We consider a homogeneous cellular network where a multi-antenna base station (BS) in each cell transmits messages to its intended users over a common frequency band. To improve the system capacity of this multi-cell multi-input single-output (MISO) interference channel, one of the state-of-the-art algorithms, namely, downlink-beamforming coordination, allows all BSs to cooperate with one another to mitigate inter-cell interference. However, most existing algorithms are suboptimal and impractical in a dynamic wireless environment, due to the high computational complexity and the overhead involved in collecting global channel state information (CSI). In this study, we exploit deep reinforcement learning (DRL) and propose a distributed dynamic downlink-beamforming coordination (DDBC) method with partial observability of the CSI. Each BS is able to train its own deep Q-network and employs appropriate beamformers depending on its environment, which is observed through a designed limited-information exchange protocol. The simulation results show that the proposed DRL-based DDBC method, with a considerably lower system overhead, achieves a system capacity that is very close to that of the fractional programming algorithm with global and instantaneous CSI measurements. In addition, this work demonstrates the potential of utilizing DRL to solve DDBC problems in a more practical manner.

In this paper, we propose a reconfigurable intelligent surface (RIS) enhanced spectrum sensing system, in which the primary transmitter is equipped with a single antenna, the secondary transmitter is equipped with multiple antennas, and the RIS is employed to reduce the required signal samples while realizing a high detection probability. Without loss of generality, we adopt the maximum eigenvalue detection approach, and propose a corresponding analytical framework based on random matrix theory, to evaluate the detection probability in the asymptotic regime. Besides, the RIS is configured with only the statistical channel state information to avoid realtime channel estimation. With the statistical configuration, the asymptotic distributions of the equivalent channel gains are derived. Then, we provide the theoretical predictions about the number of reflecting elements required to achieve a detection probability close to 1. Finally, we present the Monte-Carlo simulation results to evaluate the accuracy of the proposed asymptotic analytical framework for the detection probability and the validity of the theoretical predictions about the number of REs required to achieve a detection probability close to 1. Moreover, the simulation results show that the proposed RIS-enhanced spectrum sensing system can substantially improve the detection performance.

Cooperative spectrum sensing (CSS) is a key enabling technology of cognitive radio networks with multiple secondary users (SUs). In conventional CSS systems, when the primary signals are weak, the SUs require long sensing time to achieve a high detection probability for protecting the transmission of the primary user (PU), leading to little remaining time for secondary transmissions. To address this issue, we propose a reconfigurable intelligent surface (RIS) assisted CSS system, where multiple RISs are employed to improve the CSS performance within limited sensing time. Considering that the dependency of the CSS performance on the received primary signal strengths at the SUs differs across various CSS schemes, the RIS configurations could also be optimized differently regarding these CSS schemes. Motivated by this, we investigate the phase shift matrix (PSM) optimization problems to maximize the cooperative detection probability given a maximum tolerable false alarm probability, and we consider two typical kinds of CSS schemes, namely, data fusion and decision fusion. As it is intractable to directly solve these problems due to the complex expressions of the cooperative detection probability with respect to the PSMs, we show that the solutions can be obtained by transforming these problems into channel gain-related optimization problems. Furthermore, we show that the proposed PSM optimization methods can be extended to the more practical scenarios where instantaneous channel state information (CSI) is unavailable. In such cases, we leverage statistical CSI to improve the CSS performance in the sense of expectation. Subsequently, we conduct a numerical analysis on the number of reflecting elements required to achieve a target detection probability in the statistical CSI case. Finally, simulation results demonstrate that the proposed PSM optimization methods can significantly improve the CSS performance within limited sensing time.

Massive multiple-input multiple-output (MIMO) is a key enabling technology for next-generation communication systems. In massive MIMO cellular networks, coordinated beamforming (CBF), which jointly designs the beamformers of multiple base stations (BSs), is an efficient method to enhance the network performance. In this paper, we investigate the sum rate maximization problem in a massive MIMO mobile cellular network, where in each cell a multi-antenna BS serves multiple mobile users simultaneously via downlink beamforming. Although existing optimization-based CBF algorithms can provide near-optimal solutions, they require real-time and global channel state information (CSI), in addition to their high computation complexity. Due to the non-negligible delay of practical backhaul networks and the high-complexity optimization process, it is almost impossible to apply them in mobile cellular networks. Noting that the considered problem under the practical constraints can be modeled as a networked distributed partially observable Markov decision process, we propose a deep reinforcement learning-based distributed dynamic coordinated beamforming (DDCBF) scheme, which enables each BS to determine the beamformers with only local CSI and some historical information from other BSs. Besides, the beamformers can be calculated with a considerably lower computational complexity by exploiting neural networks and expert knowledge, i.e., a solution structure observed from the iterative procedure of the centralized optimization algorithms. Moreover, we provide extensive numerical simulations to validate the effectiveness of the proposed DRL-based approach. With lower computational complexity and less required information, the results show that the proposed approach can achieve comparable performance to the centralized iterative optimization algorithms.

In space-air-ground integrated networks (SAGINs), cognitive spectrum sharing has been regarded as a promising solution to meet the rapidly increasing spectrum demand of various applications, because it can significantly improve the spectrum efficiency by enabling a secondary network to access the spectrum of a primary network. However, different networks in SAGIN may have different quality of service (QoS) requirements, which can not be well satisfied with the traditional cognitive spectrum sharing architecture. To address this issue, in this paper, we propose a hierarchical cognitive spectrum sharing architecture (HCSSA) for SAGINs, where the secondary networks are divided into a preferential one and an ordinary one. Specifically, the aerial and terrestrial networks can access the spectrum of the satellite network under the condition that the caused interference to the satellite terminal is below a certain threshold. Besides, considering that the aerial network has a higher priority than the terrestrial network, we aim to use a rate constraint to ensure the performance of the aerial network. Subject to these two constraints, we consider a sum-rate maximization for the terrestrial network by jointly optimizing the transmit beamforming vectors of the aerial and terrestrial base stations. To solve this non-convex problem, we propose a penalty-based iterative beamforming (PIBF) scheme that uses the penalty method and the successive convex approximation technique. Moreover, we also develop three low-complexity schemes, where the beamforming vectors are obtained by optimizing the normalized beamforming vectors and power control. In addition, we consider the case where only statistical channel state information is available and the case where channel estimation errors exist, and propose the corresponding beamforming schemes. Finally, we provide extensive numerical simulations to evaluate the performance of the proposed beamforming schemes and demonstrate the advantages of the proposed HCSSA compared with the traditional cognitive spectrum sharing architecture.

Due to the characteristics of global coverage, on-demand access, and large capacity, the low earth orbit (LEO) satellite communication (SatCom) has become one promising technology to support the Internet-of-Things (IoT). However, due to the scarcity of satellite spectrum and the high cost of designing satellites, it is difficult to launch a dedicated satellite for IoT communications. To facilitate IoT communications over LEO SatCom, in this paper, we propose the cognitive LEO satellite system, where the IoT users act as the secondary user to access the legacy LEO satellites and cognitively use the spectrum of the legacy LEO users. Due to the flexibility of code division multiple access (CDMA) in multiple access and the wide use of CDMA in LEO SatCom, we apply CDMA to support cognitive satellite IoT communications. For the cognitive LEO satellite system, we are interested in the achievable rate analysis and resource allocation. Specifically, considering the randomness of spreading codes, we use the random matrix theory to analyze the asymptotic signal-to-interference-plus-noise ratios (SINRs) and accordingly obtain the achievable rates for both legacy and IoT systems. The power of the legacy and IoT transmissions at the receiver are jointly allocated to maximize the sum rate of the IoT transmission subject to the legacy satellite system performance requirement and the maximum received power constraints. We prove that the sum rate of the IoT users is quasi-concave over the satellite terminal receive power, based on which the optimal receive powers for these two systems are derived. Finally, the resource allocation scheme proposed in this paper has been verified by extensive simulations.