Adaptive partial update channel shortening in impulsive noise environments

Partial updating is an effective method for reducing computational complexity in adaptive filter implementations. In this paper adaptive partial update channel shortening algorithms in impulsive noise environments are proposed. These algorithms are based on updating a portion of the coefficients at each time sample instead of the entire set of coefficients. These algorithms have low computational complexity whilst retaining essentially identical performance to the sum-absolute autocorrelation minimization (SAAM) algorithm due to Nawaz and chambers. Simulation studies show the ability of the deterministic partial update SAAM (DPUSAAM) algorithm and the Random Partial Update SAAM (RPUSAAM)algorithm to achieve channel shortening and hence an acceptable level of bitrate within a multicarrier system

Optimal Lower Bounds for Projective List Update Algorithms

The list update problem is a classical online problem, with an optimal competitive ratio that is still open, known to be somewhere between 1.5 and 1.6. An algorithm with competitive ratio 1.6, the smallest known to date, is COMB, a randomized combination of BIT and the TIMESTAMP algorithm TS. This and almost all other list update algorithms, like MTF, are projective in the sense that they can be defined by looking only at any pair of list items at a time. Projectivity (also known as "list factoring") simplifies both the description of the algorithm and its analysis, and so far seems to be the only way to define a good online algorithm for lists of arbitrary length. In this paper we characterize all projective list update algorithms and show that their competitive ratio is never smaller than 1.6 in the partial cost model. Therefore, COMB is a best possible projective algorithm in this model.Comment: Version 3 same as version 2, but date in LaTeX \today macro replaced by March 8, 201

Beamforming Array Antenna Technique Based on Partial Update Adaptive Algorithms

The most important issues for improving the performance of modern wireless communication systems are interference cancellation, efficient use of energy, improved spectral efficiency and increased system security. Beamforming Array Antenna (BAA) is one of the efficient methods used for this purpose. Full band BAA, on the other hand, will suffer from a large number of controllable elements, a long convergence time and the complexity of the beamforming network. Since no attempt had previously been made to use Partial Update (PU) for BAA, the main novelty and contribution of this paper was to use PU instead of full band adaptive algorithms. PU algorithms will connect to a subset of the array elements rather than all of them. As a result, a common number of working antennas for the system\u27s entire cells can be reduced to achieve overall energy efficiency and high cost-effectiveness. In this paper, we propose a new architectural model that employs PU adaptive algorithms to control and minimize the number of phase shifters, thereby reducing the number of base station antennas. We will concentrate on PU LMS (Least Mean Square) algorithms such as sequential-LMS, M-max LMS, periodic-LMS, and stochastic-LMS. According to simulation results using a Uniform Linear Array (ULA) and three communications channels, the M-max-LMS, periodic LMS, and stochastic LMS algorithms perform similarly to the full band LMS algorithm in terms of square error, tracking weight coefficients, and estimation input signal, with a quick convergence time, low level of error signal at steady state and keeping null steering\u27s interference-suppression capability intact

Succinct Partial Sums and Fenwick Trees

We consider the well-studied partial sums problem in succint space where one is to maintain an array of n k-bit integers subject to updates such that partial sums queries can be efficiently answered. We present two succint versions of the Fenwick Tree - which is known for its simplicity and practicality. Our results hold in the encoding model where one is allowed to reuse the space from the input data. Our main result is the first that only requires nk + o(n) bits of space while still supporting sum/update in O(log_b n) / O(b log_b n) time where 2 <= b <= log^O(1) n. The second result shows how optimal time for sum/update can be achieved while only slightly increasing the space usage to nk + o(nk) bits. Beyond Fenwick Trees, the results are primarily based on bit-packing and sampling - making them very practical - and they also allow for simple optimal parallelization

Boundary conditions in local electrostatics algorithms

We study the simulation of charged systems in the presence of general boundary conditions in a local Monte Carlo algorithm based on a constrained electric field. We firstly show how to implement constant-potential, Dirichlet, boundary conditions by introducing extra Monte Carlo moves to the algorithm. Secondly, we show the interest of the algorithm for studying systems which require anisotropic electrostatic boundary conditions for simulating planar geometries such as membranes.Comment: 8 pages, 6 figures, accepted in JC