This paper proposes a movable antenna (MA) assisted beamforming scheme without digital phase shifters. We perform phase shifts to signals by adjusting MA positions, achieving higher received power compared to fixed position antennas (FPAs). To compute the optimal MA positions, we propose the geometric-detection-based derivative matching optimization (DMO) method. The MAs using the DMO method demonstrate superior cost-effectiveness and energy efficiency compared to FPAs with digital phase shifters. Unlike existing MA optimization methods, the DMO method does not require complex matrix computation. It is non-iterative and can achieve the optimal MA positions under several designed geometric conditions. When the conditions are not satisfied, we prove that the normalized mean square error (NMSE) between the theoretic optimal result and the DMO result can converge to zero. Simulation results demonstrate that the DMO-assisted MA array outperforms the FPA in received power in multi-path propagation environments.

This paper addresses the mobility problem with the assistance of fluid antenna (FA) on the user equipment (UE) side. We propose a matrix pencil-based moving port (MPMP) prediction method, which may transform the time-varying channel to a static channel by timely sliding the liquid. Different from the existing channel prediction method, we design a moving port selection method, which is the first attempt to transform the channel prediction to the port prediction by exploiting the movability of FA. In the performance analysis, we derive the asymptotical lower and upper bounds of the prediction error for a multipath channel, when the number of base station (BS) antennas and the port density of the FA are large enough. When the UEs move at a speed of 120 km/h, simulation results show that, with the assistance of FA, our proposed MPMP method performs better than the existing channel prediction method.

Massive multi-input multi-output (MIMO) in Frequency Division Duplex (FDD) mode suffers from heavy feedback overhead for Channel State Information (CSI). In this paper, a novel manifold learning-based CSI feedback framework (MLCF) is proposed to reduce the feedback and improve the spectral efficiency for FDD massive MIMO. Manifold learning (ML) is an effective method for dimensionality reduction. However, most ML algorithms focus only on data compression, and lack the corresponding recovery methods. Moreover, the computational complexity is high when dealing with incremental data. Considering to utilize the intrinsic manifold structure where the CSI samples reside, we propose a landmark selection algorithm to describe the topological skeleton of this manifold. Based on the learned skeleton, the local patch of the incremental CSI on the manifold can be easily determined by its nearest landmarks. This motivates us to propose an incremental CSI compression and reconstruction scheme by keeping the local geometric relationships with landmarks invariant. We theoretically prove the convergence of the proposed landmark selection algorithm. Meanwhile, the upper bound on the error of approximating CSI with landmarks is derived. Simulation results under an industrial channel model of 3GPP demonstrate that the proposed MLCF outperforms existing deep learning based algorithms.

This paper addresses the mobility problem with the assistance of fluid antenna (FA) on the user equipment (UE) side. We propose a matrix pencil-based moving port (MPMP) prediction method, which may transform the time-varying channel to a static channel by timely sliding the liquid. Different from the existing channel prediction methods, we design a moving port selection method, which is the first attempt to transform the channel prediction to the port prediction by exploiting the movability of FA. Our analysis shows that for a multi-path channel with a strong line-of-sight (LoS) path, the prediction error of our proposed MPMP method nearly converges to zero, as the number of BS antennas and the port density of the FA are large enough. For a general multi-path channel, we also derive the upper and lower bounds of the prediction error when the number of paths is large enough. When the UEs move at a speed of 60 or 120 km/h, simulation results show that, with the assistance of FA, our proposed MPMP method performs better than the existing channel prediction methods.

This paper addresses the mobility problem in massive multiple-input multiple-output systems, which leads to significant performance losses in the practical deployment of the fifth generation mobile communication networks. We propose a novel channel prediction method based on multi-dimensional matrix pencil (MDMP), which estimates the path parameters by exploiting the angular-frequency-domain and angular-time-domain structures of the wideband channel. The MDMP method also entails a novel path pairing scheme to pair the delay and Doppler, based on the super-resolution property of the angle estimation. Our method is able to deal with the realistic constraint of time-varying path delays introduced by user movements, which has not been considered so far in the literature. We prove theoretically that in the scenario with time-varying path delays, the prediction error converges to zero with the increasing number of the base station (BS) antennas, providing that only two arbitrary channel samples are known. We also derive a lower-bound of the number of the BS antennas to achieve a satisfactory performance. Simulation results under the industrial channel model of 3GPP demonstrate that our proposed MDMP method approaches the performance of the stationary scenario even when the users’ velocity reaches 120 km/h and the latency of the channel state information is as large as 16 ms.

Massive multi-input multi-output (MIMO) in Frequency Division Duplex (FDD) mode suffers from heavy feedback overhead for Channel State Information (CSI). In this paper, a novel manifold learning-based CSI feedback framework (MLF) is proposed to reduce the amount of feedback and improve the spectral efficiency of FDD massive MIMO. In most traditional manifold learning approaches, the newly sampled data has to be combined with the existing dataset and the training process has to be done all over again, making it complex to process the incremental CSI in a wireless communication system. Also, the number of component functions required for reconstruction is proportional to the dimension of channel matrix, which limits their practicality in wideband systems. In this paper, we solve the incremental problem by introducing two groups of dictionaries. The key idea of our MLF framework is to learn these dictionaries to represent the manifold structure of CSI data. The incremental CSI is reconstructed by preserving the local manifold structure, i.e., sharing the same neighbor and coding relationships in the input space and the feature space. Experimental results under an industrial channel model of 3GPP show that the proposed algorithm outperforms existing algorithms based on compressive sensing and deep learning in terms of CSI reconstruction performance.

Massive multiple-input multiple-output (MIMO) is believed to deliver unrepresented spectral efficiency gains for 5G and beyond. However, a practical challenge arises during its commercial deployment, which is known as the “curse of mobility”. The performance of massive MIMO drops alarmingly when the velocity level of user increases. In this paper, we tackle the problem in frequency division duplex (FDD) massive MIMO with a novel Channel State Information (CSI) acquisition framework. A joint angle-delay-Doppler (JADD) wideband precoder is proposed for channel training. Our idea consists in the exploitation of the partial channel reciprocity of FDD and the angle-delay-Doppler channel structure. More precisely, the base station (BS) estimates the angle-delay-Doppler information of the UL channel based on UL pilots using Matrix Pencil (MP) method. It then computes the wideband JADD precoders according to the extracted parameters. Afterwards, the user estimates and feeds back some scalar coefficients for the BS to reconstruct the predicted DL channel. Asymptotic analysis shows that the CSI prediction error converges to zero when the number of BS antennas and the bandwidth increases. Numerical results with industrial channel model demonstrate that our framework can well adapt to high speed (350 km/h), large CSI delay (10 ms) and channel sample noise.