Uplink Coordinated Pilot Design for 1-bit Massive MIMO in Correlated Channel

In this paper, we propose a coordinated pilot design method to minimize the channel estimation mean squared error (MSE) in 1-bit analog-to-digital converters (ADCs) massive multiple-input multiple-output (MIMO). Under the assumption that the well-known Bussgang linear minimum mean square error (BLMMSE) estimator is used for channel estimation, we first observe that the resulting MSE leads to an intractable optimization problem, as it involves the arcsin function and a complex multiple matrix ratio form. To resolve this, we derive the approximate MSE by assuming the low signal-to-noise ratio (SNR) regime, by which we develop an efficient coordinated pilot design based on a fractional programming technique. The proposed pilot design is distinguishable from the existing work in that it is applicable in general system environments, including correlated channel and multi-cell environments. We demonstrate that the proposed method outperforms the channel estimation accuracy performance compared to the conventional approaches.

Reducing Latency by Eliminating CSIT Feedback: FDD Downlink MIMO Precoding Without CSIT Feedback for Internet-of-Things Communications

This paper presents a novel framework for low-latency frequency division duplex (FDD) multi-input multi-output (MIMO) transmission with Internet of Things (IoT) communications. Our key idea is eliminating feedback associated with downlink channel state information at the transmitter (CSIT) acquisition. Instead, we propose to reconstruct downlink CSIT from uplink reference signals by exploiting the frequency invariance property on channel parameters. Nonetheless, the frequency disparity between the uplink and downlink makes it impossible to get perfect downlink CSIT, resulting in substantial interference. To address this, we formulate a max-min fairness problem and propose a rate-splitting multiple access (RSMA)-aided efficient precoding method. In particular, to fully harness the potential benefits of RSMA, we propose a method that approximates the error covariance matrix and incorporates it into the precoder optimization process. This approach effectively accounts for the impact of imperfect CSIT, enabling the design of a robust precoder that efficiently handles CSIT inaccuracies. Simulation results demonstrate that our framework outperforms other baseline methods in terms of the minimum spectral efficiency when no direct CSI feedback is used. Moreover, we show that our framework significantly reduces communication latency compared to conventional CSI feedback-based methods, underscoring its effectiveness in enhancing latency performance for IoT communications.

A Selective Secure Precoding Framework for MU-MIMO Rate-Splitting Multiple Access Networks Under Limited CSIT

In this paper, we propose a robust and adaptable secure precoding framework designed to encapsulate a intricate scenario where legitimate users have different information security: secure private or normal public information. Leveraging rate-splitting multiple access (RSMA), we formulate the sum secrecy spectral efficiency (SE) maximization problem in downlink multi-user multiple-input multiple-output (MIMO) systems with multi-eavesdropper. To resolve the challenges including the heterogeneity of security, non-convexity, and non-smoothness of the problem, we initially approximate the problem using a LogSumExp technique. Subsequently, we derive the first-order optimality condition in the form of a generalized eigenvalue problem. We utilize a power iteration-based method to solve the condition, thereby achieving a superior local optimal solution. The proposed algorithm is further extended to a more realistic scenario involving limited channel state information at the transmitter (CSIT). To effectively utilize the limited channel information, we employ a conditional average rate approach. Handling the conditional average by deriving useful bounds, we establish a lower bound for the objective function under the conditional average. Then we apply the similar optimization method as for the perfect CSIT case. In simulations, we validate the proposed algorithm in terms of the sum secrecy SE.

Low-Earth Orbit Satellite Network Analysis: Coverage under Distance-Dependent Shadowing

This paper offers a thorough analysis of the coverage performance of Low Earth Orbit (LEO) satellite networks using a strongest satellite association approach, with a particular emphasis on shadowing effects modeled through a Poisson point process (PPP)-based network framework. We derive an analytical expression for the coverage probability, which incorporates key system parameters and a distance-dependent shadowing probability function, explicitly accounting for both line-of-sight and non-line-of-sight propagation channels. To enhance the practical relevance of our findings, we provide both lower and upper bounds for the coverage probability and introduce a closed-form solution based on a simplified shadowing model. Our analysis reveals several important network design insights, including the enhancement of coverage probability by distance-dependent shadowing effects and the identification of an optimal satellite altitude that balances beam gain benefits with interference drawbacks. Notably, our PPP-based network model shows strong alignment with other established models, confirming its accuracy and applicability across a variety of satellite network configurations. The insights gained from our analysis are valuable for optimizing LEO satellite deployment strategies and improving network performance in diverse scenarios.

Spectrum Sharing Between Low Earth Orbit Satellite and Terrestrial Networks: A Stochastic Geometry Perspective Analysis

Low Earth orbit (LEO) satellite networks with mega constellations have the potential to provide 5G and beyond services ubiquitously. However, these networks may introduce mutual interference to both satellite and terrestrial networks, particularly when sharing spectrum resources. In this paper, we present a system-level performance analysis to address these interference issues using the tool of stochastic geometry. We model the spatial distributions of satellites, satellite users, terrestrial base stations (BSs), and terrestrial users using independent Poisson point processes on the surfaces of concentric spheres. Under these spatial models, we derive analytical expressions for the ergodic spectral efficiency of uplink (UL) and downlink (DL) satellite networks when they share spectrum with both UL and DL terrestrial networks. These derived ergodic expressions capture comprehensive network parameters, including the densities of satellite and terrestrial networks, the path-loss exponent, and fading. From our analysis, we determine the conditions under which spectrum sharing with UL terrestrial networks is advantageous for both UL and DL satellite networks. Our key finding is that the optimal spectrum sharing configuration among the four possible configurations depends on the density ratio between terrestrial BSs and users, providing a design guideline for spectrum management. Simulation results confirm the accuracy of our derived expressions.

LLM-Empowered Resource Allocation in Wireless Communications Systems

The recent success of large language models (LLMs) has spurred their application in various fields. In particular, there have been efforts to integrate LLMs into various aspects of wireless communication systems. The use of LLMs in wireless communication systems has the potential to realize artificial general intelligence (AGI)-enabled wireless networks. In this paper, we investigate an LLM-based resource allocation scheme for wireless communication systems. Specifically, we formulate a simple resource allocation problem involving two transmit pairs and develop an LLM-based resource allocation approach that aims to maximize either energy efficiency or spectral efficiency. Additionally, we consider the joint use of low-complexity resource allocation techniques to compensate for the reliability shortcomings of the LLM-based scheme. After confirming the applicability and feasibility of LLM-based resource allocation, we address several key technical challenges that remain in applying LLMs in practice.

FDD Massive MIMO: How to Optimally Combine UL Pilot and Limited DL CSI Feedback?

In frequency-division duplexing (FDD) multiple-input multiple-output (MIMO) systems, obtaining accurate downlink channel state information (CSI) for precoding is vastly challenging due to the tremendous feedback overhead with the growing number of antennas. Utilizing uplink pilots for downlink CSI estimation is a promising approach that can eliminate CSI feedback. However, the downlink CSI estimation accuracy diminishes significantly as the number of channel paths increases, resulting in reduced spectral efficiency. In this paper, we demonstrate that achieving downlink spectral efficiency comparable to perfect CSI is feasible by combining uplink CSI with limited downlink CSI feedback information. Our proposed downlink CSI feedback strategy transmits quantized phase information of downlink channel paths, deviating from conventional limited methods. We put forth a mean square error (MSE)-optimal downlink channel reconstruction method by jointly exploiting the uplink CSI and the limited downlink CSI. Armed with the MSE-optimal estimator, we derive the MSE as a function of the number of feedback bits for phase quantization. Subsequently, we present an optimal feedback bit allocation method for minimizing the MSE in the reconstructed channel through phase quantization. Utilizing a robust downlink precoding technique, we establish that the proposed downlink channel reconstruction method is sufficient for attaining a sum-spectral efficiency comparable to perfect CSI.

Splitting Messages in the Dark- Rate-Splitting Multiple Access for FDD Massive MIMO Without CSI Feedback

A critical hindrance to realize frequency division duplex (FDD) massive multi-input multi-output (MIMO) systems is overhead associated with downlink channel state information at the transmitter (CSIT) acquisition. To address this challenge, we propose a novel framework that achieves robust performances while completely eliminating downlink CSIT training and feedback. Specifically, by exploiting partial frequency invariance of channel parameters between the uplink (UL) and downlink (DL), we adopt the 2D-Newtonized orthogonal matching pursuit (2D-NOMP) algorithm to reconstruct DL CSIT from UL training. Due to inherent discrepancies arising from a carrier frequency difference between two disjoint bands, however, the multi-user interference is inevitable. To overcome this, we propose a precoding method that employs rate-splitting multiple access (RSMA) and also develop an error covariance matrix (ECM) estimation method by using the observed Fisher information matrix (O-FIM). We find that this ECM estimation is crucial for our precoding design in maximizing the sum spectral efficiency (SE). Simulation results show that our method significantly improves the sum SE compared to other state-of-the-art approaches, underscoring the importance of our ECM estimation.

Modeling and Coverage Analysis of K-Tier Integrated Satellite-Terrestrial Downlink Networks

Integrated satellite-terrestrial networks (ISTNs) can significantly expand network coverage while diminishing reliance on terrestrial infrastructure. Despite the enticing potential of ISTNs, there is no comprehensive mathematical performance analysis framework for these emerging networks. In this paper, we introduce a tractable approach to analyze the downlink coverage performance of multi-tier ISTNs, where each network tier operates with orthogonal frequency bands. The proposed approach is to model the spatial distribution of cellular and satellite base stations using homogeneous Poisson point processes arranged on concentric spheres with varying radii. Central to our analysis is a displacement principle that transforms base station locations on different spheres into projected rings while preserving the distance distribution to the typical user. By incorporating the effects of Shadowed-Rician fading on satellite channels and employing orthogonal frequency bands, we derive analytical expressions for coverage in the integrated networks while keeping full generality. Our primary discovery is that network performance reaches its maximum when selecting the optimal density ratio of users associated with the network according to the density and the channel parameters of each network. Through simulations, we validate the precision of our derived expressions.

Joint and Robust Beamforming Framework for Integrated Sensing and Communication Systems

Integrated sensing and communication (ISAC) is widely recognized as a fundamental enabler for future wireless communications. In this paper, we present a joint communication and radar beamforming framework for maximizing a sum spectral efficiency (SE) while guaranteeing desired radar performance with imperfect channel state information (CSI) in multi-user and multi-target ISAC systems. To this end, we adopt either a radar transmit beam mean square error (MSE) or receive signal-to-clutter-plus-noise ratio (SCNR) as a radar performance constraint of a sum SE maximization problem. To resolve inherent challenges such as non-convexity and imperfect CSI, we reformulate the problems and identify first-order optimality conditions for the joint radar and communication beamformer. Turning the condition to a nonlinear eigenvalue problem with eigenvector dependency (NEPv), we develop an alternating method which finds the joint beamformer through power iteration and a Lagrangian multiplier through binary search. The proposed framework encompasses both the radar metrics and is robust to channel estimation error with low complexity. Simulations validate the proposed methods. In particular, we observe that the MSE and SCNR constraints exhibit complementary performance depending on the operating environment, which manifests the importance of the proposed comprehensive and robust optimization framework.