Enhanced Estimation of a Noisy Quantum Channel Using Entanglement

We discuss the estimation of channel parameters for a noisy quantum channel - the so-called Pauli channel - using finite resources. It turns out that prior entanglement considerably enhances the fidelity of the estimation when we compare it to an estimation scheme based on separable quantum states.Comment: 4 pages, 2 figure

Performance of adaptive bayesian equalizers in outdoor environments

Outdoor communications are affected by multipath propagation that imposes an upper limit on the system data rate and restricts possible applications. In order to overcome the degrading effect introduced by the channel, conventional equalizers implemented with digital filters have been traditionally used. A new approach based on neural networks is considered. In particular, the behavior of the adaptive Bayesian equalizer implemented by means of radial basis functions applied to the channel equalization of radio outdoor environments has been analyzed. The method used to train the equalizer coefficients is based on a channel response estimation. We compare the results obtained with three channel estimation methods: the least sum of square errors (LSSE) channel estimation algorithm, recursive least square (RLS) algorithm employed only to obtain one channel estimation and, finally, the RLS algorithm used to estimate the channel every decided symbol for the whole frame.Peer ReviewedPostprint (published version

Channel Estimation for Millimeter-Wave Massive MIMO with Hybrid Precoding over Frequency-Selective Fading Channels

Channel estimation for millimeter-wave (mmWave) massive MIMO with hybrid precoding is challenging, since the number of radio frequency (RF) chains is usually much smaller than that of antennas. To date, several channel estimation schemes have been proposed for mmWave massive MIMO over narrow-band channels, while practical mmWave channels exhibit the frequency-selective fading (FSF). To this end, this letter proposes a multi-user uplink channel estimation scheme for mmWave massive MIMO over FSF channels. Specifically, by exploiting the angle-domain structured sparsity of mmWave FSF channels, a distributed compressive sensing (DCS)-based channel estimation scheme is proposed. Moreover, by using the grid matching pursuit strategy with adaptive measurement matrix, the proposed algorithm can solve the power leakage problem caused by the continuous angles of arrival or departure (AoA/AoD). Simulation results verify that the good performance of the proposed solution.Comment: 4 pages, 3 figures, accepted by IEEE Communications Letters. This paper may be the first one that investigates the frequency selective fading channel estimation for mmWave massive MIMO systems with hybrid precoding. Key words: Millimeter-wave (mmWave) massive MIMO, frequency-selective fading, channel estimation, compressive sensing, hybrid precodin

Performance evaluation of channel estimation techniques for MIMO-OFDM systems with adaptive sub-carrier allocation

Dynamic Sub-Carrier Allocation (DSA) strategies have been shown previously to achieve significant performance benefits when applied to OFDMA systems and further benefits for MIMOOFDMA systems. Analysis thus far has focussed on the assumption of ideal Channel State Information (CSI). In this paper, the impact of non-ideal CSI is investigated. Various channel estimation techniques are evaluated for application to MIMO-OFDMA systems. They are based on Least Squares (LS) Estimation with training pilots. ‘Conventional’ (as for MIMO-OFDM) CTP (combining training pilots and ‘improved’ (optimised for MIMO-OFDMA) STP (Separate training pilots) versions of both Frequency Domain Least Square (FDLS) and Time Domain Least Square (TDLS) channel estimation are considered, as are the options of both Space- Time Block coding (STBC) and Spatial Multiplexing (SM) as MIMO strategies. The STP-TDLS strategy is shown to significantly outperform other channel estimation options, achieving performance within 1dB of the ideal case. Subsequently, the use of the STP-TDLS channel estimation method in conjunction with the DSA algorithm is considered in order to determine the impact of non-ideal channel knowledge on the gain achieved by DSA. The performance for the cases of ideal CSI and CSI derived via STP-TDLS channel estimation are compared and evaluated for both the STBC and SM cases. The effects of non-ideal channel estimation in both the DSA mechanism and channel equalisation separately and together are evaluated. It is shown that STP-TDLS channel estimation works better in SM (only 1dB worse than ideal CSI case) than in STBC. Furthermore, it is shown that DSA is less sensitive than channel equalisation to nonideal CSI. The degradation of system performance in the realistic case of non-ideal CSI for both DSA and channel equalisation is a compound of the effects of the separate effects of non-ideal CSI error. It is shown here that in both STBC and SM cases, the effect is almost a linear addition of the two parts. Given the substantial benefits of DSA and its relative insensitivity to channel estimation errors, it is concluded that DSA remains a highly promising technique

Compressive sensing based Bayesian sparse channel estimation for OFDM communication systems: high performance and low complexity

In orthogonal frequency division modulation (OFDM) communication systems, channel state information (CSI) is required at receiver due to the fact that frequency-selective fading channel leads to disgusting inter-symbol interference (ISI) over data transmission. Broadband channel model is often described by very few dominant channel taps and they can be probed by compressive sensing based sparse channel estimation (SCE) methods, e.g., orthogonal matching pursuit algorithm, which can take the advantage of sparse structure effectively in the channel as for prior information. However, these developed methods are vulnerable to both noise interference and column coherence of training signal matrix. In other words, the primary objective of these conventional methods is to catch the dominant channel taps without a report of posterior channel uncertainty. To improve the estimation performance, we proposed a compressive sensing based Bayesian sparse channel estimation (BSCE) method which can not only exploit the channel sparsity but also mitigate the unexpected channel uncertainty without scarifying any computational complexity. The propose method can reveal potential ambiguity among multiple channel estimators that are ambiguous due to observation noise or correlation interference among columns in the training matrix. Computer simulations show that propose method can improve the estimation performance when comparing with conventional SCE methods.Comment: 24 pages,16 figures, submitted for a journa