We consider the problem of high-dimensional channel estimation in fast time-varying millimeter-wave MIMO systems with a hybrid architecture. By exploiting the low-rank and sparsity properties of the channel matrix, we propose a two-phase compressed sensing framework consisting of observation matrix completion and channel matrix sparse recovery, respectively. First, we formulate the observation matrix completion problem as a low-rank matrix completion (LRMC) problem and develop a robust rank-one matrix completion (R1MC) algorithm that enables the matrix and its rank to iteratively update. This approach achieves high-precision completion of the observation matrix and explicit rank estimation without prior knowledge. Second, we devise a rank-aware batch orthogonal matching pursuit (OMP) method for achieving low-latency sparse channel recovery. To handle abrupt rank changes caused by user mobility, we establish a discrete-time autoregressive (AR) model that leverages the temporal rank correlation between continuous-time instances to obtain a complete observation matrix capable of perceiving rank changes for more accurate channel estimates. Simulation results confirm the effectiveness of the proposed channel estimation frame and demonstrate that our algorithms achieve state-of-the-art performance in low-rank matrix recovery with theoretical guarantees.

This paper investigates the secure transmission problem in satellite-terrestrial integrated network (STIN) and a cross-domain hybrid precoding security scheme is proposed. Considering a multi-user STIN system with satellite terrestrial co-channel interference and satellite inter-beam interference. The scheme in this paper uses the above interference as green interference in spatial domain, and weighted fractional Fourier transform (WFRFT) precoding is utilized as signal domain interference. To achieve system security, a problem of maximizing system secure energy efficiency (SEE) through cross-domain hybrid precoding is proposed. Meanwhile, the signal to interference plus noise ratio (SINR) requirements of satellite users and terrestrial users, along with the transmit power constraints need to be satisfied. Due to the non-convexity and high complexity of the problem, Taylor expansion and semidefinite relaxation (SDR) are used to reformulate the problem. Besides, algorithms of fractional programming and alternating search are designed to solve the problem. Finally, the feasibility and practicality of the proposed scheme are verified by numerical simulation.

The emergence of Industry 4.0 has introduced stringent demands on industrial networks regarding determinism, flexibility, and scalability. Integrating Time-Sensitive Networking (TSN) with 5G offers a promising solution, but efficient cross-domain (wired-wireless) cooperative scheduling remains a challenge. To address this, we propose Wireless Cyclic Queuing and Forwarding (W-CQF), an innovative cooperative scheduling scheme that extends TSN’s CQF mechanism to 5G networks. By synchronizing scheduling cycles across domains, W-CQF enables end-to-end deterministic transmission. The approach consists of: (1) Offline static planning, which leverages Network Calculus to derive constraints for optimizing traffic injection times, balancing load, and maximizing schedulable Time-Sensitive (TS) flows; and (2) Online dynamic allocation, where the 5G base station (gNB) fulfills its service guarantees by reserving resources for TS flows while utilizing remaining capacity for Best-Effort (BE) traffic. Simulations show that W-CQF outperforms uncoordinated CQF (UC-CQF) and traditional Time-Aware Shaper (TAS)-based methods, significantly improving mixed-traffic handling, wireless resource efficiency, and end-to-end reliability while meeting strict latency requirements.

Millimeter wave (mmWave) communications are highly affected by blockage, whereas the emerging reconfigurable intelligent surface (RIS) has the potential to overcome this issue. This paper proposes a Neyman-Pearson (N-P) criterion-based blockage-aware algorithm to improve resilience to blockage in mobile mmWave multiple input multiple output (MIMO) systems. By virtue of this pragmatic blockage-aware technique, we further propose an outage-constrained beamforming design for RIS-assisted mmWave MIMO transmission to achieve outage probability minimization and achievable rate maximization. Specifically, we propose an accelerated projected gradient descent (PGD) algorithm to solve the computational challenge of high-dimensional RIS phase-shift matrix (PSM) optimization. Particularly, we formulate a new Nesterov momentum acceleration scheme to speed up the convergence rate. Extensive experiments confirm the effectiveness of the proposed blockage-aware approach and the proposed accelerated PGD algorithm outperforms a number of representative baseline algorithms in terms of the achievable rate performance.

In high-speed mobile scenarios, conventional estimation schemes for millimetre-wave multiple-input multiple-output (MIMO) communication systems may fail to track of time varying channels, so this paper proposes an efficient channel estimation scheme for time-varying millimetre-wave channels. Since the arrival/departure angles (AoAs/AoDs) vary much slower than the path gains, the channel estimation is decoupled into two stages. In the first stage, we utilise a full-rank pilot structure to construct a maximum likelihood estimation (ML) of the observed diagonal covariance matrices to reduce the inter-user interference and estimate the channel covariance matrix. Then, we utilize the special structure of the channel covariance matrix to accomplish the estimation of AoAs/AoDs, Doppler shift with low complexity. In the second stage, we utilize the estimates from the previous stage as a priori knowledge and track the time-varying parameter path gains using a Kalman filter to achieve accurate estimation of the path gain. Simulation results show that the proposed scheme can accurately estimate the time-varying millimeter-wave channel based on a small number of pilot frequencies.

A robust hybrid beamforming algorithm for millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems with imperfect channel state information (CSI) is presented. The imperfect CSI is modeled as a norm ball/bounded model assuming that the actual channel lies within the neighborhood of the estimated channel, leading to the worst-case robust design. In particular, we first utilize the S-procedure to transform the non-convex problem with uncertain CSI into an easily solvable convex semidefinite programming (SDP) problem, thereby obtaining the optimal unconstrained beamformer. Then, we derive the hybrid beamformer that approximates the optimal one by alternating optimization. In this step, the non-convex problem arising from the constant modulus constraints is addressed by the successive convex approximation (SCA). Numerical simulations validate the theoretical analysis and demonstrate that the proposed algorithm is robust to imperfect CSI.

Millimeter wave (mmWave) communications are sensitive to blockage over radio propagation paths. The emerging paradigm of reconfigurable intelligent surface (RIS) has the potential to overcome this issue by its ability to arbitrarily reflect the incident signals toward desired directions. This paper proposes a Neyman-Pearson (NP) criterion-based blockage-aware algorithm to improve communication resilience against blockage in mobile mmWave multiple input multiple output (MIMO) systems. By virtue of this pragmatic blockage-aware technique, we further propose an outage-constrained beamforming design for downlink mmWave MIMO transmission to achieve outage probability minimization and achievable rate maximization. To minimize the outage probability, a robust RIS beamformer with variant beamwidth is designed to combat uncertain channel state information (CSI). For the rate maximization problem, an accelerated projected gradient descent (PGD) algorithm is developed to solve the computational challenge of high-dimensional RIS phase-shift matrix (PSM) optimization. Particularly, we leverage a subspace constraint to reduce the scope of the projection operation and formulate a new Nesterov momentum acceleration scheme to speed up the convergence process of PGD. Extensive experiments confirm the effectiveness of the proposed blockage-aware approach, and the proposed accelerated PGD algorithm outperforms a number of representative baseline algorithms in terms of the achievable rate.

Multiple-input multiple-output orthogonal frequency division multiplexing with index modulation (MIMO-OFDM-IM) has recently received increased attention, due to the potential advantage to balance the trade-off between spectral efficiency (SE) and energy efficiency (EE). In this paper, we investigate the application of MIMO-OFDM-IM to millimeter wave (mmWave) communication systems, where a hybrid analogy-digital (HAD) beamforming architecture is employed. Taking advantage of the Pareto-optimal beam design, we propose a feasible solution to approximately achieve a globally Pareto-optimal trade-off between SE and EE, and the collision constraints of the multi-objective optimization problem (MOP) can be solved efficiently. Correspondingly, the MOP of SE-EE trade-off can be converted into a feasible solution for energy-efficient resource usage, by finding the Pareto-optimal set (POS) towards the Pareto front. This combinatorial-oriented resource allocation approach on the SE-EE relation considers the optimal beam design and power control strategies for downlink multi-user mmWave transmission. To ease the system performance evaluation, we adopt the Poisson point process (PPP) to model the mobile data traffic, and the evolutionary algorithm is applied to speed up the search efficiency of the Pareto front. Compared with benchmarks, the experimental results collected from extensive simulations demonstrate that the proposed optimization approach is vastly superior to existing algorithms.

Multiple-input multiple-output orthogonal frequency division multiplexing with index modulation (MIMO-OFDM-IM) has the potential advantage to balance the trade-off between spectral efficiency (SE) and energy efficiency (EE). This paper investigates the application of MIMO-OFDM-IM to millimeter wave (mmWave) communication systems. Taking advantage of the properties of Pareto optimality, we propose a feasible solution to achieve a globally Pareto-optimal trade-off between SE and EE, and the collision constraints of multi-objective optimization problem (MOP) can be solved efficiently. The MOP of SE-EE trade-off can then be converted into a Pareto-optimal set (POS) solution problem. This combinatorial-oriented resource allocation approach on SE-EE relation considers the optimal beam design and power reallocation for downlink multi-user mmWave transmission. We adopt the Poisson point process (PPP) to model the mobile data traffic, and the evolutionary algorithm is applied to speed up the search efficiency of the Pareto front. Compared with benchmarks, the experimental results collected from extensive simulations reveal that the proposed optimization approach is vastly superior to existing algorithms.

This paper proposes a Bayesian approach for angle-based hybrid beamforming and tracking that is robust to uncertain or erroneous direction-of-arrival (DOA) estimation in millimeter wave (mmWave) multiple input multiple output (MIMO) systems. Because the resolution of the phase shifters is finite and typically adjustable through a digital control, the DOA can be modeled as a discrete random variable with a prior distribution defined over a discrete set of candidate DOAs, and the variance of this distribution can be introduced to describe the level of uncertainty. The estimation problem of DOA is thereby formulated as a weighted sum of previously observed DOA values, where the weights are chosen according to a posteriori probability density function (pdf) of the DOA. To alleviate the computational complexity and cost, we present a motion trajectory-constrained a priori probability approximation method. It suggests that within a specific spatial region, a directional estimate can be close to true DOA with a high probability and sufficient to ensure trustworthiness. We show that the proposed approach has the advantage of robustness to uncertain DOA, and the beam tracking problem can be solved by incorporating the Bayesian approach with an expectation-maximization (EM) algorithm. Simulation results validate the theoretical analysis and demonstrate that the proposed solution outperforms a number of state-of-the-art benchmarks.

A Bayesian approach for joint beamforming and tracking is presented, which is robust to uncertain direction-of-arrival (DOA) estimation in millimeter wave (mmWave) multiple input multiple output (MIMO) systems. The uncertain or completely unknown DOA is modeled as a discrete random variable with a priori distribution defined over a set of candidate DOAs, which describes the level of uncertainty. The estimation problem of DOA is formulated as a weighted sum of previously observed DOA values, where the weights are chosen according to a posteriori probability density function (pdf) of the DOA. In particular, we present a motion trajectory-based a priori probability approximation method, which implies a high probability to perform a directional estimate within a specific spatial region. We demonstrate that the proposed approach is robust to DOA uncertainty, and the beam tracking problem can be addressed by incorporating the Bayesian approach with an expectation-maximization (EM) algorithm. Simulation results validate the theoretical analysis and demonstrate the effectiveness of the proposed solution.

This paper proposes a Markov decision process (MDP)-based pilot placement optimization approach for the radio access in 5G vehicle to everything communications to support Internet of Vehicles applications. The optimal placement problem of pilot symbols is based on a typical pilot-assisted frequency-division multiplexing transmission and simplified to a finite state-space representation. We propose and formulate a finite MDP so as to determine an appropriate pilot pattern from a set of candidate pilot configurations. Also, an enhanced pilot placement scheme is developed to reduce the complexity for solving the formulated MDP problems. Furthermore, we derive analytical expressions of the mutual information, which to some extent allow us to jointly evaluate the dynamics of the channel state in time and frequency domains. Numerical results generated by Monte Carlo simulations show that the proposed pilot optimization policy is capable of improving the channel estimation in fast time-varying vehicular channels, and the mutual information-based measurement criteria can yield more accurate evaluations in fast time-varying vehicular channels than other conventional schemes.

This paper proposes an Markov decision process (MDP) based pilot placement optimization approach for the radio access in 5G vehicle to everything (V2X) communications. The optimal placement problem of pilot symbols is based on a typical pilot-assisted OFDM transmission and simplified to a finite state-space representation. We propose and formulate a finite MDP so as to determine an appropriate pilot pattern from a set of candidate pilot configurations. Additionally, an enhanced pilot placement scheme is developed to reduce the complexity of solving MDP problems. We derive analytical expressions of the mutual information, which to some extent allow us to jointly evaluate the dynamics of the channel state in time and frequency domain. Numerical results show that the proposed pilot optimization policy is capable of improving the channel estimation, and the mutual information based measurement criteria can yield more accurate evaluations in fast time-varying vehicular channels.

This paper proposes a decision-assisted pilot placement optimization method in IEEE802.11p physical layer. The fast time-varying channel is first modeled as a typical Gaussian-Markov process. Under the constraint of state-spaces, the pilot optimization problem is further formulated as constrained Markov decision processes (MDPs). Secondly, for achieving compatibility with existing standards, our goal is to determine the optimal pilot placement and employ only very limited pilot patterns in response to fast varying channels. We develop a channel state matched pilot optimization method, where the optimization procedures focus on how to respond the different channel variations in the time and frequency domains. To jointly evaluate the severity of channel variations in the time and frequency domains, we derive an effective mutual information measurement criterion. Simulation and numerical results show the efficiency of the pilot optimization decision scheme in reducing the channel estimation error, and mutual information measurement can yield an accurate performance evaluation in relatively fast time-varying vehicular communication scenarios.

The intra-tier interference in heterogeneous networks becomes more serious when the number of macro-femto cells dramatically increases. Based on the singular value decomposition (SVD) algorithm, this paper proposes an optimized interference alignment (IA) algorithm to improve the macro-femto cell downlink rate. To achieve the largest degrees of freedom (DOF), we adopt the precoding matrix as a powerful tool to mitigate the effect of inter-channel interference (ICI). In comparison with the conventional interference coordination methods, the SVD algorithm is capable of adjusting the coefficients of the precoding matrix with lower complexity. Furthermore, zero-forcing algorithm is also used to combat ICI in this proposed IA algorithm. The numerical results show that ICI can be suppressed effectively and the system performance in terms of throughput and signal-to-interference- plus-noise ratio (SINR) can be improved considerably.

This article proposes a framework of handover decision in LTE networks under high-speed mobility scenario,which is expected to support the subject's mobility at a speed of up to 500 km/h. In order to improve the handover performance in high-speed railway, a Location-Based Handover Algorithm (LBHA) is researched in this paper. The basic idea is that the vehicle speed sensor (VSS) calculates the train's distance to eNodeB (evolved Node B) by sensing its real-time velocity, and then reports the distance information to the source eNodeB periodically. Based on the location information from the train, the source eNodeB will estimate the train's relative distance to eNodeB at next reporting time, which will be further used to make handover decision in time. For better handover performance, the mobile station's location information as well as the receive signal strength has been collected to aid the handover decision. Finally, some numerical results are shown to demonstrate the effectiveness of the proposed handover decision algorithm. Compared with the traditional handover algorithm, LBHA adds 15% probability of successful handover to the traditional one, and reduces 10% probability of unnecessary handover. Furthermore, this algorithm is easy to be carried out.

CBTC (Communication Based Train Control) will be based on mobile communication, which the key problem is how to support reliability of control data transmission to improve track utilization and enhance train safety. This paper proposed a kind of real-time transmission model for high speed train end-to-end control data operation, which based on circuit switched data service by traditional wireless network access to transmit simultaneous instructions between the train operation control system and locomotive. In order to support this end-to-end real-time data application, we have studied a circuit domain latency model and improved a Simple Tunnel Protocol technology to accelerate data service which is easy to be implemented in the network. We have analyzed the performance under different service pressure by using MMPP (Markov Modulation Poisson Process) to describe service traffic pattern and employed a priority scheduling algorithm to estimate approximate service latency. The system simulation showed that data transmission latency should be reduced in busy time by the way of service priority scheduling. Although it might introduce extra traffic expenses and require reservation certain protection circuit, the average standby time probability could be cut down significantly.