Strong Multi-user Interference Research Articles

Graph Neural Network Aided MU-MIMO Detectors

Multi-user multiple-input multiple-output (MU-MIMO) systems can be used to meet high throughput requirements of 5G and beyond networks. A base station serves many users in an uplink MU-MIMO system, leading to a substantial multi-user interference (MUI). Designing a high-performance detector for dealing with a strong MUI is challenging. This paper analyses the performance degradation caused by the posterior distribution approximation used in the state-of-the-art message passing (MP) detectors in the presence of high MUI. We develop a graph neural network based framework to fine-tune the MP detectors’ cavity distributions and thus improve the posterior distribution approximation in the MP detectors. We then propose two novel neural network based detectors which rely on the expectation propagation (EP) and Bayesian parallel interference cancellation (BPIC), referred to as the GEPNet and GPICNet detectors, respectively. The GEPNet detector maximizes detection performance, while GPICNet detector balances the performance and complexity. We provide proof of the permutation equivariance property, allowing the detectors to be trained only once, even in the systems with dynamic changes of the number of users. The simulation results show that the proposed GEPNet detector performance approaches maximum likelihood performance in various configurations and GPICNet detector doubles the multiplexing gain of BPIC detector.

Dynamic Spectrum Sharing Among Repeatedly Interacting Selfish Users With Imperfect Monitoring

We develop a novel design framework for dynamic distributed spectrum sharing among secondary users (SUs), who adjust their power levels to compete for spectrum opportunities while satisfying the interference temperature (IT) constraints imposed by primary users. The considered interaction among the SUs is characterized by the following three unique features. First, the SUs are interacting with each other repeatedly and they can coexist in the system for a long time. Second, the SUs have limited and imperfect monitoring ability: they only observe whether the IT constraints are violated, and their observation is imperfect due to the erroneous measurements. Third, since the SUs are decentralized, they are selfish and aim to maximize their own long-term payoffs from utilizing the network rather than obeying the prescribed allocation of a centralized controller. To capture these unique features, we model the interaction of the SUs as a repeated game with imperfect monitoring. We first characterize the set of Pareto optimal operating points that can be achieved by deviation-proof spectrum sharing policies, which are policies that the selfish users find it in their interest to comply with. Next, for any given operating point in this set, we show how to construct a deviation-proof policy to achieve it. The constructed deviation-proof policy is amenable to distributed implementation, and allows users to transmit in a time-division multiple-access (TDMA) fashion. In the presence of strong multi-user interference, our policy outperforms existing spectrum sharing policies that dictate users to transmit at constant power levels simultaneously. Moreover, our policy can achieve Pareto optimality even when the SUs have limited and imperfect monitoring ability, as opposed to existing solutions based on repeated game models, which require perfect monitoring abilities. Simulation results validate our analytical results and quantify the performance gains enabled by the proposed spectrum sharing policies.

Adaptive interference suppression and cancellation for underwater acoustic communications.

An adaptive, time‐reversal multichannel combiner is embedded within an iterative, successive interference cancellation framework to create a reduced‐complexity, multiuser receiver with multiple‐access interference suppression. The combined receiver achieves the goal of separating simultaneous, full‐band transmissions from physically separated users transmitting to a base station in an underwater uplink setting. Although both receiver components could feasibly act alone, the combined receiver is shown to be more robust than its individuals, achieving both temporal and spatial interference mitigations in the presence of strong multiuser interference. Furthermore, with the addition of matching pursuit, a popular sparse channel estimation technique, and a block‐by‐block channel adaptation, the overall receiver is designed to be applicable to time‐varying underwater acoustic environments. Analysis of experimental data collected during FAF‐06 illustrates that for a two‐user multiple‐access system, multiuser separation can be achieved even in low signal‐to‐noise ratio (SNR) environments.

Frequency-Interleaved Spread Spectrum with MMSE Frequency-Domain Equalization

The use of frequency-domain equalization (FDE) based on minimum mean square error (MMSE) criterion can significantly improve the downlink bit error rate (BER) performances of DS- and MC-CDMA in a frequency-selective fading channel. However, the uplink BER performance degrades due to a strong multi-user interference (MUI). In this paper, we propose frequency-interleaved spread spectrum (SS) using MMSE-FDE, in which the subcarrier components of each user's signal are interleaved onto a wider bandwidth. Then, the frequency-interleaved frequency-domain signal is transformed into a time-domain signal by the inverse fast Fourier transform (IFFT). Frequency-interleaving patterns assigned to different users are orthogonal to each other. The proposed scheme can avoid the MUI completely while achieving frequency diversity gain due to MMSE-FDE. It is shown by computer simulation that the use of frequency-interleaving can significantly improve the uplink performance in a frequency-selective Rayleigh fading channel.