Low Earth orbit (LEO) satellite is one of the most promising infrastructures for realizing next-generation global wireless networks with enhanced data rates. Applying massive multiple-input multiple-output (mMIMO) to LEO satellite communication systems is a novel idea to enhance communication capacity and realize the global high-speed interconnection. However, obtaining effective instantaneous channel state information (iCSI) is challenging due to the time-varying propagation environment and long transmission delay. In this letter, a deep learning (DL)-based CSI prediction scheme is proposed to address channel aging problem by exploiting the correlation of changing channels. Specifically, we design a satellite channel predictor (SCP) that is composed by long short term with memory (LSTM) units. The predictor is first trained by offline learning and then feeds back the corresponding output results online based on the input data to realize channel feature extraction and future CSI prediction in LEO satellite scenarios. Numerical results demonstrate that the proposed DL-based predictor can mitigate the channel aging problem in LEO satellite mMIMO system effectively.

The substantial growth in mobile Internet and Internet of Things traffic has made system capacity enhancement the most important requirement for next-generation mobile communication systems. Massive multiple-input, multiple-output (mMIMO) is a key technology for fulfilling the traffic requirements because of its strong potential to boost spectral efficiency (SE). However, the uplink and downlink of mMIMO usually operate in time-division duplex (TDD) or frequency-division duplex (FDD), which results in an insufficient utilization of time-frequency resources.

Co-time co-frequency uplink and downlink (CCUD) transmission was considered challenging in the cellular system due to the strong self-interference (SI) between the transmitter and receiver of base station (BS). In this paper, by investigating the beam-domain representation of channels based on the basis expansion model, we propose a beam-domain full-duplex (BDFD) massive multiple-input multiple-output (MIMO) scheme to make the CCUD transmission possible. The key idea of the BDFD scheme lies in intelligently scheduling the uplink and downlink user equipment (UE) based on the beam-domain distributions of their associated channels to mitigate SI and enhance transmission efficiency. We show that the BDFD scheme achieves significant savings in uplink/downlink training resource and achieves uplink and downlink sum capacities simultaneously as the number of BS antennas approaches infinity. The superiority of the BDFD scheme over the traditional time-division duplex (TDD)/frequency-division duplex (FDD) massive MIMO is evaluated through simulation for the macrocell environment. The results show that the spectral efficiency gain can even exceed 2× in the specific scenarios, since the BDFD scheme utilizes the time-frequency resource more efficiently in both the training and data transmission phases.

In this paper, beam-domain (BD) anti-jamming transmission in a downlink massive multiple-input multiple-output (MIMO) system is investigated. A smart jammer with multiple antennas attempts to interfere with the signal reception of users with the desired energy efficiency (EE), whereas a base station (BS) tries to minimize the transmission cost while ensuring uninterrupted communication. A Bayesian Stackelberg game between the BS and jammer, where the jammer is the follower and the BS acts as the leader, is modeled. In the follower subgame, the optimal jamming precoding with a closed-form power solution is introduced. The optimal jamming power is proportional to the transmission power in the downlink, and thus, for the BS, the strategy of suppressing malicious attacks by increasing the transmission power fails. In the leader subgame, generalized zero-forcing (ZF), whose closed-form power solution constitutes the unique Stackelberg equilibrium (SE) with that of the jammer, is found to be the optimal anti-jamming precoding for robust transmission. The results show that there always exists a precoding solution for the BS that ensures reliable transmission when the SE is obtained. A proper increase in the minimum signal-to-interference-and-noise ratio (SINR) threshold or the BD channel approximation error helps the BS save power during the resistance against the jammer. Then, a simplified power solution without the instantaneous channel state information (CSI) of jamming channels is further introduced for practical implementation. Numerical results are provided to verify the proposed solutions.

In this paper, we investigate the interactions between a base station (BS) and a full-duplex (FD) active eavesdropper in the uplink of multi-cell massive multiple-input multiple-output (MIMO) systems. Focusing on the resistance against simultaneous eavesdropping and jamming, we introduce a beam extraction method based on spatial information to eliminate pilot contamination, from which a beam-domain (BD) distributed receiving scheme is proposed to suppress the jamming mixed in users’ signals. From a practical point of view, we use the perspective of a hierarchical game to analyze the interactions without the prior information of the eavesdropper. The results show that with the help of the proposed scheme, the performance of the FD-mode attack is inferior to that of passive eavesdropping. A statistical upper bound of eavesdropping is given in a closed-form, which is mainly determined by the direction and distance of the eavesdropper relative to users. Then, a joint power and combining matrix optimization algorithm is proposed to improve the secrecy capacity of the system.

Wireless communication for high-speed railways (HSRs) has received considerable attention in recent years. The fast time-varying and doubly selective fading properties of channels pose a substantial threat to the link performance. Meanwhile, the nonnegligible Doppler frequency offset (DFO) resulting from the high-speed motion induces intercarrier interference. In this paper, we propose an angle-domain channel tracking and hybrid beamforming scheme for HSR by investigating the time and spatial properties of channels. A large-scale uniform linear antenna array is applied at the base station to provide high spatial resolution. The DFO is compensated through beam alignment and capturing the precise angular information from signals. Then, the spatial beam gain is tracked during the angle-of-arrival time, which can be used to reconstruct the full-dimensional channel state information. Finally, the hybrid beamforming is used to realize data transmission. The key idea of tracking is, applying a linear Kalman filter to modify the time-varying beam gain, while the main idea of hybrid beamforming is utilizing nonorthogonal beams to compress the channel dimension and decrease the computational complexity. The superiority of the proposed scheme is evaluated through simulations.