Underlay Cognitive Radio with Full or Partial Channel Quality Information

Underlay cognitive radios (UCRs) allow a secondary user to enter a primary user's spectrum through intelligent utilization of multiuser channel quality information (CQI) and sharing of codebook. The aim of this work is to study two-user Gaussian UCR systems by assuming the full or partial knowledge of multiuser CQI. Key contribution of this work is motivated by the fact that the full knowledge of multiuser CQI is not always available. We first establish a location-aided UCR model where the secondary user is assumed to have partial CQI about the secondary-transmitter to primary-receiver link as well as full CQI about the other links. Then, new UCR approaches are proposed and carefully analyzed in terms of the secondary user's achievable rate, denoted by $C_2$, the capacity penalty to primary user, denoted by $\Delta C_1$, and capacity outage probability. Numerical examples are provided to visually compare the performance of UCRs with full knowledge of multiuser CQI and the proposed approaches with partial knowledge of multiuser CQI.Comment: 29 Pages, 8 figure

Exploiting hidden pilots for carrier frequency offset estimation for generalized MC-CDMA systems

This paper proposes a novel carrier frequency offset (CFO) estimation method for generalized MC-CDMA systems in unknown frequency-selective channels utilizing hidden pi- lots. It is established that CFO is identifiable in the frequency domain by employing cyclic statistics (CS) and linear re-gression (LR) algorithms. We show that the CS-based estimator is capable of mitigating the normalized CFO (NCFO) to a small error value. Then, the LR-based estimator can be employed to offer more accurate estimation by removing the residual quantization error after the CS-based estimator

An Orthogonal-SGD based Learning Approach for MIMO Detection under Multiple Channel Models

In this paper, an orthogonal stochastic gradient descent (O-SGD) based learning approach is proposed to tackle the wireless channel over-training problem inherent in artificial neural network (ANN)-assisted MIMO signal detection. Our basic idea lies in the discovery and exploitation of the training-sample orthogonality between the current training epoch and past training epochs. Unlike the conventional SGD that updates the neural network simply based upon current training samples, O-SGD discovers the correlation between current training samples and historical training data, and then updates the neural network with those uncorrelated components. The network updating occurs only in those identified null subspaces. By such means, the neural network can understand and memorize uncorrelated components between different wireless channels, and thus is more robust to wireless channel variations. This hypothesis is confirmed through our extensive computer simulations as well as performance comparison with the conventional SGD approach.Comment: 6 pages, 4 figures, conferenc

Alternative Normalized-Preconditioning for Scalable Iterative Large-MIMO Detection

Signal detection in large multiple-input multiple-output (large-MIMO) systems presents greater challenges compared to conventional massive-MIMO for two primary reasons. First, large-MIMO systems lack favorable propagation conditions as they do not require a substantially greater number of service antennas relative to user antennas. Second, the wireless channel may exhibit spatial non-stationarity when an extremely large aperture array (ELAA) is deployed in a large-MIMO system. In this paper, we propose a scalable iterative large-MIMO detector named ANPID, which simultaneously delivers 1) close to maximum-likelihood detection performance, 2) low computational-complexity (i.e., square-order of transmit antennas), 3) fast convergence, and 4) robustness to the spatial non-stationarity in ELAA channels. ANPID incorporates a damping demodulation step into stationary iterative (SI) methods and alternates between two distinct demodulated SI methods. Simulation results demonstrate that ANPID fulfills all the four features concurrently and outperforms existing low-complexity MIMO detectors, especially in highly-loaded large MIMO systems.Comment: Accepted by IEEE GLOBECOM 202

On Chernoff Lower-Bound of Outage Threshold for Non-Central χ2\chi^2-Distributed MIMO Beamforming Gain

The cumulative distribution function (CDF) of a non-central $\chi^2$-distributed random variable (RV) is often used when measuring the outage probability of communication systems. For adaptive transmitters, it is important but mathematically challenging to determine the outage threshold for an extreme target outage probability (e.g., $10^{-5}$ or less). This motivates us to investigate lower bounds of the outage threshold, and it is found that the one derived from the Chernoff inequality (named Cher-LB) is the most {effective} lower bound. The Cher-LB is then employed to predict the multi-antenna transmitter beamforming-gain in ultra-reliable and low-latency communication, concerning the first-order Markov time-varying channel. It is exhibited that, with the proposed Cher-LB, pessimistic prediction of the beamforming gain is made sufficiently accurate for guaranteed reliability as well as the transmit-energy efficiency.Comment: 6 pages, 4 figures, published on GLOBECOM 202

Sherman-Morrison Regularization for ELAA Iterative Linear Precoding

The design of iterative linear precoding is recently challenged by extremely large aperture array (ELAA) systems, where conventional preconditioning techniques could hardly improve the channel condition. In this paper, it is proposed to regularize the extreme singular values to improve the channel condition by deducting a rank-one matrix from the Wishart matrix of the channel. Our analysis proves the feasibility to reduce the largest singular value or to increase multiple small singular values with a rank-one matrix when the singular value decomposition of the channel is available. Knowing the feasibility, we propose a low-complexity approach where an approximation of the regularization matrix can be obtained based on the statistical property of the channel. It is demonstrated, through simulation results, that the proposed low-complexity approach significantly outperforms current preconditioning techniques in terms of reduced iteration number for more than $10\%$ in both ELAA systems as well as symmetric multi-antenna (i.e., MIMO) systems when the channel is i.i.d. Rayleigh fading.Comment: 7 pages, 5 figures, IEEE ICC 202

Power Allocation for FDMA-URLLC Downlink with Random Channel Assignment

Concerning ultra-reliable low-latency communication (URLLC) for the downlink operating in the frequency-division multiple-access with random channel assignment, a lightweight power allocation approach is proposed to maximize the number of URLLC users subject to transmit-power and individual user-reliability constraints. Provided perfect channel-state-information at the transmitter (CSIT), the proposed approach is proven to ensure maximized URLLC users. Assuming imperfect CSIT, the proposed approach still aims to maximize the URLLC users without compromising the individual user reliability by using a pessimistic evaluation of the channel gain. It is demonstrated, through numerical results, that the proposed approach can significantly improve the user capacity and the transmit-power efficiency in Rayleigh fading channels. With imperfect CSIT, the proposed approach can still provide remarkable user capacity at limited cost of transmit-power efficiency.Comment: 6 pages, 6 figures, published on the conference of PIMRC 202