Channel estimation plays a critical role in the system performance of wireless networks. In addition, deep learning has demonstrated significant improvements in enhancing the communication reliability and reducing the computational complexity of 5G-and-beyond networks. Even though least squares (LS) estimation is popularly used to obtain channel estimates due to its low cost without any prior statistical information regarding the channel, this method has relatively high estimation error. This paper proposes a new channel estimation architecture with the assistance of deep learning in order to improve the channel estimation obtained by the LS approach. Our goal is achieved by utilizing a MIMO (multiple-input multiple-output) system with a multi-path channel profile for simulations in 5G-and-beyond networks under the level of mobility expressed by the Doppler effects. The system model is constructed for an arbitrary number of transceiver antennas, while the machine learning module is generalized in the sense that an arbitrary neural network architecture can be exploited. Numerical results demonstrate the superiority of the proposed deep learning-based channel estimation framework over the other traditional channel estimation methods popularly used in previous works. In addition, bidirectional long short-term memory offers the best channel estimation quality and the lowest bit error ratio among the considered artificial neural network architectures.

The Internet of Things (IoT) and massive IoT systems are key to sixth-generation (6G) networks due to dense connectivity, ultrareliability, low latency, and high throughput. Artificial intelligence, including deep learning and machine learning, offers solutions for optimizing and deploying cutting-edge technologies for future radio communications. However, these techniques are vulnerable to adversarial attacks, leading to degraded performance and erroneous predictions, outcomes unacceptable for ubiquitous networks. This survey extensively addresses adversarial attacks and defense methods in 6G network-assisted IoT systems. The theoretical background and up-to-date research on adversarial attacks and defenses are discussed. Furthermore, we provide Monte Carlo simulations to validate the effectiveness of adversarial attacks compared to jamming attacks. Additionally, we examine the vulnerability of 6G IoT systems by demonstrating attack strategies applicable to key technologies, including reconfigurable intelligent surfaces, massive multiple-input-multiple-output (MIMO)/cell-free massive MIMO, satellites, the metaverse, and semantic communications. Finally, we outline the challenges and future developments associated with adversarial attacks and defenses in 6G IoT systems.

In this paper, we consider the problem of power control and load balancing in the uplink of the cell-free (CF) multi-user (MU) massive multiple-input multiple-output system. The power control problem is solved using three different criteria: 1) power minimization; 2) maximize min quality of service (QoS); and 3) maximize sum spectral efficiency (SE) under imperfect channel state information. While power minimization and min-QoS maximization problems can be solved in polynomial time, sum SE maximization is NP-hard. Hence, we apply a successive approach to convert it into a geometric program and achieve near optimum solution. As the number of connections in CF-MU system is large, we minimize the number of base stations (BSs) serving each user while maintaining the optimized power consumption and QoS from the previous stage. We propose an iterative elimination (IE) algorithm to remove ineffective BSs for each user. The system analysis is performed under two common BS receivers: maximum ratio combining and zero forcing. The simulation shows that our method is better than both maximum signal-to-noise ratio association and full-set joint transmission, especially in the high QoS regime. Another notable result is that the selection of the linear receiver becomes more influential than the association method.

This paper describes a nonorthogonal multiple access (NOMA)-based device-to-device (D2D) communication system underlaying in a cellular network sharing time and frequency resource. To optimize the total transmit power for all users in the network, we investigate a comprehensive resource allocation solution consisting of joint subchannel assignments, user pairings, and power control. The optimization problem is a mixed integer linear program, which is solved in three low-complexity steps: We first group D2D users to minimize mutual interference from cellular users (CU). Second, we demonstrate a heuristic solution for coupling D2D users in each group. Finally, we minimize the total transmit power of all devices in the network while maintaining the SINR requirements for both CU and D2D users. The optimal solution is derived off-line using Rayleigh fading channels for any given QoS level of CU and NOMA-D2D users.We recognize that both subchannel assignment, and user pairings play the important roles in reducing the total transmit power of the network in which both cellular and D2D users coexist. However, optimization of the subchannel assignment contributes more towards minimizing the total network transmit power than the proposed model of optimizing user pairings.

In this paper, we propose a unmanned aerial vehicle (UAV)-assisted multi-hop edge computing (UAV-assisted MEC) system in which a UE can offload its task to multiple UAVs in a multi-hop fashion. In particular, the UE offloads a task to its nearby UAV, and this UAV can execute a part of the received task and offload the remaining part to its neighboring UAV. The offloading process continues until the task execution is finished. The benefit of this multihop offloading is that the task execution can be finished faster, and the computing load can be shared among multiple UAVs, thus avoiding overloading and congestion. Each node, i.e., the UE or the UAV, needs to determine the task size for offloading to minimize the cumulative energy consumption and latency over the nodes. We formulate a stochastic optimization problem under the dynamics and uncertainty of the UAV-assisted MEC system. Then, we propose a deep reinforcement learning (DRL) algorithm to solve this problem. Simulation results are provided to demonstrate the effectiveness of the DRL algorithm.

This paper proposes a flexible pilot assignment method to jointly optimize the uplink and downlink data transmission in multi-cell Massive multiple input multiple output (MIMO) systems with correlated Rayleigh fading channels. By utilizing a closed-form expression of the ergodic spectral efficiency (SE) achieved with maximum ratio processing, we formulate an optimization problem for maximizing the minimum weighted sum of the uplink and downlink SEs subject to the transmit powers and pilot assignment sets. This combinatiorial optimization problem is solved by two sequential algorithms: a heuristic pilot assignment is first proposed to obtain a good pilot reuse set and the data power control is then implemented. Numerical results manifest that the proposed algorithm converges fast to a better minimum sum SE per user than the algorithms in previous works.