Wireless communication technologies have profoundly impacted the interconnectivity of mobile users and terminals. Nevertheless, the exponential increase in the number of users poses significant challenges, particularly in interference management, which is a major concern in wireless communication. Machine learning (ML) approaches have emerged as powerful tools for solving various problems in this domain. However, existing studies have not fully addressed the problem of interference management for wireless communication using ML techniques. In this paper, we explore the application of recurrent neural network (RNN) approaches to address co-channel interference in wireless communication. Specifically, we investigate the effectiveness of long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and gated recurrent unit (GRU) network architectures in two different network settings. The first network comprises 10 connected devices, while the second network involves 20 devices. Our experimental results demonstrate that Bi-LSTM outperforms LSTM and GRU in terms of mean squared error, normalized mean squared error, and sum rate. While LSTM and GRU produce similar results, LSTM exhibits a marginal advantage over GRU. In addition, a combined RNN approach is also studied, and it can provide better results in dense networks.

We investigate the beamforming for cooperative non-orthogonal multiple access (NOMA) transmission through a decode-and-forward relay either with full-duplex (FD) mode or with half-duplex (HD) mode. The beamforming vectors at the source and at the relay are optimized to maximize the end-to-end sum rates of the NOMA transmission. The optimal beamforming for the second-hop of the NOMA transmission is present, from which the beamforming algorithms for the two relay modes are derived. For the FD case, the removal of self interference from the feedback loop is important and a two dimensional (2-D) search algorithm based on semi definite relaxation (SDR) is provided, where the 2-D search range is narrowed down as the algorithm iterates. For the HD mode, no iteration is necessary and only one implementation of SDR is required since the SI suppression is not needed. Simulation results show the performance advantage of the proposed FD mode system over that of HD mode in various parameter settings. This justifies the cost and complex hardware circuitry of FD mode.

We investigate the beamforming for the full-duplex self-energy recycling relay system with decode-and-forward protocol, where the relay received signal is divided into two circuits, one for the full-duplex relaying for the destination and the other for the energy harvesting through re-cycling of its RF energy. The beamforming vectors and the power splitting ratio at the relay are optimized to maximize the harvested energy while a certain reception quality threshold at the destination is met. Simulation results show how the performance of the proposed system is affected by various design factors such as numbers of relay antenna sets and analog interference cancellation level.

We propose signal-to-interference-plus-noise power ratio (SINR) maximizing rank one beamforming schemes for the full-duplex (FD) relay systems. The non-regenerative amplify and forward (AaF) relay is considered with multiple transmit antennas and multiple receive antennas. The residual selfinterference (SI) after analogue domain processing is to be suppressed by the proposed schemes so that the spectral efficiency of FD can be improved. We first analyze the correlation matrix of SI in a matrix power series and derive a convergent form of the correlation matrix so that the SINR expressions can be identified. Leveraging the structure of the SINR function on the beamformer (BF) vectors and power control parameter, two of the proposed schemes try to maximize SINR by optimizing a BF vector through iterative updates exploiting a bisection type search with the other BF vector being fixed. The third proposed scheme alternates over these two schemes to further enhance them by widening the search space that the BF vectors span. We compare the SINR of the proposed schemes with some reference schemes based on zero forcing approaches, maximum ratio reception, and maximum ratio transmission to characterize the performance improvement of the proposed schemes.

In this letter, we address the beamforming in full-duplex self-energy recycling relay system, where an amplify-and-forward relay delivers the source message to the destination in a single time slot while recycling a portion of its RF energy through energy harvesting. The signal received at the realy is divided into two circuits, one for the full-duplex relaying and the other for the energy harvesting. The beamforming vectors and the power division ratio at the relay are optimized to maximize the harvested energy while maintaining a certain reception quality threshold at the destination. Simulation results show various advantages and trade-offs of the proposed system.

Recently, as the demand for data rate of users has increased, wireless communication systems have aimed to offer high throughput. For this reason, various techniques which guarantee high performance have been invented, such as massive multiple-input multiple-output (MIMO). However, the implementation of huge base station (BS) antenna array and decrease of reliability as the number of users increases are chief obstacles. In order to mitigate these problems, this paper proposes an adaptive precoder which provides high throughput and bit error rate (BER) performances to achieve the desired data rate in multi user (MU) MIMO downlink systems which have a practical BS antenna array (up to 16). The proposed scheme is optimized with a modified minimum mean square error (MMSE) criterion in order to improve BER gain and reduce data streams in order to obtain diversity gain at low signal to noise ratio (SNR). It is shown that the BER and throughput performances of the proposed scheme are improved.