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Register a new userDeterministic Pilot Design and Channel Estimation for Downlink Massive MIMO-OTFS Systems in Presence of the Fractional Doppler
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Added by
Ding Shi
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Although the combination of the orthogonal time frequency space (OTFS) modulation and the massive multiple-input multiple-output (MIMO) technology can make communication systems perform better in high-mobility scenarios, there are still many challenges in downlink channel estimation owing to inaccurate modeling and high pilot overhead in practical systems. In this paper, we propose a channel state information (CSI) acquisition scheme for downlink massive MIMO-OTFS in presence of the fractional Doppler, including deterministic pilot design and channel estimation algorithm. First, we analyze the input-output relationship of the single-input single-output (SISO) OTFS based on the orthogonal frequency division multiplexing (OFDM) modem and extend it to massive MIMO-OTFS. Moreover, we formulate an accurate model for the practical system in which the fractional Doppler is considered and the influence of subpaths is revealed. A deterministic pilot design is then proposed based on the model and the structure of the pilot matrix to reduce pilot overhead and save memory consumption. Since channel geometry changes very slowly relative to the communication timescale, we put forward a modified sensing matrix based channel estimation (MSMCE) algorithm to acquire the downlink CSI. Simulation results demonstrate that the proposed downlink CSI acquisition scheme has significant advantages over traditional algorithms.
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This paper investigates downlink channel estimation in frequency-division duplex (FDD)-based massive multiple-input multiple-output (MIMO) systems. To reduce the overhead of downlink channel estimation and uplink feedback in FDD systems, cascaded precoding has been used in massive MIMO such that only a low-dimensional effective channel needs to be estimated and fed back. On the other hand, traditional channel estimations can hardly achieve the minimum mean-square-error (MMSE) performance due to lack of the a priori knowledge of the channels. In this paper, we design and analyze a strategy for downlink channel estimation based on the parametric model in massive MIMO with cascaded precoding. For a parametric model, channel frequency responses are expressed using the path delays and the associated complex amplitudes. The path delays of uplink channels are first estimated and quantized at the base station, then fed forward to the user equipment (UE) through a dedicated feedforward link. In this manner, the UE can obtain the a priori knowledge of the downlink channel in advance since it has been demonstrated that the downlink and the uplink channels can have identical path delays. Our analysis and simulation results show that the proposed approach can achieve near-MMSE performance.
We consider the problem of channel estimation for uplink multiuser massive MIMO systems, where, in order to significantly reduce the hardware cost and power consumption, one-bit analog-to-digital converters (ADCs) are used at the base station (BS) to quantize the received signal. Channel estimation for one-bit massive MIMO systems is challenging due to the severe distortion caused by the coarse quantization. It was shown in previous studies that an extremely long training sequence is required to attain an acceptable performance. In this paper, we study the problem of optimal one-bit quantization design for channel estimation in one-bit massive MIMO systems. Our analysis reveals that, if the quantization thresholds are optimally devised, using one-bit ADCs can achieve an estimation error close to (with an increase by a factor of $pi/2$) that of an ideal estimator which has access to the unquantized data. The optimal quantization thresholds, however, are dependent on the unknown channel parameters. To cope with this difficulty, we propose an adaptive quantization (AQ) approach in which the thresholds are adaptively adjusted in a way such that the thresholds converge to the optimal thresholds, and a random quantization (RQ) scheme which randomly generate a set of nonidentical thresholds based on some statistical prior knowledge of the channel. Simulation results show that, our proposed AQ and RQ schemes, owing to their wisely devised thresholds, present a significant performance improvement over the conventional fixed quantization scheme that uses a fixed (typically zero) threshold, and meanwhile achieve a substantial training overhead reduction for channel estimation. In particular, even with a moderate number of pilot symbols (about 5 times the number of users), the AQ scheme can provide an achievable rate close to that of the perfect channel state information (CSI) case.
Massive multiple-input multiple-output is a very important technology for future fifth-generation systems. However, massive massive multiple input multiple output systems are still limited because of pilot contamination, impacting the data rate due to the non-orthogonality of pilot sequences transmitted by users in the same cell to the neighboring cells. We propose a channel estimation with complete knowledge of large-scale fading by using an orthogonal pilot reuse sequence to eliminate PC in edge users with poor channel quality based on the estimation of large-scale fading and performance analysis of maximum ratio transmission and zero forcing precoding methods. We derived the lower bounds on the achievable downlink DR and signal-to-interference noise ratio based on assigning PRS to a user grouping that mitigated this problem when the number of antenna elements approaches infinity The simulation results showed that a high DR can be achieved due to better channel estimation and reduced performance loss
Massive Multiple-Input Multiple-Output (massive MIMO) is a variant of multi-user MIMO in which the number of antennas at each Base Station (BS) is very large and typically much larger than the number of users simultaneously served. Massive MIMO can be implemented with Time Division Duplexing (TDD) or Frequency Division Duplexing (FDD) operation. FDD massive MIMO systems are particularly desirable due to their implementation in current wireless networks and their efficiency in situations with symmetric traffic and delay-sensitive applications. However, implementing FDD massive MIMO systems is known to be challenging since it imposes a large feedback overhead in the Uplink (UL) to obtain channel state information for the Downlink (DL). In recent years, a considerable amount of research is dedicated to developing methods to reduce the feedback overhead in such systems. In this paper, we use the sparse spatial scattering properties of the environment to achieve this goal. The idea is to estimate the support of the continuous, frequency-invariant scattering function from UL channel observations and use this estimate to obtain the support of the DL channel vector via appropriate interpolation. We use the resulting support estimate to design an efficient DL probing and UL feedback scheme in which the feedback dimension scales proportionally with the sparsity order of DL channel vectors. Since the sparsity order is much less than the number of BS antennas in almost all practically relevant scenarios, our method incurs much less feedback overhead compared with the currently proposed methods in the literature, such as those based on compressed-sensing. We use numerical simulations to assess the performance of our probing-feedback algorithm and compare it with these methods.
In this paper, we investigate the robust linear precoder design for three dimensional (3D) massive multi-input multi-output (MIMO) downlink with uniform planar array (UPA) and imperfect channel state information (CSI). In practical massive MIMO with UPAs, the number of antennas in each column or row is usually limited. The straightforward extension of the conventional DFT based beam domain channel model widely used in massive MIMO with uniform linear arrays (ULAs) can not apply. To overcome this issue, we establish a new beam domain channel model by using sampled steering vectors. Then, a novel method to obtain the beam domain channel power matrices and the instantaneous beam domain channel coefficients is proposed, and an a posteriori beam domain channel model which includes the channel aging and the spatial correlation is established. On the basis of the a posteriori channel model, we consider the robust precoder design with the expected weighted sum-rate maximization under a total power constraint. By viewing the power constraint as a Riemannian manifold, we transform the constrained optimization problem into an unconstrained optimization problem on the Riemannian manifold. Then, we derive an iterative algorithm to obtain the optimal precoders by setting the Riemannian gradient of the objective function to zero. Furthermore, we propose a low complexity robust precoder design by replacing the expected rates in the objective function with their upper bounds. Simulation results show that the proposed precoders can achieve significant performance gain than the widely used regularized zero forcing (RZF) precoder and signal to leakage noise ratio (SLNR) precoder.
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