Low Earth Orbit (LEO) constellations and Unmanned Aerial Vehicle (UAV) networks enable wide coverage for the sixth generation (6 G) mobile communication. However, it is a challenge to achieve high scheduling success rate, ultra-low latency, and efficient load balance in the Space-Air-Ground Integrated 6 G Network (SAGGIN). This paper addresses the following issue:How to effectively and orderly transmit time-sensitive traffic in SAGGIN under strict deadlines, limited computational ability, and restrained link capacity?Specifically, this paper uses Software-Defined Networking (SDN) and designs a joint optimization method to enhance the traffic transmission ability of SAGGIN. Considering response time, computing cost, and link capacity in SAGGIN, the scheduling, computing, and load balance issues are modeled as a multi-objective optimization problem that minimizes the worst-case response time and computing cost of data frames while maximizing the network flow. Then, this paper leverages a Federated Reinforcement Learning (FRL) scheme to solve the problem. Results show that the FRL could achieve great scheduling, computing, and load balance performance. Specifically, our method can successfully schedule 80% of the traffic at most when the current network load is around 90%. Furthermore, the computational delay could reduce around 50%.

Intelligent reflecting surface (IRS) is a promising technology for the 6G networks and attracts much attention. However, existing research seldom considers its energy demand. In this paper, we study an IRS assisted multiple-input single-output downlink broadcasting system where there exist one access point (AP), multiple users and a wireless powered IRS. We focus on the broadcasting data transmission which includes two phases. In the first phase, the AP transmits broadcasting data to users and energy signals for IRS energy harvesting (EH). In the second phase, the AP broadcasts messages with the assistance of the IRS. We aim at maximizing the transmission throughput by designing the phase duration scheduling, the transmit beamforming at the AP in each phase, the energy signal covariance matrix and the IRS reflect beamforming with discrete phase shifts. We first propose a semidefinite relaxation (SDR) based transmission design by also employing one-dimensional line search and further proposing a randomization process. Then, a low complexity transmission design has been further developed. Simulation results demonstrate that the SDR based design can almost achieve the optimal and the low complexity design can perform close to the SDR based design with much lower complexity.

Semantic communication is a newly emerged communication paradigm that exploits deep learning (DL) models to realize communication processes like source coding and channel coding. Recent advances have demonstrated that DL-based joint source-channel coding (DeepJSCC) can achieve exciting data compression and noise-resiliency performances for wireless image transmission tasks, especially in environments with low channel signal-to-noises (SNRs). However, existing DeepJSCC-based semantic communication frameworks still cannot achieve adaptive code rates for different channel SNRs and image contents, which reduces its flexibility and bandwidth efficiency. In this paper, we propose a predictive and adaptive deep coding (PADC) framework for realizing flexible code rate optimization with a given target transmission quality requirement. PADC is realized by a variable code length enabled DeepJSCC (DeepJSCC-V) model for realizing flexible code length adjustment, an Oracle Network (OraNet) model for predicting peak-signal-to-noise (PSNR) value for an image transmission task according to its contents, channel signal to noise ratio (SNR) and the compression ratio (CR) value, and a CR optimizer aims at finding the minimal data-level or instance-level CR with a PSNR quality constraint. By using the above three modules, PADC can transmit the image data with minimal CR, which greatly increases bandwidth efficiency. Simulation results demonstrate that the proposed DeepJSCC-V model can achieve similar PSNR performances compared with the state-of-the-art Attention-based DeepJSCC (ADJSCC) model, and the proposed OraNet model is able to predict high-quality PSNR values with an average error lower than 0.5dB. Results also demonstrate that the proposed PADC can use nearly minimal bandwidth consumption for wireless image transmission tasks with different channel SNR and image contents, at the same time guaranteeing the PSNR constraint for each image data.

Sparse channel estimation for massive multiple-input multiple-output systems has drawn much attention in recent years. The required pilots are substantially reduced when the sparse channel state vectors can be reconstructed from a few numbers of measurements. A popular approach for sparse reconstruction is to solve the least-squares problem with a convex regularization. However, the convex regularizer is either too loose to force sparsity or lead to biased estimation. In this paper, the sparse channel reconstruction is solved by minimizing the least-squares objective with a nonconvex regularizer, which can exactly express the sparsity constraint and avoid introducing serious bias in the solution. A novel algorithm is proposed for solving the resulting nonconvex optimization via the difference of convex functions programming and the gradient projection descent. Simulation results show that the proposed algorithm is fast and accurate, and it outperforms the existing sparse recovery algorithms in terms of reconstruction errors.

Simultaneous wireless information and power transfer (SWIPT) is a promising technique to address the energy scarcity problem in heterogeneous cellular networks (HCNs). Since information security is a critical issue in the SWIPT HCNs, we consider the secrecy beamforming design for a multiple-input single-output HCN with one macrocell base station (BS) and multiple femtocell BSs. In the considered system, all BSs send confidential messages to information receivers and energy signals to energy receivers (ERs). The ERs have the potential to wiretap the confidential messages. Consequently, by taking fairness into account, we propose a sum logarithmic secrecy rates maximization beamforming design problem under the energy harvesting constraints for ERs. The formulated design problem is nontrivial to solve due to the nonconvexity in the objective and the constraints. To tackle the design problem, a semidefinite relaxation and successive convex approximation-based centralized beamforming design algorithm is proposed. Our analysis reveals that using single-stream beamforming to transmit confidential messages does not cause any loss of optimality. Moreover, an alternating direction method of multipliers-based distributed beamforming design is also proposed. Simulation results demonstrate the effectiveness of the proposed algorithms.

Robust quality-of-service (QoS)-based and secrecy rate-based secure transmission designs are investigated for a multiple-input single-output system with multiple eavesdroppers and a cooperative jammer. Two scenarios are considered: (a) eavesdroppers' channel state information (ECSI) is available and (b) ECSI is unavailable. In scenario (a), a QoS-based design is considered to minimize the worst case signal-to-interference-and-noise ratio at the eavesdroppers and to guarantee the QoS of the legitimate receiver. A secrecy rate-based design is also studied where the worst case secrecy rate is maximized. In scenario (b), a QoS-based design is considered to maximize the power of jamming signals under the QoS constraint of the legitimate receiver, and the secrecy rate-based design is not applicable. In all these designs, we jointly optimize the transmit beamforming vector and the covariance matrix of jamming signals under individual power constraints. As the optimization problems are non-convex, we propose an algorithm for each problem through semidefinite relaxation. Our analysis and simulation results show that, even though the linear precoding scheme in our designs is transmit beamforming rather than the general rank transmit covariance, this does not cause any loss of optimality.

A robust quality-of-service (QoS) based secure transmission design is investigated for a cooperative jamming aided multiple-input single- output system with unknown eavesdroppers. By assuming legitimate receiver's channel state information (CSI) is imperfect and eavesdroppers' CSI is unavailable, our system design goal is to maximize the power of artificial jamming signals, and to guarantee a minimum QoS at the legitimate receiver. The transmit beamforming vector and covariance matrix of jamming signals are jointly optimized under individual power constraints. As this optimization problem is non-convex, we propose a solution method by using the semidefinite relaxation approach. Numerical results show that, our design, in which the precoding scheme of information transmission is transmit beamforming, outperforms its transmit covariance based counterpart.