Junting Chen

Learning radio maps for UAV-aided wireless networks: A segmented regression approach

This paper targets the promising area of unmanned aerial vehicle (UAV)-assisted wireless networking, by which communication-enabled robots operate as flying wireless relays to help fill coverage or capacity gaps in the networks. In order to feed the UAVs autonomous path planning and positioning algorithm, a radio map is exploited, which must be, in practice, reconstructed from UAV-based measurements from a limited subset of locations. Unlike existing methods that ignore the segmented propagation structure of the radio map, this paper proposes a machine learning approach to reconstruct a finely structured map by exploiting both segmentation and signal strength models. A data clustering and parameter estimation problem is formulated using a maximum likelihood approach, and solved by an iterative clustering and regression algorithm. Numerical results demonstrate significant performance advantage in radio map reconstruction as compared to the baseline.

Precoder feedback versus channel feedback in massive MIMO under user cooperation

In multiuser massive MIMO systems, it is not clear whether users should feed back the channel or the precoder when they can exchange the channel state information (CSI). This paper compares the precoder feedback scheme with the channel feedback scheme. It is found that when there are sufficient number of bits for CSI exchange, the precoder feedback scheme can reduce the interference leakage to 1 /(K - 1) of the channel feedback scheme, where K is the number of users. Moreover, the interference leakage under the precoder feedback scheme decreases faster than the channel feedback scheme when the number of feedback bits increases.

Feedback Mechanisms for FDD Massive MIMO With D2D-Based Limited CSI Sharing

Channel state information (CSI) feedback is a challenging issue in frequency division duplexing (FDD) massive MIMO systems. This paper studies a cooperative feedback scheme, where the users first exchange their CSI with each other through device-to-device (D2D) communications, then compute the precoder by themselves, and feedback the precoder to the base station (BS). Analytical results are derived to show that the cooperative precoder feedback is more efficient than the CSI feedback in terms of interference mitigation. To reduce the delays for CSI exchange, we develop an adaptive CSI exchange strategy based on signal subspace projection and optimal bit partition. Numerical results demonstrate that the proposed cooperative precoder feedback scheme with adaptive CSI exchange significantly outperforms the CSI feedback scheme, even under moderate delays for CSI exchange via D2D.

Optimal positioning of flying relays for wireless networks: A LOS map approach

This paper considers the exploitation of unmanned aerial vehicles (UAVs) in wireless networking, with which communication-enabled robots operate as flying wireless relays to help fill coverage or capacity gaps in the network. We focus on the particular problem of (automatic) UAV positioning, which is known to crucially affect performance. Existing methods typically rely on statistical models of the air-to-ground channel, and thus, they fail to exploit the fine-grained information of line-of-sight (LOS) conditions at some locations. This paper develops an efficient algorithm to find the best position of the UAV based on the fine-grained LOS information. In spite of the complex terrain topology, the algorithm is able to converge to the optimal UAV position to maximize the end-to-end throughput without a global exploration of a signal strength radio map. Numerical results demonstrate that in a dense urban area, the UAV-aided wireless system with the optimal UAV position can almost double the end-to-end capacity from the base station (BS) to the user as compared to that of a direct BS to user link.

Dual-Regularized Feedback and Precoding for D2D-Assisted MIMO Systems

This paper considers the problem of efficient feedback design for massive multiple-input multiple-output (MIMO) downlink transmissions in frequency division duplexing (FDD) bands, where some partial channel state information (CSI) can be directly exchanged between users via device-to-device (D2D) communications. Drawing inspiration from classical point-to-point MIMO, where efficient mechanisms are obtained by feeding back directly the precoder, this paper proposes a new approach to bridge the channel feedback and the precoder feedback by the joint design of the feedback and precoding strategy following a team decision framework. Specifically, the users and the base station (BS) minimize a common mean squared error (MSE) metric based on their individual observations on the imperfect global CSI. The solutions are found to take similar forms as the regularized zero-forcing (RZF) precoder, with additional regularizations that capture any level of uncertainty in the exchanged CSI, in case the D2D links are absent or unreliable. Numerical results demonstrate superior performance of the proposed scheme for an arbitrary D2D link quality setup.

Joint user grouping and beamforming for low complexity massive MIMO systems

In massive MIMO systems, to partition users into groups and serve the groups separately can significantly reduce the processing complexity. In the existing literature, user grouping is done by classifying the channel covariance matrices. Consequently, the inter-group interference due to user grouping and per group processing is not taken into account. In addition, those methods only work for a fixed number of groups, and the optimal group number is hard to determine. In this paper, a joint user grouping and beamforming strategy is proposed to jointly optimize the number of groups, the user grouping, and the beamforming. The scheme is derived by maximizing the total expected signal-to-interference-leakage-and-noise-ratio (SLNR) lower bound in the network via two-timescale stochastic optimization techniques. Numerical results demonstrate significant sum rate performance gain over the baseline scheme in the literature.

Dual-regularized precoding: A robust approach for D2D-enabled massive MIMO

This paper designs efficient feedback mechanisms to help enable massive MIMO in frequency division multiplexing (FDD) bands. By exploiting possible device-to-device (D2D) coordination and using a team decision approach, a scheme is developed to bridge the feedback in the channel space and that in the precoder space. It is found that the desired feedback and precoding vectors take similar forms as the regularized zero-forcing (RZF) precoder, with additional regularizations to capture any level of uncertainty of the exchanged channel state information (CSI). Numerical results demonstrate superior performance of the proposed scheme for an arbitrary D2D link quality setup.