Iterative synchronisation and DC-offset estimation using superimposed training

In this paper, we propose a new iterative approach for superimposed training (ST) that improves synchronisation, DC-offset estimation and channel estimation. While synchronisation algorithms for ST have previously been proposed in [2],[4] and [5], due to interference from the data they performed sub-optimally, resulting in channel estimates with unknown delays. These delay ambiguities (also present in the equaliser) were estimated in previous papers in a non-practical manner. In this paper we avoid the need for estimation of this delay ambiguity by iteratively removing the effect of the data “noise”. The result is a BER performance superior to all other ST algorithms that have not assumed a-priori synchronisation

Internet information on birth options after caesarean compared to the RCOG patient information leaflet : a web survey

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Channel estimation and symbol detection for block transmission using data-dependent superimposed training

We address the problem of frequency-selective channel estimation and symbol detection using superimposed training. The superimposed training consists of the sum of a known sequence and a data-dependent sequence that is unknown to the receiver. The data-dependent sequence cancels the effects of the unknown data on channel estimation. The performance of the proposed approach is shown to significantly outperform existing methods based on superimposed training (ST)

Block synchronisation for joint channel and DC-offset estimation using data-dependent superimposed training

In this paper, we propose a new (single-step) block synchronisation algorithm for joint channel and DC-offset estimation for data-dependent superimposed training (DDST). While a (two-step) block synchronisation algorithm for DDST has previously been proposed in [5], due to interference from the information-bearing data it performed sub-optimally, resulting in channel estimates with unknown delays. These delay ambiguities (also present in the equaliser) were then estimated in [5] in a non-practical manner. In this paper we avoid the need for estimation of this delay ambiguity by exploiting the special structure of the channel output’s cyclic mean vector. The result is a BER performance superior to the DDST synchronisation algorithm first published in [5]

Clinical prediction models to inform individualized decision-making in subfertile couples : a stratified medicine approach

Funding This work was supported by a Chief Scientist Office Postdoctoral Training Fellowship in Health Services Research and Health of the Public Research (Ref PDF/12/06). The views expressed in this paper represent the views of the authors and not necessarily the views of the funding body.Peer reviewedPostprin

A low complexity iterative channel estimation and equalisation scheme for (data-dependent) superimposed training

Channel estimation/symbol detection methods based on superimposed training (ST) are known to bemore bandwidth efficient than those based on traditional time-multiplexed training. In this paper we present an iterative version of the ST methodwhere the equalised symbols obtained via ST are used in a second step to improve the channel estimation, approaching the performance of the more recent (and improved) data dependent ST (DDST), but now with less complexity. This iterative ST method (IST) is then compared to a different iterative superimposed training method of Meng and Tugnait (LSST).We show via simulations that the BER of our IST algorithm is very close to that of the LSST but with a reduced computational burden of the order of the channel length. Furthermore, if the LSST iterative approach (originally based on ST) is now implemented using DDST, a faster convergence rate can be achieved for the MSE of the channel estimates

The epidemiology of infectious mononucleosis in Northern Scotland : a decreasing incidence and winter peak

Peer reviewedPublisher PD

Information Centric Modeling for Two-tier Cache Enabled Cellular Networks

In this article, we introduce a new metric called `information centric coverage probability' to characterize the performance of a two-tier cache enabled cellular network. The proposed metric unifies the dynamics of in-network caching and heterogeneous networking to provide a unified performance measure. Specifically, it quantifies the probability that a mobile user (MU) is covered at a desired rate when a certain content is requested from a global content library. In other words, it quantifies the percentage of time when an MU can be served locally without paying the traffic penalties at backhaul, fronthaul and core networks. Caching dynamics are modeled by considering that the content which is least recently used (LRU) is evicted while the requested content is stored in the cache. The considered two-tier cellular model leverages coordination between the macro base-station (MBS) and the small cell base-stations (SBSs) to maximize the resource efficiency. More specifically, coordination between macro and small cells enables an arbitrary SBS to exploit the caches at other SBSs in the neighborhood. Thus reducing the requirement for huge and expensive memory modules at individual SBSs. The spatial dynamics of cellular network are modeled by borrowing well established tools from stochastic geometry. Propagation uncertainties are explicitly factored in characterization by considering the small scale Rayleigh fading and the large scale power-law path-loss model. It is shown that the information centric coverage probability is a function of (i) the size of caches at the SBSs and the MBS; (ii) the content eviction strategy; (iii) the underlying popularity law for referenced objects; (iv) the size of the global content library; (v) desired downlink transmission rate; (vi) the amount of spectrum allocated to each tier; (vii) pathloss exponent; and (viii) the deployment density of the SBSs and the MBSs. Our analysis reveals that significant performance gains can be harnessed with appropriate dimensioning of both cache sizes and deployment density. Finally, identification of memory limited vs. QoS limited operational regime for two-tier cellular networks is considered

Simplified Chirp Dictionary for Time-Frequency Signature Sparse Reconstruction of Radar Returns

In sparse reconstruction of the Doppler frequency, the chirp atom approach has been shown to give a better performance than its sinusoidal counterpart. Nevertheless, the chirp atom has a relatively large dimension and so its computational load is much greater compared to the sinusoidal atom. In this paper, we propose a simplified chirp dictionary that obtains a satisfactory time-frequency signature approximation of the signals, but with a computational load comparable to the sinusoidal atom. We estimate the chirp rate through the DTFT of the bilinear product at a certain lag, and the initial frequency is solved in the time domain

Distributed Two-Step Quantized Fusion Rules via Consensus Algorithm for Distributed Detection in Wireless Sensor Networks

We consider the problem of distributed soft decision fusion in a bandwidth-constrained spatially uncorrelated wireless sensor network (WSN). The WSN is tasked with the detection of an intruder transmitting an unknown signal over a fading channel. Existing distributed consensus-based fusion rules algorithms only ensure equal combining of local data and in the case of bandwidth-constrained WSNs, we show that their performance is poor and does not converge across the sensor nodes (SNs). Motivated by this fact, we propose a two-step distributed quantized fusion rule algorithm where in the first step the SNs collaborate with their neighbors through error-free, orthogonal channels (the SNs exchange quantized information matched to the channel capacity of each link). In the second step, local 1-bit decisions generated in the first step are shared among neighbors to yield a consensus. A binary hypothesis testing is performed at any arbitrary SN to optimally declare the global decision. Simulations show that our proposed quantized two-step distributed detection algorithm approaches the performance of the unquantized centralized (with a fusion center) detector and its power consumption is shown to be 50% less than the existing (unquantized) conventional algorithm