SummaryPilot contamination (PC) is one of the most crucial problems in massive multiple‐input multiple‐output (MIMO) systems, due to a limited source of the coherence block. In the current study, two pilot assignment schemes proposed for massive MIMO uplink transmission. We divided each cell into two groups of edge and center users under distance‐based user grouping algorithm (DUG), which the aim is finding the best user grouping boundary. The best boundary is where both signal to interference plus noise Ratio (SINR) of edge and center users is maximum, according to the user's distance to base stations (BS). We investigated the location‐aware pilot assignment (LPA) scheme to enhance the quality of services for both groups. In LPA by assuming three pilot groups for adjacent cells and determining the user's distance to BS, the planned pilots assigned to the users according to their distance to the BS. Then, we optimized pilot schedules in each group by maximizing uplink (UL) rate of each user with auction‐based optimization pilot assignment (AOPA). Simulation results show that both DUG and LPA improve the rate of edge users about 70% and the rate of center users about 30%. Joint DUG and AOPA enhance the rate of the edge and center users about 100% and 80% in each group than conventional schemes and about 50% and 10%, compared with the LPA method. Simulation results show that the proposed method is efficient in reducing PC and improving spectral efficiency.

Interference cancellation is one of the important issues in Heterogeneous networks (HetNets), due to the density of the network. In this paper, we investigate the problem of interference cancellation and resource allocation with the Q-learning approach. We consider the Inter-interference and Intra-interference between the femtocell and macro cells in the uplink scenario. With the aim of maximizing the QoS of the macro user equipment (MUE) and minimizing the interference of the MUE, a new reward function was proposed. The simulation results show the improvement of the proposed algorithm in two strong interference and increasing distance scenarios.

Summary As Massive multiple‐input multiple‐output (Ma‐MIMO) is one of the critical 5G technologies, security aspects of this technology in terms of powerful distributive jamming attacks for improving the total system performance are so important. In this paper, we evaluate the sum spectral efficiency (SE) of Ma‐MIMO systems in the multicell scenario concerning jamming effects. First, the closed‐form expression for sum SE using two detectors maximum ratio combining (MRC) and zero‐forcing (ZF) in the presence of a jammer was obtained. Next, the effects of jamming power, the number of base station antennas, the signal‐to‐noise ratio (SNR), and coherence block lengths on the system performance were obtained. Finally, to improve the signal‐to‐noise and interference ratio (SINR) of cell edge users, the problem of optimizing the fairness trade‐off between the central legitimate users and the cell edge legitimate users is modeled by using log barrier functions. We propose an alternating algorithm to solve the non‐convex problem. Numerical results showed that by considering the same interference power and the power of the legitimate users, the sum SE improved with proposed schemes and also by increasing the number of antennas at BS. Also, the ZF outperforms MRC with increasing the number of antennas. Finally, it is shown that by using the optimal legitimate users instead of overall legitimate users, the system performance in terms of fairness sum SE trade‐off improved.