Sha Hu

A low-complexity channel shortening receiver with diversity support for evolved 2G devices

The second generation (2G) cellular networks are the current workhorse for machine-to-machine (M2M) communications. Diversity in 2G devices can be present both in form of multiple receive branches and blind repetitions. In presence of diversity, intersymbol interference (ISI) equalization and co-channel interference (CCI) suppression are usually very complex. In this paper, we consider the improvements for 2G devices with receive diversity. We derive a low-complexity receiver based on a channel shortening filter, which allows to sum up all diversity branches to a single stream after filtering while keeping the full diversity gain. The summed up stream is subsequently processed by a single stream Max-log-MAP (MLM) equalizer. The channel shortening filter is designed to maximize the mutual information lower bound (MILB) with the Ungerboeck detection model. Its filter coefficients can be obtained mainly by means of discrete-Fourier transforms (DFTs). Compared with the state-of-art homomorphic (HOM) filtering based channel shortener which cooperates with a delayed-decision feedback MLM (DDF-MLM) equalizer, the proposed MILB channel shortener has superior performance. Moreover, the equalization complexity, in terms of real-valued multiplications, is decreased by a factor that equals the number of diversity branches.

On the design of reduced state demodulators with interference cancellation for iterative receivers

We consider the problem of designing demodulators for channels with memory that use reduced-size trellis descriptions for the received signal. We assume an overall iterative receiver, and for the parts of the signal not covered by the trellis description, we use interference cancellation based on the soft information provided by the outer decoder. In order to reach a trellis description, a linear filter is applied as front-end to compress the signal structure into a small trellis. This process requires three parameters to be designed: (i) the front-end filter, (ii) the feedback filter through which the interference cancellation is done, and (iii) a target response which specifies the trellis. While (i) and (ii) can be found in closed form, a numerical search is required for (iii). The numerical search is, however, very efficient and stable. Demodulators of this form have been studied before under the name channel shortening (CS), but the interplay between CS and interference cancellation has not been adequately addressed in the literature.

Exploiting antenna correlation in measured massive MIMO channels

We investigate antenna correlation of an M-antenna massive multiple-input multiple-output (MIMO) setup with the purpose of obtaining a low-rank representation of the instantaneous massive MIMO channel. Low-rank representation bases using short-term and long-term antenna correlation statistics are defined, and their performance is evaluated with data sets obtained from channel measurements in both indoor and outdoor environments at 2.6 GHz. Our results indicate that the short-term bases can capture a larger amount of the channel energy compared to the long-term ones, but they have a limited timespan, one coherence time or less. On the other hand, the long-term bases are stable over time-spans of a few seconds. Hence, they can be obtained relatively easily. We also investigate a rank-p vector-scalar LMMSE channel estimator that exploits antenna correlation. Our results show that the investigated estimator can achieve a performance similar to that of full-rank LMMSE at a (2p + 1)/M times lower cost. The investigated estimator may be used in conjunction with estimators that exploit correlation in the frequency and time domains or, alternatively, in situations in which these estimators cannot be used, e.g., when pilot separation is larger than the channel coherence bandwidth or time.

A Soft-Output MIMO Detector with Achievable Information Rate based Partial Marginalization

In this paper, we propose a soft-output detector for multiple-input multiple-output (MIMO) channels that utilize achievable information rate (AIR) based partial marginalization (PM). The proposed AIR based PM (AIR-PM) detector has superior performance compared to previously proposed PM designs and other soft-output detectors such as K-best, while at the same time yielding lower computational complexity, a detection latency that is independent of the number of transmit layers, and straightforward inclusion of soft-input information. Using a tree representation of the MIMO signal, the key property of the AIR-PM is that the connections among all child layers are broken. Therefore, least-square estimates used for marginalization are obtained independently and in parallel, which have better quality than the zero-forcing decision feedback estimates used in previous PM designs. Such a property of the AIR-PM detector is designed via a mismatched detection model that maximizes the AIR. Furthermore, we show that the chain rule holds for the AIR calculation, which facilitates an information theoretic characterization of the AIR-PM detector.

Comparison of two channel shortening approaches for MIMO-ISI channels

We consider a multiple-input multiple-output (MIMO) channel with inter-symbol interference (ISI) where signal detection is highly complex due to the large signal state-space dimensionality. A common strategy is to apply a front-end filter (FEF) to eliminate the ISI dimension (i.e. full ISI equalization). This FEF is then followed by a frequency non-selective MIMO detector. However, another, much less researched, strategy is to apply an FEF that eliminates the MIMO dimension and results in a set of parallel ISI channels followed by a bank of parallel single-input single-output (SISO) detectors. Which of the two strategies is better in a Shannon capacity sense? In this paper, we show that the answer to this question depends on system parameters such as SNR, number of antennas, ISI duration, and spatial correlation properties.

The Potential of Using Large Antenna Arrays on Intelligent Surfaces

In this paper, we consider capacities of single- antenna terminals communicating to large antenna arrays that are deployed on surfaces. That is, the entire surface is used as an intelligent receiving antenna array. Under the condition that the surface area is sufficiently large, the received signal after matched-filtering (MF) can be well approximated by an intersymbol interference (ISI) channel where channel taps are closely related to a sinc function. Based on such an approximation, we have derived the capacities for both one- dimensional (terminals on a line) and high dimensional (terminals on a plane or in a cube) terminal-deployments. In particular, we analyze the normalized capacity

Sequential channel estimation in the presence of random phase noise in NB-IoT systems

\bar{\mathcal{C}}

A generalized zero-forcing precoder for multiple antenna Gaussian broadcast channels

, measured in nats/s/Hz/m

Optimal Channel Shortener Design for Reduced- State Soft-Output Viterbi Equalizer in Single-Carrier Systems

^2

Channel shortening algorithms for multiple intersymbol interference channels

, under the constraint that the transmit power per m