In this paper, we investigate the joint beamforming and power allocation problem in multi-antenna Cellular Vehicle-to-Everything (C-V2X) networks under uncertain channel state information (CSI). Our objective is to minimize the beamforming vector at the base station and the transmit powers of vehicle users while satisfying probabilistic quality-of-service (QoS) constraints. To address the uncertainty of CSI, we first develop an ellipsoid-based uncertainty set learning approach, which models the uncertain CSI as a symmetric ellipsoid. Building on this uncertainty set, we propose a robust counterpart transformation method to reformulate the joint beamforming and power allocation problem into a deterministic semi-definite problem without probabilistic constraints. Through our analysis, the ellipsoid-based uncertainty set exhibits significant conservatism when applied to uncertain CSI with asymmetric distributions. To mitigate this conservatism, we propose a support vector clustering (SVC)-based uncertainty set learning approach, which can tightly enclose the distribution of uncertain CSI. To further simplify the spatial structure of the SVC-based uncertainty set, we develop aK-Medoids-based equivalent set construction (KMSC) approach, significantly reducing the number of variables in the resulting robust equivalent problem. Finally, we conduct extensive simulations to evaluate the performance of our proposed robust approaches and compare them with non-robust methods.

In this work, we consider a covert communication scenario with multiple friendly interference nodes. The goal is to hide a legitimate communication link from a transmitter to a receiver under a warden’s surveillance. Firstly, we propose a novel strategy for generating artificial noise (AN) signals and formulate a corresponding design problem, aiming to minimize the adverse effects of AN on the legitimate receiver while enhancing communication covertness. Specifically, we optimize the basis matrix for AN signal space using statistical information of the involved channel coefficients, when precise channel state information are unavailable. Secondly, we analyze the geometric structure of the AN basis matrix constraint and transform the nonconvex problem into an unconstrained problem on the complex Stiefel manifold. Additionally, we develop a specialized Riemannian Stochastic Variance Reduced Gradient (R-SVRG) algorithm to tackle the problem. In the algorithm, the problem is solved with lower computational complexity compared to full gradient algorithm. Thirdly, we prove that the customized R-SVRG algorithm is theoretically guaranteed to be converge and that the solution is a stationary point of the optimization problem. In the end, simulation results demonstrate the superiority of the proposed AN signal generation strategy over traditional AN in terms of achieving higher covert communication rate.

In this paper, we consider a scenario of covert communication assisted by multiple friendly interference nodes, aimed at concealing the legitimate communication link under the surveillance of a warden. Its main content is summarized in the following: firstly, we propose a novel strategy for generating artificial noise signals. Specifically, in the absence of accurate channel information between the friendly interference nodes and the legitimate receiver, the statistical information of channel coefficients is leveraged to formulate an optimization problem to design the basis matrix of the artificial noise signal space. Secondly, we employ the Riemannian optimization framework to analyze the geometric structure of the basis matrix constraint and transform the original non-convex optimization problem to an unconstrained problem on complex Stiefel manifold. Additionally, we utilize the Riemannian Stochastic Variance Reduced Gradient (RSVRG) algorithm on complex Stiefel manifold to solve the problem, which has the advantage of low computational burden. Thirdly, we prove that the customized R-SVRG algorithm is theoretically guaranteed convergent. In the end, simulation results show that under some mild conditions on covertness and jamming power, the designed artificial noise signals can let the system enjoy better covertness and less impact on the legitimate communication link than the traditional artificial noise signals.

In this paper, we apply angle-domain Doppler compensation for high-mobility wideband massive multi-input multi-output (MIMO) uplink communications. The time-varying multipath channel is considered between high-speed terminal and static base station (BS), where multiple Doppler frequency offsets (DFOs) are associated with distinct angle of departures (AoDs). With the aid of large-scale uniform linear array (ULA) at the transmitter, we design a beamforming network to generate multiple parallel beamforming branches, each transmitting signal pointing to one particular angle. Then, the transmitted signal in each branch will experience only one dominant DFO when passing over the time-varying channel, which can be easily compensated before transmission starts. We theoretically analyze the Doppler spread of the equivalent uplink channel after angle-domain Doppler compensation, which takes into account both the mainlobe and sidelobes of the transmit beam in each branch. It is seen that the channel time-variation can be effectively suppressed if the number of transmit antennas is sufficiently large. Interestingly, the asymptotic scaling law of channel variation is obtained, which shows that the Doppler spread is proportional to the maximum DFO √ and decreases approximately as 1/√(M) (M is the number of transmit antennas) when M is sufficiently large. The numerical results are provided to corroborate the proposed scheme.

In this paper, we propose a Doppler pre-compensation scheme for high-mobility orthogonal frequency division multiplexing (OFDM) uplink, where a high-speed terminal transmits signals to the base station (BS). Considering that the time-varying multipath channel consists of multiple Doppler frequency offsets (DFOs) with different angle of departures (AoDs), we propose to perform DFO pre-compensation at the transmitter with a large-scale uniform linear array (ULA). The transmitted signal passes through a beamforming network with high- spatial resolution to produce multiple parallel branches. Each branch transmits signal towards one direction thus the transmitted signal is affected by one dominant DFO when passing over the time-varying channel. Therefore, we can compensate the DFO for each branch at the transmitter previously. Theoretical analysis for the Doppler spread of the equivalent uplink channel is also conducted. It is found that when the number of transmit antennas is sufficiently large, the time-variation of channel can be efficiently suppressed. Therefore, the performance will not degrade significantly if we apply the conventional time- invariant channel estimation and equalization methods at the receiver. Simulation results are provided to verify the proposed scheme.

With device-to-device (D2D) communications, a user terminal (UT) can be used as a relay node to support multi-hop transmission so that cell-edge or deeply faded users can obtain a better connective experience. In this paper, we investigate energy-efficient transmission for D2D-enabled cooperative networks. We aim to maximize the energy efficiency (EE) of the uplink transmission while guaranteeing the minimum data rate requirement via joint D2D relay node (DRN) placement and power allocation. To solve the problem, we first decompose it into four sub-problems depending on the minimum data rate requirement and the power limits at the UT and DRN and then derive a closed-form solution for each problem. Numerical results demonstrate that the maximum EE can be always achieved with the proposed DRN placement and power allocation.

We investigate multi‐user (MU) multiple‐input multiple‐output (MIMO) scheduling under the practical constraints in long‐term evolution advanced (LTE‐A) downlink cellular networks. The authors first derive the received signal model in MU‐MIMO systems when there exist both inter‐stream interference (ISI) and inter‐user interference (IUI) at each user device. Based on this, they formulate the optimisation problem as joint user pairing, precoding matrix indicator (PMI) selection, and resource block (RB) allocation to maximise the total system throughput. The authors then develop a codebook grouping technique for user pairing and PMI selection in MU‐MIMO. With the help of codebook grouping, the authors propose some suboptimal and low‐complexity scheduling algorithms to improve system throughput. From system‐level simulation, the proposed algorithms can improve the network throughput significantly when exploiting only limited feedback designed for single‐user (SU) MIMO in the LTE‐A specification.

In this paper, we study resource allocation for full-duplex communications in an orthogonal frequency division multiple access femtocell network. We aim to maximize the throughput of the femtocell while avoiding severe inter-tier interference to the macrocell via joint sub-channel assignment and power allocation. To be more practical, we take channel estimation error into account and use the robust optimization theory to model the uncertainty in interference channels. By using the Lagrangian dual method, we decompose the original optimization problem into a primal problem and a dual problem. We adopt the concave-convex procedure to transform the non-convex primal problem into a tractable form through sequential convex approximations and then utilize the sub-gradient method to solve the dual problem. Simulation results show the effectiveness of the proposed algorithm and demonstrate the impact of channel uncertainty on the system performance.

In this paper, we study resource allocation for secondary users (SUs) in underlay full-duplex cognitive networks, where the channel state information of the links between SUs and primary users (PUs) is uncertain. To protect the transmission of the PUs from interference generated by the SUs, we utilize robust optimization theory to characterize the channel uncertainty and formulate a resource allocation problem by jointly optimizing sub-channel assignment, user pairing, and power allocation. By using the dual method, we decompose the original resource allocation problem into a primal problem and a dual problem. We adopt the concave-convex procedure to transform the primal problem into a tractable form through sequential convex approximations while we utilize the sub-gradient method to solve the dual problem. Simulation results demonstrate the effectiveness of our proposed algorithm.

In this paper, we study resource allocation in full-duplex OFDMA networks. We explore the joint optimization of subcarrier assignment, uplink-downlink user pairing, and power allocation to maximize the overall throughput with consideration of self-interference and inter-node interference. By using the dual method, we can decompose the original optimization problem into a primal problem and a dual problem. We adopt the concave-convex procedure to transform the primal problem into a tractable form through sequential convex approximations while we utilize the sub-gradient method to solve the dual problem. Simulation results show that the proposed algorithm can always achieve better throughput in comparison with the existing algorithms.

In this paper, we investigate three-dimensional (3D) multi-user (MU) multiple-input multiple-output (MIMO) transmission for long term evolution advanced (LTE-A) downlink systems. We investigate some key techniques for 3D MU-MIMO to improve the performance of LTE-A systems, including rank and precoding matrix (PM) determination and user pairing. To reduce the complexity caused by a large number of co-scheduled users in 3D MU-MIMO, we develop a simplified and high efficient 3D MU-MIMO scheduling algorithm. The performance improvement of the proposed algorithm is demonstrated by system level simulation.

With device-to-device (D2D) communications, a user terminal can be used as a relay node to support multi-hop transmission, so that cell-edge or deeply faded users can obtain a better connective experience. In this paper, we investigate energy-efficient mobile association in D2D-enabled heterogeneous networks. We consider joint access point selection, mode switching, D2D relay node (DRN) selection, and power control to maximize the energy efficiency (EE) of uplink transmission while guaranteeing the quality-of-service requirement of users. The optimization problem can be decomposed into three subproblems: access point selection, power control, and joint mode switching and DRN selection. The joint mode switching and DRN selection problem is a 0-1 integer optimization problem, whose optimal solution can be found by the brute-force searching method that is complexity-prohibitive when the number of DRNs is large. To reduce the complexity involved in computation, channel estimation, and feedback, we develop a distance-based mobile association (DMA) algorithm, which only operates based on the location information of users and DRNs. Simulation results demonstrate that the proposed DMA algorithm can achieve a good tradeoff between the EE and the complexity.

In this paper, we investigate three-dimensional (3D) multiple-input multiple-output (MIMO) techniques with dynamic rank selection for long term evolution advanced (LTE-A) downlink cellular networks. To facilitate users to transmit different numbers of data streams, we develop a new structure for designing 3D precoding matrix (PM). Then based on the designed PM, 3D MIMO transmission with rank adaptation is proposed. The performance improvement of the proposed algorithms is demonstrated by system level simulation.

With device-to-device (D2D) communications, a user terminal(UT) can naturally be used as a relay node (RN) and thus inherently support multi-hop transmission. Thus, cell-edge or deeply faded users can obtain a more uniform connectivity experience. In this paper, we investigate mobile association for the UT with the capability of D2D communications in heterogeneous networks (HetNets). We will develop a framework on joint mobile association and transmission mode switching between the direct and the D2D relay modes to improve the system spectrum efficiency (SE) and energy efficiency (EE). We first formulate the optimization problems, and then obtain closed-form solutions. Simulation results show that with the proposed schemes, both SE and EE of the network can be significantly improved compared to the traditional solutions without D2D communications. We also discuss the trade-off between the minimum rate requirement and EE of a network.

In this paper, we consider the problem of secure communications for a four-node system consisting of one source, one destination, one eavesdropper, and one helper. We investigate the question of which role should the helper act to improve the secrecy, to jam, or to relay. Two transmission schemes are investigated: (1) direct transmission scheme (DTS) with jamming and (2) relay transmission scheme (RTS). We consider both the path-loss and fading-in channel models and define a notion of distance normalized signal-to-noise-ratio (DN-SNR) to account for propagation. The ergodic secrecy rate (ESR) is adopted as the performance metric and semi-closed-form expressions of ESR for the two schemes are derived. Additionally, optimal power allocations in both low and high DN-SNR regimes are characterized analytically. We give the performance comparison of the two schemes from the perspective of energy efficiency in the low DN-SNR regime, and characterize the secrecy degree of freedom in the high DN-SNR regime. In the high DN-SNR regime, DTS provides higher secrecy rate compared with RTS, while in the low DN-SNR regime, RTS outperforms DTS. Furthermore, we show that eavesdropper's position impacts greatly on security.

In this paper, we investigate multi-user (MU) multiple-input multiple-output (MIMO) scheduling for long term evolution advanced (LTE-A) downlink cellular networks, where only limited feedback designed for single-user (SU) MIMO is available. To help the base station (BS) allocate resource blocks (RBs), we develop codebook grouping and MU-SINR estimation techniques. Two adaptive SU/MU-MIMO scheduling methods are proposed to improve system throughput. Although the proposed adaptive SU/MU- MIMO scheduling algorithms exploit the only limited feedback in the LTE-A specification, they can improve the network throughput by 13% from system level simulation.