procedure. We show that the proposed algorithm considerably improves the success rate with only a slight increase in computational cost.
In a slowly time-varying fading broadcast channel, a proposed randomized scheduler achieves multi-user diversity gain while reducing the amount of feedback. The scheduler requests feedback of signal-to-noise ratios (S...
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ISBN:
(纸本)1424407281
In a slowly time-varying fading broadcast channel, a proposed randomized scheduler achieves multi-user diversity gain while reducing the amount of feedback. The scheduler requests feedback of signal-to-noise ratios (SNR) from a random subset of users in conjunction with the previously scheduled user, and then selects the user with the largest SNR. With temporal correlation, this scheduler achieves near optimal sum-rate even with feedback from a small subset of users, which considerably reduces the amount of feedback.
Robust Principal Component Analysis (PCA) (or robust subspace recovery) is a particularly important problem in unsupervised learning pertaining to a broad range of applications. In this paper, we analyze a randomized ...
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ISBN:
(纸本)9781467391696
Robust Principal Component Analysis (PCA) (or robust subspace recovery) is a particularly important problem in unsupervised learning pertaining to a broad range of applications. In this paper, we analyze a randomized robust subspace recovery algorithm to show that its complexity is independent of the size of the data matrix. Exploiting the intrinsic low-dimensional geometry of the low rank matrix, the big data matrix is first turned to smaller size compressed data. This is accomplished by selecting a small random subset of the columns of the given data matrix, which is then projected into a random low-dimensional subspace. In the next step, a convex robust PCA algorithm is applied to the compressed data to learn the columns subspace of the low rank matrix. We derive new sufficient conditions, which show that the number of linear observations and the complexity of the randomized algorithm do not depend on the size of the given data.
Simulations of large scale dynamical systems in multi-query or real-time contexts require efficient surrogate modelling techniques, as e.g. achieved via Model Order Reduction (MOR). Recently, symplectic methods like t...
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ISBN:
(纸本)9783031562075;9783031562082
Simulations of large scale dynamical systems in multi-query or real-time contexts require efficient surrogate modelling techniques, as e.g. achieved via Model Order Reduction (MOR). Recently, symplectic methods like the complex singular value decomposition (cSVD) or the SVD-like decomposition have been developed for preserving Hamiltonian structure during MOR. In this contribution, we show how symplectic structure preserving basis generation can be made more efficient with randomized matrix factorizations. We present a randomized complex SVD (rcSVD) algorithm and a randomized SVD-like decomposition (rSVD-like). We demonstrate the efficiency of the approaches with numerical experiments on high dimensional systems.
We provide randomized rendezvous algorithms for two synchronous robots in a bi-directional ring of length n (n is a real number): the robots are equipped with identical chronometers, execute identical algorithms, but ...
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ISBN:
(纸本)9781450329286
We provide randomized rendezvous algorithms for two synchronous robots in a bi-directional ring of length n (n is a real number): the robots are equipped with identical chronometers, execute identical algorithms, but have different speeds u, 1 (where u > 1). In general, neither of the robots are aware of their own speed but in some cases they may be aware either of the magnitude of u or some quantity of time that depends on u, n. The robots start by choosing a direction uniformly and independently at random. Given integer k > 0, we design algorithms that have the two robots alternate for k + 1 rounds between choosing the direction at random followed by walking for a predetermined time. In the last round the robots walk until rendezvous. The first algorithm, RV0, works with one random bit per robot and consists of a single round: after choosing their initial directions the robots never change direction. Rendezvous is established in u.n/2(u(2)-1) expected time and this is shown to be optimal among all randomized algorithms employing a single random bit during their execution. The second algorithm RV1 (k), for k > 1, has the two robots alternate for k + 1 rounds between choosing the direction at random followed by walking for a predetermined time;in the last step the robots walk until rendezvous. Among all algorithms that use k + 1 random bits we establish a sharp threshold;for u <= 2, RV1 (k) is optimal in terms of expected rendezvous time while for u > 2, RV1 is optimal. Further, we provide new randomized rendezvous algorithms employing more random bits and analyze their expected rendezvous time depending on the knowledge of the robots about the length n of the ring and their speeds (u > 1).
Kernel-based K-means clustering has gained popularity due to its simplicity and the power of its implicit non-linear representation of the data. A dominant concern is the memory requirement since memory scales as the ...
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ISBN:
(纸本)9781509045457
Kernel-based K-means clustering has gained popularity due to its simplicity and the power of its implicit non-linear representation of the data. A dominant concern is the memory requirement since memory scales as the square of the number of data points. We provide a new analysis of a class of approximate kernel methods that have more modest memory requirements, and propose a specific one-pass randomized kernel approximation followed by standard K-means on the transformed data. The analysis and experiments suggest the method is accurate, while requiring drastically less memory than standard kernel K-means and significantly less memory than Nystrom based approximations.
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