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.

In recent years, many researchers have conducted extensive research on spherical robots due to their high flexibility and anti-overturning capabilities. Nevertheless, compared with legged and traditional wheeled robots, spherical robots face certain limitations. The spherical robot is composed of a closed spherical shell structure, which makes the capacity of carrying workloads weak. At the same time, the single point contact with the ground cause the contact friction force with the ground is small, so it is hard to climb obstacles such as steps and doorsill. Therefore, we propose a new solution: the variable wheel diameter ellipsoidal robot (VWDER), which combines the characteristics of two-wheel differential driven robot and spherical robot driven by equivalent pendulum. VWDER is equipped with six retractable shell-shaped legs on each side and this innovative design allows both wheels to independently change diameter while rolling. The main frame of the VWDER can keep the top of the frame facing up under the action of equivalent pendulum during the locomotion, which makes it possible to carry workloads such as manipulator arms, cameras, IMU etc. The VWDER robot can climb steps or doorsill using its two adjacent shell-shaped legs. This paper introduces the design of the VWDER and analyzes the kinematics and dynamics of the VWDER. The experimental results verified the performance of the VWDER, including its autonomous opening and closing, obstacle crossing, automatic reorientation and slope climbing etc.

This paper addresses the mobility problem in extremely large antenna array (ELAA) communication systems. In order to account for the performance loss caused by the spherical wavefront of ELAA in the mobility scenarios, we propose a wavefront transformation-based matrix pencil (WTMP) channel prediction method. In particular, we design a matrix to transform the spherical wavefront into a new wavefront, which is closer to the plane wave. We also design a time-frequency projection matrix to capture the time-varying path delay due to user movement. Furthermore, we adopt the matrix pencil (MP) method to estimate channel parameters. Our proposed WTMP method can mitigate the effect of near-field radiation when predicting future channels. For an ELAA communication system in the mobility scenarios, we prove that the prediction error converges to zero with the increasing number of base station antennas. Simulation results demonstrate that our designed transform matrix efficiently mitigates the near-field effect, and that our proposed WTMP method can overcome the ELAA mobility challenge and approach the performance in stationary settings.

This paper addresses the mobility problem in extremely large antenna array (ELAA) communication systems. In order to account for the performance loss caused by the spherical wavefront of ELAA in the mobility scenario, we propose a wavefront transformation-based matrix pencil (WTMP) channel prediction method. In particular, we design a matrix to transform the spherical wavefront into a new wavefront, which is closer to the plane wave. We also design a time-frequency projection matrix to capture the time-varying path delay. Furthermore, we adopt the matrix pencil (MP) method to estimate channel parameters. Our proposed WTMP method can mitigate the effect of near-field radiation when predicting future channels. Theoretical analysis shows that the designed matrix is asymptotically determined by the angles and distance between the base station (BS) antenna array and the scatterers or the user when the number of BS antennas is large enough. For an ELAA communication system in the mobility scenario, we prove that the prediction error converges to zero with the increasing number of BS antennas. Simulation results demonstrate that our designed transform matrix efficiently mitigates the near-field effect, and that our proposed WTMP method can overcome the ELAA mobility challenge and approach the performance in stationary setting.

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.

The estimation of Channel State Information (CSI) is one of the most difficult tasks for massive multiple-input multiple-output (MIMO) in frequency division duplex (FDD) mode. It is even more challenging in high-mobility scenarios. In this paper, we consider an FDD massive MIMO system with high-mobility and CSI delay and aim to predict the downlink (DL) channel under a realistic multipath channel model. The key novelty lies in the fact that for the first time we devise a joint angle-delay-Doppler (JADD) channel estimation framework. The main idea of our framework is to reconstruct the DL channel with the DL channel parameters estimated from the uplink (UL) channel samples and scalar feedback coefficients. To alleviate the feedback overhead, we design a wideband beamformer for the base station (BS) based on the DL angle-delay-Doppler parameters. The user equipment (UE) then estimates the DL channel parameters and feeds back some Doppler-related scalar coefficients back to the BS. We show that the feedback and DL pilot training overhead are independent of the number of BS antennas. The lower bound performance of our framework is also derived. Numerical results under the industrial channel model in rich scattering environments demonstrate that our framework works well from medium mobility scenario of 30 km/h to high mobility settings of 350 km/h.

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.