This paper proposes a robust decentralized beam-forming (BF) scheme for downlink non-orthogonal multiple access (NOMA) transmission in an integrated satellite and aerial network (ISAN) to reduce both power consumption and signaling overhead. By employing the imperfect channel state information (CSI) and the imperfect successive interference cancellation (SIC), we formulate an optimization problem to minimize the total transmit power, subject to the rate requirements of both satellite and aerial terminals, and the transmit power budget of satellite and aerial platforms. To address this complex problem, we adopt S-procedure to transform the nonconvex constraints into convex ones and then propose a decentralized BF algorithm using Lagrange duality to obtain the satisfactory solutions in an efficient way. Finally, simulation results demonstrate that our proposed scheme can achieve a similar performance but at a lower signaling overhead as compared with the centralized BF method, and confirm the superiority of the proposed scheme in terms of power consumption over other existing works.

In this paper, satellite-terrestrial assisted multi-tier computing networks (STMTCN) are proposed to satisfy the growing computation demands of user terminals (UTs) in next generation wireless networks. In the STMTCN, UT’s computation task can be processed at different computing entities and a multi-tier computation model named computing depth is proposed to better reflect the multi-tier computing process. Then, we formulate a weighted sum energy consumption minimization problem via jointly optimizing UT-satellite association, computing depth, multiple-input multiple-out (MIMO) precoding, and computation resource allocation. The non-convex optimization problem is decomposed into four subproblems, each of which is solved iteratively. Specifically, the UT-satellite association subproblem is solved by quadratic transform based fractional programming and Lagrangian dual method and a closed-form expression is obtained. The computing depth for local tier and the satellite tier is solved respectively with first-order Taylor expansion. Then, MIMO precoding subproblem for UT and satellite offloading is solved by quadratic transform and interior point method (IPM). Finally, the computation resource allocation for UT and satellite is obtained in a closed-form expression and the GW computation resource allocation is solved by using IPM. Simulation results show that the proposed STMTCN and algorithms can fulfill the UT’s computing demands with low energy consumption.

This paper proposes a downlink transmission scheme for intelligent reflecting surface (IRS) enhanced cognitive-satellite-aerial-network to support massive access of Internet-of-Things devices (IoTDs). By sharing the same frequency band with satellite network, the aerial network offers services for IoTDs having line-of-sight links through space division multiple access, and for IoTDs locating in blocked area via IRS-enhanced non-orthogonal multiple access. Assuming that only the imperfect channel state information is available, we formulate a transmit power minimization problem subject to the probabilistic constraints of the quality-of-service requirements for IoTDs, the co-channel interference power limitation, and unit-modulus requirement for IRS. To tackle this mathematically intractable problem, we propose a generalized zero-forcing based low-complexity robust transmission algorithm, integrating the second-order Taylor expansion and Bernstein-type inequality, to obtain a satisfactory performance while reducing the computational load. Finally, simulation results validate the effectiveness and superiority of the proposed robust algorithms compared to existing algorithms.

Unmanned aerial vehicles (UAVs) have improved the capacity and coverage of wireless networks. Mobile edge computing (MEC) has provided substantial computation capability to user equipment (UEs). The integration of UAV and MEC can take advantages of both to provide flexible computation service. In UAV assisted MEC networks, delay and energy consumption are two main concerns, which are conflicting to a certain extent. This paper investigates delay and energy consumption jointly in a multiple UAVs assisted MEC network. A cost function is defined to balance the delay and the energy consumption. The user access, task offloading, and computational resource allocation are jointly considered to minimize the long-term cost. To tackle this difficult problem, we formulate the long-term problem into sequential decision problem and treat all UEs as intelligent agents. Each UE decides its access UAV, task offloading proportion, and required edge computation resource to minimize the its own cost. Moreover, the optimal task offloading proportion and required computation resource can be obtained in closed-form given user access so that the action space can be significantly reduced. Then, an adversarial multi-armed bandit based algorithm is employed at each UE and a distributed scheme is proposed to solve the joint optimization problem. Simulation results validate the effectiveness and robustness of the distributed scheme and show its superiority to benchmarks.

In this work, we aim to minimize the deployment cost of active simultaneously transmitting and reflecting RISs (STAR-RISs) with the constraints of users’ communication quality requirements. To address this problem, we decouple the optimization problem into a transmission optimization subproblem and a deployment optimization subproblem. The transmission scheme is obtained by leveraging fractional programming (FP). In addition, we propose two approaches to efficiently obtain the deployment scheme of active STAR-RIS, namely a penalty-majorization-minimization (MM) method and a heuristic binary search method. Simulation results validated the effectiveness of the proposed algorithm in terms of deployment cost.

Cognitive satellite and terrestrial network (CSTN) is considered as a promising technology to provide ubiquitous connectivity for various users within wide-coverage. This paper proposes a robust downlink transmission scheme for multiple intelligent reflecting surfaces (IRSs) assisted CSTN. Here, the satellite network adopts multigroup multicast transmission scheme to serve many earth stations, while the terrestrial network exploits space division multiple access and multi-IRS-enhanced non-orthogonal multiple access technology to communicate with many terrestrial users. By assuming that these two networks share the same frequency band having only the angular information based imperfect channel state information of each user, we formulate an optimization problem to minimize the total transmit power subject to the constraints of quality-of-service requirement for each user, per-antenna transmit power budgets of satellite and BS, and unit-modulus requirement for each reflecting element. To tackle this mathematically intractable problem, we then employ angular discretization together with the successive convex approximation method to obtain the active beamforming (BF) vectors of satellite and BS, the passive BF vector of IRS, and the power allocation coefficients. Moreover, we propose a generalized zero forcing BF and alternative optimization to obtain the suboptimal solutions of the optimization problem with low computational complexity. Finally, simulation results are given to demonstrate the effectiveness and superiority of the proposed two schemes over the benchmarks.

A dense satellite-terrestrial integrated mobile-edge computing network (SATIMECN) architecture is developed to meet the computing demands for next generation networks. We formulate an average weighted sum energy consumption minimization problem by jointly considering task ratio allocation of computing or offloading at local and the gateway (GW), ground user terminal (GUT)-satellite association relation, GUT multiple-input and multiple-output (MIMO) precoding, and computation resource allocation at local and the GW. Due to the stochastic property of the optimization problem, we adopt Lyapunov optimization theory to transform it into a deterministic one. Then, we decompose the optimization problem into four subproblems and solve each one iteratively. Specifically, task ratio allocation of computing or offloading at local and the GW is obtained in a closed-form expression using the delay constraint. Then, the binary GUT-satellite association subproblem is solved by the weighted minimum mean-squared error and quadratic transform based fractional programming (QTFP) methods. Moreover, the MIMO precoding subproblem is solved by QTFP and interior point methods. Finally, the computation resource allocation subproblem for local and edge computing is derived in closed-form expressions. Simulation results demonstrate that the tradeoff between the average weighted sum energy consumption and the average queue length can be realized by adjusting the Lyapunov control parameter. Moreover, the proposed MIMO communication and frequency reuse schemes for dense satellite network can realize efficient computation offloading with relative low cost.

In this paper, we propose a joint beamforming (BF) and power allocation scheme for non-orthogonal multiple access based multibeam satellite systems. Unlike the existing works where perfect channel state information (CSI) is required, we use imperfect CSI in formulating a constrained optimization problem aiming to minimize the maximum individual antenna powers subject to the outage constraints of quality-of-service requirements. Since the original problem is mathematically intractable, we first adopt Bernstein-Type II inequality to convert the outage constraints into deterministic forms. Then, an alternating optimization algorithm is proposed to jointly design BF vectors and power allocation coefficients. Finally, simulation results are provided to demonstrate the robustness and superiority of the proposed scheme compared with benchmark schemes.

Hybrid architecture is a promising candidate precoding scheme to balance the achievable spectral efficiency and power consumption in millimeter wave (mmWave) multiple input multiple output (MIMO) systems. A practical partially dynamic subarray-connected architecture is developed to improve the transmission performance. In this proposed architecture, the set of antennas in each subarray is fixed, but the subarrays connected to each radio frequency chain are dynamic. Moreover, we study how to optimize jointly the partially dynamic subarray structure and the hybrid precoders under the constraints of total transmit power and hardware limitation. This joint optimization problem is divided into two sub-problems. For the first sub-problem, a low complexity algorithm is proposed to determine the partition of subarrays using the long-term spatial channel covariance. Then, the penalty decomposition method is adopted to design the hybrid precoders. Numerical results verify that the partially dynamic subarray design algorithm offers one or two orders of computation time saving compared with the existing algorithms. Moreover, the proposed structure achieves spectral efficiency gain using less hardware, compared with the fully dynamic subarray structure adopted in the existing algorithms.

Hybrid architecture is a promising candidate precoding scheme to balance the achievable spectral efficiency and power consumption in millimeter wave multiple input multiple output systems. A practical partially dynamic subarray-connected architecture is developed to improve the transmission performance. In this proposed architecture, the set of antennas in each subarray is fixed, but the subarrays connected to each radio frequency chain are dynamic. Moreover, we study how to optimize jointly the partially dynamic subarray structure and the hybrid precoders under the constraints of total transmit power and hardware limitation. This joint optimization problem is divided into two sub-problems. For the first sub-problem, a low-complexity algorithm is proposed to determine the partition of subarrays using the long-term spatial channel covariance. Then, the penalty decomposition method is adopted to design the hybrid precoders. Numerical results verify that the partially dynamic subarray design algorithm offers one or two orders of computation time saving compared with the existing algorithms, and the hybrid precoding algorithm outperforms the existing algorithms in terms of spectral efficiency. Moreover, compared with the fully dynamic subarray structure adopted in the existing algorithms, the proposed structure achieves spectral efficiency gain and energy efficiency gain using less hardware.

Mobile edge computing (MEC) is considered as an efficient method to relieve the computation burden of mobile devices. In order to reduce the energy consumption and time delay of mobile devices (MDs) in MEC, multiple users multiple input and multiple output (MU-MIMO) communications is considered to be applied to the MEC system. The purpose of this paper is to minimize the weighted sum of energy consumption and time delay of MDs by jointly considering the offloading decision and MU-MIMO beamforming problems. And the resulting optimization problem is a mixed-integer non- linear programming problem, which is NP-hard. To solve the optimization problem, a semidefinite relaxation based algorithm is proposed to solve the offloading decision problem. Then, the MU-MIMO beamforming design problem is handled with a newly proposed fractional programming method. Simulation results show that the proposed algorithms can effectively reduce the energy consumption and time delay of the computation offloading.

With the development of Internet of Things, the mobile edge computing has become a promising technology for real-time communications. This paper investigates a mobile edge computing system that consists of an access point integrated with a mobile edge computing server, multiple mobile stations, and a malicious eavesdropper. By offloading part of the computing tasks to the mobile edge computing server, the energy consumption of mobile stations can be reduced significantly and the lifetime is prolonged as well. Moreover, the physical layer security is an effective technique to guarantee the secure transmission of the offloading data. Based on the proposed system model, we formulate an optimization problem to minimize the energy consumption of the system by jointly optimizing the allocations of local computing tasks, local central processor's frequency, offloading power, and offloading timeslots. A difference of convex algorithm based scheme is proposed to solve the problem. The performance of the proposed scheme is superior to the benchmark schemes, which is demonstrated by simulation results.

High-speed trains are being widely deployed around the world. To meet the high data rate transmission requirements, millimeter wave high-speed train communication systems with large antenna arrays have drawn increasingly attentions. Since channel conditions vary rapidly in high-speed train communication scenarios, frequent channel estimation is required. Moreover, due to the limit period of each transmission time interval, the key challenge in channel estimation is to design an efficient beam searching scheme to allow more time for data transmission. This paper formulates the beam searching problem into a multi-armed bandit problem, and proposes a bandit inspired beam searching scheme to reduce the number of measurements. The performance of the proposed scheme is evaluated in terms of regret, and simulation results show that the proposed scheme can approach the theocratical limit quickly.

This paper considers the problem of transmission scheduling of delay-sensitive data over a point-to-point correlated Rayleigh fading channel with channel estimation errors. According to the imperfect channel state information (CSI) and the buffer state, the transmit power and the modulation and coding scheme (MCS) are determined to jointly maximize the energy efficiency, and minimize transmission delay and overflow probability. To account of the effects of the channel estimation errors, the CSI imperfection is modeled as uncertain sets using the ellipsoidal approximation. Then the joint optimization problem is formulated using the weighted sum method. Using the idea of online learning, two algorithms are proposed to schedule the delay-sensitive data for the situations with and without the uncertainty bound of channel estimation, respectively. The numerical results indicate that the proposed online learning based scheduling algorithms can tackle the imperfect CSI issue and improve the system performance in terms of the energy efficiency, transmission delay and overflow probability. Moreover, the convergence times are very short, which highlights the feasibility of the proposed online learning based scheduling for practical systems.

Distributed antenna systems (DAS) is a promising approach to cope with challenges of next generation mobile communications. This paper studies the downlink ergodic capacity of a (N, K), (K ≤ N), virtual cell based DAS, in which each mobile station (MS) selects the N closest antenna ports (APs) to form its virtual cell and the K closest APs will serve the target MS cooperatively with a total transmit power constraint. Using the stochastic geometry, the locations of APs and MSs are modeled as two independent Poisson point processes, respectively. Then, a computationally tractable integral expression is derived for the downlink ergodic capacity of the (N, K) virtual cell based DAS. Numerical results indicate that the cooperation among multiple APs within each virtual cell can improve the downlink ergodic capacity significantly.