Search

The future Six-Generation (6G) envisions massive access of wireless devices in the network, leading to more serious interference from concurrent transmissions between wireless devices in the same frequency band. Existing interference mitigation approaches takes the interference signals as Gaussian white noise, which cannot precisely estimate the non-Gaussian interference signals from other devices. In this paper, we present IntLearner, a new interference mitigation technique that estimates and mitigates the impact of interference signals with only physical-layer (PHY) information available in base-station (BS) and user-equipment (UE), including channel estimator and constellations. More specifically, IntLearner utilizes the power of AI to estimate the features in interference signals, and removes the interference from the interfered received signal with neural network (NN). IntLearner's NN adopts a modular NN design, which takes the domain knowledge of BS and UE PHY as the guidance to NN design for minimizing training confusion and NN complexity. Simulation results show IntLearner increases Uplink (UL) channel estimation accuracy up to 7.4x, and reduces the Downlink (DL) Signal to Interference Ratio plus Noise Ratio (SINR) requirement to achieve the same Block Error Rate (BLER) by 1.5dB in a conventional multi-cell scenario.

In this paper, we propose an efficient simulation method based on adaptive importance sampling, which can automatically find the optimal proposal within the Gaussian family based on previous samples, to evaluate the probability of bit error rate (BER) or word error rate (WER). These two measures, which involve high-dimensional black-box integration and rare-event sampling, can characterize the performance of coded modulation. We further integrate the quasi-Monte Carlo method within our framework to improve the convergence speed. The proposed importance sampling algorithm is demonstrated to have much higher efficiency than the standard Monte Carlo method in the AWGN scenario., Comment: importance sampling for system model

Since the introduction of massive MIMO (mMIMO), the design of a transceiver with feasible complexity has been a challenging problem. Initially, it was believed that the main issue in this respect is the overall RF-cost. However, as mMIMO is becoming more and more a key technology for future wireless networks, it is realized, that the RF-cost is only one of many implementational challenges and design trade-offs. In this paper, we present, analyze and compare various novel mMIMO architectures, considering recent emerging technologies such as intelligent surface-assisted and Rotman lens based architectures. These are compared to the conventional fully digital (FD) and hybrid analog-digital beamforming (HADB) approaches. To enable a fair comparison, we account for various hardware imperfections and losses and utilize a novel, universal algorithm for signal precoding. Based on our thorough investigations, we draw a generic efficiency to quality trade-off for various mMIMO architectures. We find that in a typical cellular communication setting the reflect/transmit array based architectures sketch the best overall trade-off. Further, we show that in a qualitative ranking the power efficiency of the considered architectures is independent of the frequency range., Comment: 11 pages, 3 tables, 3 figures

In this work, we introduce $\beta$-expansion, a notion borrowed from number theory, as a theoretical framework to study fast construction of polar codes based on a recursive structure of universal partial order (UPO) and polarization weight (PW) algorithm. We show that polar codes can be recursively constructed from UPO by continuously solving several polynomial equations at each recursive step. From these polynomial equations, we can extract an interval for $\beta$, such that ranking the synthetic channels through a closed-form $\beta$-expansion preserves the property of nested frozen sets, which is a desired feature for low-complex construction. In an example of AWGN channels, we show that this interval for $\beta$ converges to a constant close to $1.1892 \approx 2^{1/4}$ when the code block-length trends to infinity. Both asymptotic analysis and simulation results validate our theoretical claims.

We address the problem of noise and interference corrupted channel estimation in massive MIMO systems. Interference, which originates from pilot reuse (or contamination), can in principle be discriminated on the basis of the distributions of path angles and amplitudes. In this paper we propose novel robust channel estimation algorithms exploiting path diversity in both angle and power domains, relying on a suitable combination of the spatial filtering and amplitude based projection. The proposed approaches are able to cope with a wide range of system and topology scenarios, including those where, unlike in previous works, interference channel may overlap with desired channels in terms of multipath angles of arrival or exceed them in terms of received power. In particular we establish analytically the conditions under which the proposed channel estimator is fully decontaminated. Simulation results confirm the overall system gains when using the new methods., Comment: 14 pages, 5 figures, accepted for publication in IEEE Transactions on Signal Processing

A decentralized network of cognitive and non-cognitive transmitters where each transmitter aims at maximizing his energy-efficiency is considered. The cognitive transmitters are assumed to be able to sense the transmit power of their non-cognitive counterparts and the former have a cost for sensing. The Stackelberg equilibrium analysis of this $2-$level hierarchical game is conducted, which allows us to better understand the effects of cognition on energy-efficiency. In particular, it is proven that the network energy-efficiency is maximized when only a given fraction of terminals are cognitive. Then, we study a sensing game where all the transmitters are assumed to take the decision whether to sense (namely to be cognitive) or not. This game is shown to be a weighted potential game and its set of equilibria is studied. Playing the sensing game in a first phase (e.g., of a time-slot) and then playing the power control game is shown to be more efficient individually for all transmitters than playing a game where a transmitter would jointly optimize whether to sense and his power level, showing the existence of a kind of Braess paradox. The derived results are illustrated by numerical results and provide some insights on how to deploy cognitive radios in heterogeneous networks in terms of sensing capabilities. Keywords: Power Control, Stackelberg Equilibrium, Energy-Efficiency., Comment: 13 pages,5 figures, Journal Paper