Resource Allocation for Intelligent Reflecting Surfaces Assisted Federated Learning System with Imperfect CSI

Due to its ability to significantly improve the wireless communication efficiency, the intelligent reflective surface (IRS) has aroused widespread research interest. However, it is a challenge to obtain perfect channel state information (CSI) for IRS-related channels due to the lack of the ability to send, receive, and process signals at IRS. Since most of the existing channel estimation methods are developed to obtain cascaded base station (BS)-IRS-user devices (UDs) channel, this paper studies the problem of computation and communication resource allocation of the IRS-assisted federated learning (FL) system based on the imperfect CSI. Specifically, we take the statistical CSI error model into consideration and formulate the training time minimization problem subject to the rate outage probability constraints. In order to solve this issue, the semi-definite relaxation (SDR) and the constrained concave convex procedure (CCCP) are invoked to transform it into a convex problem. Subsequently, a low-complexity algorithm is proposed to minimize the delay of the FL system. Numerical results show that the proposed algorithm effectively reduces the training time of the FL system base on imperfect CSI.

CR-NOMA Networks over Nakagami- m Fading: CSI Imperfection Perspective

There are high demands on both massive connections and high spectrum efficiency, and the cognitive radio-based nonorthogonal multiple access (CR-NOMA) system is developed to satisfy such demand. The unreal situation of CR-NOMA is considering the perfect channel state information (CSI) in receivers. This paper indicates impacts of imperfect CSI on outage and throughput performance. In particular, we focus on performance of the secondary network related to the imperfect CSI, and we derive closed-form expressions of outage probability and throughput for the downlink in such a CR-NOMA system. Particularly, a general form of Nakagami- m fading channel is adopted to examine the impact of fading on the performance of the CR-NOMA system. As the main achievement, we conduct extensive simulations and provide analyses to demonstrate the outage performance of the CR-NOMA system with CSI imperfections.

Information Bottleneck for a Rayleigh Fading MIMO Channel with an Oblivious Relay

This paper considers the information bottleneck (IB) problem of a Rayleigh fading multiple-input multiple-out (MIMO) channel with an oblivious relay. The relay is constrained to operating without knowledge of the codebooks, i.e., it performs oblivious processing. Moreover, due to the bottleneck constraint, it is impossible for the relay to inform the destination node of the perfect channel state information (CSI) in each channel realization. To evaluate the bottleneck rate, we first provide an upper bound by assuming that the destination node can obtain a perfect CSI at no cost. Then, we provide four achievable schemes, where each scheme satisfies the bottleneck constraint and gives a lower bound to the bottleneck rate. In the first and second schemes, the relay splits the capacity of the relay–destination link into two parts and conveys both the CSI and its observation to the destination node. Due to CSI transmission, the performance of these two schemes is sensitive to the MIMO channel dimension, especially the channel input dimension. To ensure that it still performs well when the channel dimension grows large, in the third and fourth achievable schemes, the relay only transmits compressed observations to the destination node. Numerical results show that, with simple symbol-by-symbol oblivious relay processing and compression, the proposed achievable schemes work well and can demonstrate lower bounds that come quite close to the upper bound on a wide range of relevant system parameters.

Robust Spectrum Sensing Detector Based on MIMO Cognitive Radios with Non-Perfect Channel Gain

The spectrum has increasingly become occupied by various wireless technologies. For this reason, the spectrum has become a scarce resource. In prior work, the authors have addressed the spectrum sensing problem by using multi-input and multi-output (MIMO) in cognitive radio systems. We considered the detection and estimation framework for MIMO cognitive network where the noise covariance matrix is unknown with perfect channel state information. In this study, we propose a generalized likelihood ratio test (GLRT) for the spectrum sensing problem in cognitive radio where the noise covariance matrix is unknown with non-perfect channel state information. Two scenarios are examined in this study: (i) in the first scenario, the sub-optimal solution of the worst case of the system’s performance is considered; (ii) in the second scenario, we present a robust detector for the MIMO spectrum sensing problem. For both scenarios, the Bayesian approach with a generalized likelihood ratio test based on the binary hypothesis problem is used. From the results, it can be seen that our approach provides the best performance in the spectrum sensing problem under specified assumptions. The simulation results also demonstrate that our approach significantly outperforms other state-of-the-art spectrum sensing detectors when the channel uncertainty is addressed.

Antenna tilt optimization for multi-cell massive multiple-input multiple-output (MIMO) systems with two tilts

In this paper, we study the optimization of two tilt angles corresponding to two antenna arrays in each base station (BS) of a massive multiple-input multiple-output system. We consider two scenarios with perfect channel state information (CSI) and imperfect CSI. In the limit of the number of the BS antennas, the channel orthogonality is employed to derive the limit and the lower bound of the throughputs. By maximizing the lower bound or the limit throughput, the two antenna tilt angles are optimized. Simulation results show that the throughput performance can be improved with the designed tilt angles.

Ergodic Rate for Fading Interference Channels with Proper and Improper Gaussian Signaling

This paper studies the performance of improper Gaussian signaling (IGS) over a 2-user Rayleigh single-input single-output (SISO) interference channel, treating interference as noise. We assume that the receivers have perfect channel state information (CSI), while the transmitters have access to only statistical CSI. Under these assumptions, we consider a signaling scheme, which we refer to as proper/improper Gaussian signaling or PGS/IGS, where at most one user may employ IGS. For the Rayleigh fading channel model, we characterize the statistical distribution of the signal-to-interference-plus-noise ratio at each receiver and derive closed-form expressions for the ergodic rates. By adapting the powers, we characterize the Pareto boundary of the ergodic rate region for the 2-user fading IC. The ergodic transmission rates can be attained using fixed-rate codebooks and no optimization is involved. Our results show that, in the moderate and strong interference regimes, the proposed PGS/IGS scheme improves the performance with respect to the PGS scheme. Additionally, we numerically compute the ergodic rate region of the full IGS scheme when both users can employ IGS and their transmission parameters are optimized by an exhaustive search. Our results suggest that most of the Pareto optimal points for the 2-user fading IC channel are attained when either both users transmit PGS or when one transmits PGS and the other transmits maximally improper Gaussian signals and time sharing is allowed.

Capacity of Cognitive Radio with Partial Channel Distribution Information in Rayleigh Fading Environments

This paper investigates the capacity of the secondary user (SU) in a cognitive radio (CR) network in Rayleigh fading environments. Different from existing works where perfect channel state information (CSI) or channel distribution information (CDI) of the interference link from the SU to the primary user (PU) is assumed to be available, this paper assumes that only partial CDI is available. Specifically, we assume the distribution parameter is unknown and estimated from a set of channel gain samples. With such partial CDI, closed-form expressions for the ergodic and outage capacities of the SU are obtained under the transmit power and the interference outage constraints. It is shown that the capacity with partial CDI is not degraded compared to that with perfect CDI if the interference outage constraint is loose. It is also shown that the capacity can be significantly improved by increasing the number of channel gain samples.