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 of 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.

This paper addresses precoder design for secure multiple-input multiple-output (MIMO) integrated sensing and communications (ISAC) systems. We introduce a MIMO channel with a multiple-antenna eavesdropper and a multiple-antenna sensing receiver (MIMO-ME-MS) and analyze the fundamental performance limits of this tripartite tradeoff. Using sensing mutual information, we formulate the precoder design as a nonconvex weighted sum rate maximization problem. A high signal-to-noise ratio analysis based on a subspace decomposition characterizes the maximum weighted degrees of freedom. This analysis reveals the structure of a quasi-optimal precoder that must span the “useful subspace” and demonstrates the inadequacy of extending known schemes from simpler wiretap or ISAC channels. To solve this nonconvex problem, we develop a practical two-stage iterative algorithm that alternates between a sequential basis construction stage and a power allocation stage that solves the resulting difference-of-convex program. We demonstrate that the proposed method captures the desirable precoder structure identified in our analysis and achieves substantial performance gains in the MIMO-ME-MS channel.

Efficient channel estimation and beamforming in the upper mid-band spectrum pose a fundamental challenge for 6G base stations (BSs). This requires practical beam management techniques that account for the propagation characteristics of this frequency range and the increasing number of BS antennas. To address the channel estimation bottleneck in Frequency Division Duplex (FDD) systems, this paper proposes an integrated framework combining channel synthesis and AI-based beam management. We first introduce partial statistical reciprocity (PSR) by extending the concept of partial reciprocity into the statistical domain, enabling a novel model-based online channel synthesis method. The proposed approach uses uplink channel parameters collected during BS operation to generate synthetic downlink channels that reflect the spatial distribution of active user equipment (UE), enabling the creation of high-quality, site-specific datasets for AI training. Furthermore, we propose a joint optimization framework for scalable codebook design and downlink channel estimation. A U-Net Transformer model is trained to estimate full beam-domain channel state information (CSI) using a single snapshot of uplink CSI and feedback from codebook-based beam sweeping. The model iteratively updates the codebook via backpropagation to align with current UE distributions and channel conditions, while channel estimation is performed through forward inference. The proposed AI-based framework achieves accurate channel reconstruction with minimal feedback, offering a scalable and practical solution for 6G spatial multiple access in the upper mid-band FDD system.

Underactuated robotic hands are extensively used in remote manipulation due to their ability to adapt to various object sizes and shapes. Their structural simplicity and small number of actuators required for operation make them highly versatile and responsive, which is crucial for effective teleoperation. In addition to grasping performance, haptic feedback, which integrates force and tactile sensing, is essential for dexterous manipulation. This study proposes a solution using fiber-optic tendons embedded with fiber Bragg gratings (FBGs), combining sensing and actuation to simultaneously perform power transmission, along with force and tactile sensing. Each finger employs a fiber-optic tendon with three FBGs: one measures tendon tension, and the other two at the fingertip detect contact force and temperature. The tendon is placed on the volar side of the finger and routed to an actuation module with a servomotor at the wrist for power transmission. This tendon enables finger flexion, while a passive extension mechanism with linear springs on the dorsal side facilitates extension. Experimental results demonstrate the feasibility of this approach, showing the hand's multifunctional capabilities, including haptic feedback and power transmission, as well as its potential for teleoperation. This approach improves the robotic hand's ability to provide real-time feedback, improving dexterity in remote manipulation.

In this paper, we propose a precoding framework for frequency division duplex (FDD) integrated sensing and communication (ISAC) systems with multiple-input multiple-output (MIMO). Specifically, we aim to maximize ergodic sum spectral efficiency (SE) while satisfying a sensing beam pattern constraint defined by the mean squared error (MSE). Our method reconstructs downlink (DL) channel state information (CSI) from uplink (UL) training signals using partial reciprocity, eliminating the need for CSI feedback. To obtain the error covariance matrix of the reconstructed DL CSI, we devise anobserved Fisher information-based estimation technique. Leveraging this, to mitigate interference caused by imperfect DL CSI reconstruction and sensing operations, we propose a rate-splitting multiple access (RSMA) aided precoder optimization method. This method jointly updates the precoding vector and Lagrange multipliers by solving the nonlinear eigenvalue problem with eigenvector dependency to maximize SE. The numerical results show that the proposed design achieves precise beam pattern control, maximizes SE, and significantly improves the sensing-communication trade-off compared to the state-of-the-art methods in FDD ISAC scenarios.

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.

Inter-beam interference is a core challenge in low Earth orbit (LEO) satellite communications, driven by dense constellations, aggressive frequency reuse, and overlapping beam footprints. To address this, we propose space–time beamforming, a novel approach that jointly exploits spatial and temporal channel characteristics—specifically the angle of arrival (AoA) and relative Doppler shift—to optimize transmission between moving satellites and ground users. By synthesizing a virtual array-of-subarrays across repeated transmissions, this method effectively expands the aperture and forms ultra-narrow beams, sharply suppressing interference leakage to neighboring users. We develop two strategies within this framework: space-time zero-forcing (ST-ZF) and space-time signal-to-leakage-plus-noise ratio (ST-SLNR) beamforming. In partially connected networks, ST-ZF provides a 3 dB SNR gain over conventional maximum ratio transmission (MRT). In more general interference scenarios, ST-SLNR delivers significant improvements in sum spectral efficiency. While temporal repetition introduces a rate trade-off, it also enables finer spatial discrimination through Doppler-induced temporal signatures. Our analysis and simulations demonstrate that space-time beamforming offers a powerful and adaptable solution for interference mitigation in next-generation LEO satellite systems, unlocking better spectral efficiency and more reliable connectivity in densely served orbital environments.

The overhead of acquiring downlink (DL) channel state information at the transmitter (CSIT) poses a significant challenge for frequency division duplex (FDD) massive multipleinput multiple-output (MIMO) systems. To address this, we propose a framework that reconstructs DL CSIT solely from uplink (UL) pilots, leveraging partial frequency invariance of channel parameters. However, since perfect reconstruction is infeasible due to inherent discrepancies between the UL and DL bands, multi-user interference (MUI) arises. To address this, we formulate the ergodic sum spectral efficiency (SE) by incorporating the estimated channel and error covariance matrix (ECM). Additionally, we propose an ECM estimation technique using observed Fisher information and enhance system performance with a rate-splitting multiple access (RSMA) precoder optimization design. The simulation results validate the effectiveness of the proposed framework, demonstrating the critical role of ECM estimation in mitigating MUI and providing robust SE gains in practical FDD-based massive MIMO scenarios.

Recently, semantic communications have drawn great attention as the groundbreaking concept surpasses the limited capacity of Shannon’s theory. Specifically, semantic communications are likely to become crucial in realizing visual tasks that demand massive network traffic. Although highly distinctive forms of visual semantics exist for computer vision tasks, a thorough investigation of what visual semantics can be transmitted in time and which one is required for completing different visual tasks has not yet been reported. To this end, we first scrutinize the achievable throughput in transmitting existing visual semantics through the limited wireless communication bandwidth. In addition, we further demonstrate the resulting performance of various visual tasks for each visual semantic. Based on the empirical testing, we suggest a task-adaptive selection of visual semantics is crucial for real-time semantic communications for visual tasks, where we transmit basic semantics (e.g., objects in the given image) for simple visual tasks, such as classification, and richer semantics (e.g., scene graphs) for complex tasks, such as image regeneration. To further improve transmission efficiency, we suggest a filtering method for scene graphs, which drops redundant information in the scene graph, thus allowing the sending of essential semantics for completing the given task.We confirm the efficacy of our task-adaptive semantic communication approach through extensive simulations in wireless channels, showing more than 45 times larger throughput over a naive transmission of original data. Our work can be reproduced at the following source codes: https://github.com/jhpark2024/jhpark.github.io.

Low Earth orbit (LEO) satellite communications (SATCOM) with ubiquitous global connectivity is deemed a pivotal catalyst in advancing wireless communication systems for 5G and beyond. LEO SATCOM excels in delivering versatile information services across expansive areas, facilitating both unicast and multicast transmissions via high-speed broadband capability. Nonetheless, given the broadband coverage of LEO SATCOM, traffic demand distribution within the service area is non-uniform, and the time/frequency/power resources available at LEO satellites remain significantly limited. Motivated by these challenges, we propose a rate-matching framework for non-orthogonal unicast and multicast (NOUM) transmission. Our approach aims to minimize the difference between offered rates and traffic demands for both unicast and multicast messages. By multiplexing unicast and multicast transmissions over the same radio resource, rate-splitting multiple access (RSMA) is employed to manage interference between unicast and multicast streams, as well as inter-user interference under imperfect channel state information at the LEO satellite. To address the formulated problem’s non-smoothness and non-convexity, the common rate is approximated using the LogSumExp technique. Thereafter, we represent the common rate portion as the ratio of the approximated function, converting the problem into an unconstrained form. A generalized power iteration (GPI)-based algorithm, coined GPI-RS-NOUM, is proposed upon this reformulation. Through comprehensive numerical analysis across diverse simulation setups, we demonstrate that the proposed framework outperforms various benchmarks for LEO SATCOM with uneven traffic demands.

Employing low-resolution quantizers increases energy efficiency (EE) while reducing spectral efficiency (SE) and deteriorating channel estimation accuracy, which induces higher inter-user interference. To overcome these drawbacks, we develop a rate-splitting multiple access (RSMA) precoding method in the low-resolution quantization system with imperfect channel state information at the transmitter (CSIT), which optimizes a balance between two critical yet often competing aspects: maximization of the SE to increase data rate and the EE to manage the power consumption. We first average the sum rate to properly define the SE and EE with the imperfect CSIT and error covariance matrices. Then we formulate a weighted SE and EE optimization problem and divide it into two sub-problems adopting a Dinkelbach approach: precoding direction and transmit power optimization. For precoding direction, we derive the first-order optimality condition. Casting the condition to a generalized eigenvalue problem, we propose an algorithm to identify the principal eigenvector which corresponds to the superior stationary point. Furthermore, we utilize a gradient method for transmit power optimization and update the precoding direction and transmit power alternately. Simulations validate the benefits of the proposed method in enhancing the SE and EE trade-off and reveal the superiority of RSMA over spatial-division multiple access.

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.

Multibeam satellite communication systems are promising to achieve high throughput. To achieve high performance without substantial overheads associated with channel state information (CSI) of ground users, we consider a fixed-beam precoding approach, where a satellite forms multiple fixed-beams without relying on CSI, then selects a suitable user set for each beam. Upon this precoding method, we put forth a satellite equipped with massive multiple-input multiple-output (MIMO), by which inter-beam interference is efficiently mitigated by narrowing the corresponding beam width. By modeling the ground users’ locations via a Poisson point process, we rigorously analyze the achievable performance of the presented multibeam satellite system. In particular, we investigate the asymptotic scaling laws that reveal the interplay between the user density, the number of beams, and the number of antennas. Our analysis offers critical design insights for the multibeam satellite with massive MIMO: i) If the user density scales proportionally with the number of antennas, the considered precoding can achieve a linear fraction of the optimal rate in the asymptotic regime. ii) A certain additional scaling factor for the user density is needed as the number of beams increases to maintain the asymptotic optimality.

Satellite-terrestrial integrated networks (STINs) are promising architecture for providing global coverage. In STINs, full frequency reuse between a satellite and a terrestrial base station (BS) is encouraged for aggressive spectrum reuse, which induces non-negligible amount of interference. To address the interference management problem in STINs, this paper proposes a novel distributed precoding method. Key features of our method are: i) a rate-splitting (RS) strategy is incorporated for efficient interference management and ii) the precoders are designed in a distributed way without sharing channel state information between a satellite and a terrestrial BS. Specifically, to design the precoders in a distributed fashion, we put forth a spectral efficiency decoupling technique, that disentangles the total spectral efficiency function into two distinct terms, each of which is dependent solely on the satellite’s precoder and the terrestrial BS’s precoder, respectively. Then, to resolve the non-smoothness raised by the RS strategy, we approximate the spectral efficiency expression as a smooth function by using the LogSumExp technique; thereafter we develop a generalized power iteration inspired optimization algorithm built based on the first-order optimality condition. Simulation results demonstrate that the proposed method offers considerable spectral efficiency gains compared to the existing methods.

A critical hindrance in realizing frequency division duplex (FDD) massive multi-input multi-output (MIMO) systems is the overhead associated with the downlink (DL) channel state information at the transmitter (CSIT) acquisition. To address this, we propose a novel framework that eliminates the need for CSI feedback, while achieving robust sum spectral efficiency (SE). Specifically, by leveraging partial frequency invariance of channel parameters, we reconstruct the DL CSIT using uplink (UL) pilots with the 2D-Newtonized orthogonal matching pursuit (2D-NOMP) algorithm. Due to discrepancies between the two disjoint bands, however, perfect DL CSIT acquisition is infeasible; resulting in multi-user interference (MUI). To account for this, we reformulate the sum SE maximization problem using the reconstructed channel and its error covariance matrix (ECM). Then, we propose an ECM estimation method based on the observed Fisher information matrix and introduce a precoder optimization technique with rate-splitting multiple access (RSMA). Our simulation results verify the validity of the proposed framework in the practical FDD massive MIMO scenarios, highlighting the essential role of ECM estimation in mitigating MUI to attain RSMA gains.

In frequency-division duplexing (FDD) multipleinput multiple-output (MIMO) systems, obtaining accurate downlink channel state information (CSI) becomes challenging due to the tremendous feedback overhead that increases with the number of antennas. Using uplink pilots to estimate downlink CSI is a promising approach that can eliminate the need for CSI feedback, but its accuracy decreases significantly as the number of channel paths increases. In this paper, we propose a mean square error (MSE)-optimal downlink channel reconstruction method that jointly utilizes uplink CSI and limited downlink CSI. With the MSE-optimal estimator, we derive the MSE as a function of the number of feedback bits for channel phase quantization and show the optimal feedback bit allocation method to minimize the MSE. Harnessing robust downlink precoding, we demonstrate that the proposed downlink channel reconstruction is sufficient to achieve a sum-spectral efficiency comparable to that with perfect downlink CSI.

Low Earth orbit (LEO) satellite communications with ubiquitous connectivity is deemed a key enabler for 6G. Since LEO satellites provide broadband coverage, traffic demand distribution of users is potentially non-uniform within the coverage area, and the available time/frequency/power resources are considerably limited. Motivated by this, we propose a rate-matching for non-orthogonal unicast and multicast (NOUM) transmission that matches the offered rates to the traffic demands of both unicast and multicast messages under limited power budgets. Also, rate-splitting multiple access (RSMA) is employed to manage interference between unicast and multi-cast streams along with inter-user interference under imperfect channel state information at the LEO satellite. To solve the non-convex and non-smooth formulated problem, we approximate the common rate using the LogSumExp technique; thereafter, we represent the common portion as the ratio of the approximated function, converting the problem into an unconstrained form. A generalized power iteration (GPI)-based algorithm, coined GPI-RS-NOUM, is proposed upon this reformulation. We numerically show that the proposed framework outperforms various benchmarks for LEO networks with uneven traffic demands.

Integrated satellite-terrestrial networks (ISTNs) has recently been significant interest to attain synergistic gains in expanded coverage and transmission rate enhancement. Despite the potential of ISTNs, a comprehensive mathematical performance analysis framwork is lacking, so this paper introduces a tractable approach to analyze the downlink coverage performance of ISTNs, where each network operates with orthogonal frequency bands. We model the spatial distribution of terrestrial and satellite base stations (BSs) using homogeneous Poisson point processes arranged on concentric spheres with varying radii. Central to our analysis is a displacement principle that transforms BS locations on different spheres into annuli while preserving the distance distribution to the typical user. By incorporating the effects of Shadowed-Rician fading on satellite channels, we derive analytical expression for coverage in the ISTN while keeping full generality. Our key finding is that network performance depends on the 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 expression.

In this paper, we utilize rate-splitting multiple access (RSMA) by expanding the achievable degrees of freedom in downlink multiuser multiple-input multiple-output (MIMO) systems that incorporate mixed-resolution quantizers at an access point (AP). Since the quantized RSMA precoder is required to consider both quantization error and the minimum rate of the common stream, optimizing the RSMA precoder is highly challenging for maximizing the sum spectral efficiency (SE). Addressing these difficulties, we introduce a new promising quantized RSMA pre coding algorithm aimed at maximizing the sum SE. To achieve a more tractable form, we first approximate the rate of the common stream with a smooth function. Subsequently, we derive the first-order optimality condition, which is cast as a nonlinear eigenvalue problem (NEP). Accordingly, we introduce a promising algorithm that can find the principal eigenvector of the NEP, which corresponds to the best local optimal solution. Numerous simulation results demonstrate that the advantages of RSMA in quantized multiuser MIMO systems are present in the proposed method.

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.

Transmitter channel state information (CSIT) is indispensable for the spectral efficiency gains offered by massive multiple-input multiple-output (MIMO) systems. In a frequency-division-duplexing (FDD) massive MIMO system, CSIT is typically acquired through downlink channel estimation and user feedback, but as the number of antennas increases, the over-head for CSI training and feedback per user grows, leading to a decrease in spectral efficiency. In this paper, we show that, using uplink pilots in FDD, the downlink sum spectral efficiency gain with perfect downlink CSIT is achievable when the number of antennas at a base station is infinite by leveraging the partial channel reciprocity between uplink and downlink channels. Specifically, the key idea showing our result is the mean squared error-optimal downlink channel reconstruction method using uplink pilots, and the robust downlink precoding method harnessing the reconstructed channel with the error covariance matrix. Our simulation results show that our proposed precoding method can attain comparable sum spectral efficiency to zero-forcing precoding with perfect downlink CSIT, without CSI training and feedback.

In this paper, we investigate the coverage performance of downlink satellite networks employing dynamic coordinated beamforming. Our approach involves modeling the spatial arrangement of satellites and users using Poisson point processes situated on concentric spheres. We derive an analytical expression for the coverage probability which is formulated in terms of various parameters, including the number of antennas per satellite, satellite density, fading characteristics, and path-loss exponent. This coverage probability validates the advantages of coordinated beamforming from a spatial average perspective. Our primary finding is that dynamic coordinated beamforming significantly improves coverage compared to the absence of satellite coordination, in proportion to the number of antennas on each satellite. Moreover, we observe that the optimal cluster size, which maximizes the ergodic spectral efficiency, increases with higher satellite density, provided that the number of antennas on the satellites is sufficiently large. To offer a more intuitive understanding, we also develop an approximation for the coverage probability. Furthermore, by considering the in-cluster geometry of the coordinated satellite set, we derive an approximate coverage probability conditioned on in-cluster geometry. Our findings are corroborated by simulation results, confirming the accuracy of the derived expressions.

The joint design of secure precoding and artificial noise (AN) transmission scheme is promising to improve secrecy performance. However, in downlink multi-user multiple-input multiple-output (MU-MIMO) systems with multiple eavesdroppers, joint design of secure precoding and AN structure involves several challenges: an objective function is non-convex and non-smooth, and a precoding matrix and AN matrix have different design principles. Classically, to jointly design precoding and AN covariance matrix, an alternating optimization approach is used which has limitations in terms of the secrecy rate performance since it does not offer joint and simultaneous optimization of the precoding and AN covariance matrices. In this paper, we propose a novel optimization framework that optimizes the precoder and the AN covariance matrix jointly and simultaneously to maximize the secrecy rate. First, we approximate the objective function to a tractable non-convex form. Next, we derive the first-order optimality condition by leveraging the nonlinear eigenvalue problem (NEP) form. Finally, we utilize an efficient technique with low computational complexity for identifying the major eigenvector of the NEP which corresponds to the best stationary point. Simulations illustrate that the proposed methods enhance the secrecy rate performance compared to the existing secure precoding methods.

Satellite communications have been promising to guarantee global coverage with high capacity. In this paper, we analyze coverage performance of satellite networks with a distance-dependent line-of-sight (LOS) and non-LOS (NLOS) channel propagation probability to incorporate shadowing effect. Extending the stochastic geometry-based network analysis for terrestrial networks, we model the satellite network and users as a Poisson point process and derive an theoretical coverage probability expression to provide analytical understanding of the satellite network. Simulation results verify the exactness of the derived expression. The derived expression includes network parameters for satellite density and altitude, channel fading, pathloss, and the LOS probability, and provides insights on satel-lite networks. Our key finding is that NLOS channel propagation benefits the coverage performance by reducing the interference from non-associated satellites, and the higher NLOS probability is desirable to improve the coverage performance as the network becomes denser.

The need for channel state information (CSIT) is crucial for the improved spectral efficiency of massive multiple-input multiple-output (MIMO) systems. In FDD massive MIMO systems, CSIT is obtained through downlink channel estimation and user feedback, but this process becomes challenging as the number of antennas increases, resulting in reduced spectral efficiency. In this paper, we show that even in FDD systems, the TDD massive MIMO gain is attainable without explicit CSIT training and feedback, by using UL pilots. We present a novel DL channel reconstruction method from uplink pilots and a robust downlink precoding technique, proving that the FDD massive MIMO gains are achievable without CSI training and feedback. Our results are verified through system-level simulations.

In this paper, we study a max-min fairness (MMF) beamforming design method in a multiple-user downlink network utilizing rate-splitting multiple access (RSMA). A main objective of the MMF problem is to provide users with uniformly good rates by appropriately adjusting the rates of common and private message, respectively. Since the optimization of the objective function with respect to beamforming vectors and common message rates for each users is intricate and difficult to solve, we divide a whole problem into two stages. In the first stage, we utilize the LogSumExp (LSE) approximation technique and the novel generalized power iteration (GPI) framework to find a beamforming vector given a common message portion. In the second stage, we allocate common message rate for each user for given fixed beamforming vectors. By repeating these two stages, we jointly design the optimal beamforming vectors and common message rates for each users. Through simulation, we show that our method outperforms existing approaches in terms of the worst-case user's rate with extremely low complexity cost.

In this paper, we consider a sum secrecy spectral efficiency (SE) maximization problem in a downlink rate-splitting multiple access (RSMA) system with multiple antennas. We also assume two types of users: secret users whose private streams require information security and normal users whose private streams do not require security. Due to its max and min operations in the wiretap SE and the SE of the common stream, respectively, which makes the non-convex problem even non-smooth, solving the problem is highly challenging. To deal with the difficulties, we reformulate the optimization problem into an approximated smooth problem using a LogSumExp approach. Then, we identify the first-order optimality condition and develop formulas into a generalized eigenvalue problem. We use a power iteration based algorithm to find the best local optimal solution. Simulations validate the proposed secure RSMA precoding method.

In this article, we propose a precoding design to maximize the sum achievable rate in downlink nonorthogonal multiple access (NOMA)-aided short-packet Internet of Things (IoT) communications, wherein each IoT device is equipped with low-resolution analog-to-digital converters (ADCs). Due to intertwined effects caused from low-resolution ADCs, short packets, and NOMA, it is challenging to find efficient precoding vectors. To resolve the difficulties, we first linearize quantization distortion by adopting an additive quantization noise model. Thereafter, we approximate nonsmooth functions by using a LogSumExp technique. With the transformed problem, we derive a first-order optimality condition and propose a novel precoding algorithm which identifies an efficient local optimal solution with low complexity. Based on the proposed algorithm, we also investigate an efficient NOMA decoding ordering method for the considered system. Via simulations, we demonstrate that the proposed method outperforms other baseline methods.

Secure precoding superimposed with artificial noise (AN) is a promising transmission technique to improve security by harnessing the superposition nature of the wireless medium. However, finding a jointly optimal precoding and AN structure is very challenging in downlink multi-user multiple-input multiple-output wiretap channels with multiple eavesdroppers. The major challenge in maximizing the secrecy rate arises from the non-convexity and non-smoothness of the rate function. Traditionally, an alternating optimization framework that identifies beamforming vectors and AN covariance matrix has been adopted; yet this alternating approach has limitations in maximizing the secrecy rate. In this paper, we put forth a novel secure precoding algorithm that jointly and simultaneously optimizes the beams and AN covariance matrix for maximizing the secrecy rate when a transmitter has either perfect or partial channel knowledge of eavesdroppers. To this end, we first establish an approximate secrecy rate in a smooth function. Then, we derive the first-order optimality condition in the form of the nonlinear eigenvalue problem (NEP). We present a computationally efficient algorithm to identify the principal eigenvector of the NEP as a suboptimal solution for secure precoding. Simulations demonstrate that the proposed methods improve secrecy rate significantly compared to the existing methods.

Low-rate and short-packet transmissions are important for ultra-reliable low-latency communications (URLLC). In this paper, we put forth a new family of sparse superposition codes for URLLC, called block orthogonal sparse superposition (BOSS) codes. We first present a code construction method for the efficient encoding of BOSS codes. The key idea is to construct codewords by the superposition of the orthogonal columns of a dictionary matrix with a sequential bit mapping strategy. We also propose an approximate maximum a posteriori probability (MAP) decoder with two stages. The approximate MAP decoder reduces the decoding latency significantly via a parallel decoding structure while maintaining a comparable decoding complexity to the successive cancellation list (SCL) decoder of polar codes. Furthermore, to gauge the code performance in the finite-blocklength regime, we derive an exact analytical expression for block-error rates (BLERs) of single-layered BOSS codes in terms of relevant code parameters. Lastly, we present a cyclic redundancy check aided-BOSS (CA-BOSS) code with simple list decoding to boost the code performance. Our experiments verify that CA-BOSS codes with the simple list decoder outperform CA-polar codes with SCL decoding in the low-rate and finite-blocklength regimes while achieving the finite-blocklength capacity upper bound within one dB of signal-to-noise ratio.

To realize ultra-reliable low latency communications with high spectral efficiency and security, we investigate a joint optimization problem for downlink communications with multiple users and eavesdroppers in the finite blocklength (FBL) regime. We formulate a multi-objective optimization problem to maximize a sum secrecy rate by developing a secure precoder and to minimize a maximum error probability and information leakage rate. The main challenges arise from the complicated multi-objective problem, non-tractable back-off factors from the FBL assumption, non-convexity and non-smoothness of the secrecy rate, and the intertwined optimization variables. To address these challenges, we adopt an alternating optimization approach by decomposing the problem into two phases: secure precoding design, and maximum error probability and information leakage rate minimization. In the first phase, we obtain a lower bound of the secrecy rate and derive a first-order Karush-Kuhn-Tucker (KKT) condition to identify local optimal solutions with respect to the precoders. Interpreting the condition as a generalized eigenvalue problem, we solve the problem by using a power iteration-based method. In the second phase, we adopt a weighted-sum approach and derive KKT conditions in terms of the error probabilities and leakage rates for given precoders. Simulations validate the proposed algorithm.

Satellite networks are promising to provide ubiquitous and high-capacity global wireless connectivity. Traditionally, satellite networks are modeled by placing satellites on a grid of multiple circular orbit geometries. Such a network model, however, requires intricate system-level simulations to evaluate coverage performance, and analytical understanding of the satellite network is limited. Continuing the success of stochastic geometry in a tractable analysis for terrestrial networks, in this paper, we develop novel models that are tractable for the coverage analysis of satellite networks using stochastic geometry. By modeling the locations of satellites and users using Poisson point processes on the surfaces of concentric spheres, we characterize analytical expressions for the coverage probability of a typical downlink user as a function of relevant parameters, including path-loss exponent, satellite height, density, and Nakagami fading parameter. Then, we also derive a tight lower bound of the coverage probability in tractable expression while keeping full generality. Leveraging the derived expression, we identify the optimal density of satellites in terms of the height and the path-loss exponent. Our key finding is that the optimal average number of satellites decreases logarithmically with the satellite height to maximize the coverage performance. Simulation results verify the exactness of the derived expressions.

Rate-splitting multiple access (RSMA) is a general multiple access scheme for downlink multi-antenna systems embracing both classical spatial division multiple access and more recent non-orthogonal multiple access. Finding a linear precoding strategy that maximizes the sum spectral efficiency of RSMA is a challenging yet significant problem. In this paper, we put forth a novel precoder design framework that jointly finds the linear precoders for the common and private messages for RSMA. Our approach is first to approximate the non-smooth minimum function part in the sum spectral efficiency of RSMA using a LogSumExp technique. Then, we reformulate the sum spectral efficiency maximization problem as a form of the log-sum of Rayleigh quotients to convert it into a tractable form. By interpreting the first-order optimality condition of the reformulated problem as an eigenvector-dependent nonlinear eigenvalue problem, we reveal that the leading eigenvector of the derived optimality condition is a local optimal solution. To find the leading eigenvector, we propose an algorithm inspired by a power iteration. Simulation results show that the proposed RSMA transmission strategy provides significant improvement in the sum spectral efficiency compared to the state-of-the-art RSMA transmission methods.

This paper investigates the sum spectral efficiency maximization problem in downlink multiuser multiple-input multiple-output systems with low-resolution quantizers at an access point (AP) and users. We consider rate-splitting multiple access (RSMA) to enhance spectral efficiency by offering opportunities to boost achievable degree-of-freedom. Optimizing RSMA precoders, however, is highly challenging due to the minimum rate constraint when determining the common rate. The quantization errors coupled with the precoders make the problem more complicated. In this paper, we develop a novel RSMA precoding algorithm incorporating quantization errors for maximizing the sum spectral efficiency. To this end, we first obtain an approximate spectral efficiency in a smooth function. Subsequently, we derive the first-order optimality condition in the form of the nonlinear eigenvalue problem (NEP). We propose a computationally efficient algorithm to find the principal eigenvector of the NEP as a sub-optimal solution. We also extend the weighted minimum mean square error-based RSMA precoding to the considered quantization system. Simulation results validate the proposed methods. The key benefit of using RSMA over spatial division multiple access (SDMA) comes from the ability of the common stream to balance between the channel gain and quantization error in multiuser MIMO systems with different quantization resolutions.

In this paper, we consider a downlink internet-of-things (IoT) multiple-input multiple-output (MIMO) network wherein an access point (AP), multiple IoT users, and a single eavesdropper coexist. The eavesdropper attempts to wiretap confidential messages of the IoT users. In the considered system, we solve a sum secrecy rate maximization problem in the finite blocklength (FBL) regime. Due to the FBL, the secrecy rate has a back-off factor with respect to blocklength, decoding error probability, and information leakage, which makes the problem more challenging. The main challenges are: i) the problem is not tractable because of the back-off factor, ii) an objective function is inherently non-convex, and iii) information leakage by the eavesdropper needs to be considered. To address these difficulties, we first obtain a lower bound of the secrecy rate and transform the problem into a product of Rayleigh quotients form. Then, we derive a first-order Karush–Kuhn–Tucker (KKT) condition to find a local optimal solution and interpret the condition as a generalized eigenvalue problem. Consequently, we develop a low-complexity algorithm by adopting a generalized power iteration-based (GPI) method. Via simulations, we validate the secrecy rate performance of the proposed method for the short-packet IoT communication systems.

In this paper, we propose a precoding method to maximize energy efficiency (EE) in a downlink multiuser massive multiple-input multiple-output system with low-resolution quantizers. To this end, we formulate an EE maximization problem with respect to precoders by incorporating the quantization errors caused by the low-resolution quantizers. The main challenges exist: i) the quantization errors are entangled with the precoders, ii) a objective function is non-convex, and iii) unlike a spectral efficiency (SE) maximization problem, a precoding power needs to be jointly optimized. To address these challenges, we first adopt a Dinkenbach method and reformulate the EE problem to a more tractable form. We further decompose the problem into an optimal precoding direction and transmit power problems. To find the optimal direction, we derive a first-order Karush–Kuhn–Tucker (KKT) condition and interpret the condition as a generalized eigenvalue problem. Accordingly, adopting a generalized power iteration-based precoding method, we find the principal eigenvector which is the best sub-optimal precoder. Regarding the transmit power optimization, the objective function becomes concave for given other variables. Hence, the transmit power level is optimized by using a gradient descent method. Via simulations, we demonstrate that the proposed algorithm provides the highest EE performance compared to baseline methods.

Physical layer security is a useful tool to prevent illegal wiretapping to confidential information. In this paper, we consider a generalized model of conventional physical layer security, referred as hierarchical information accessibility (HIA). A main feature of the HIA model is that a network has a hierarchy in information access, wherein decoding feasibility is determined by each user’s priority. Under this HIA model, we formulate a sum secrecy rate maximization problem with regard to precoding vectors. This problem is challenging since multiple non-smooth functions are involved into the secrecy rate to fulfill the HIA conditions and also the problem is non-convex. To address the challenges, we approximate the minimum function by using the LogSumExp technique, thereafter obtain the first-order optimality condition. One key observation is that the derived condition is cast as a functional eigenvalue problem, where the eigenvalue is equivalent to the approximated objective function of the formulated problem. Accordingly, we show that finding a principal eigenvector is equivalent to finding a local optimal solution. To this end, we develop a novel method called generalized power iteration for HIA (GPI-HIA). Simulations demonstrate that the GPI-HIA significantly outperforms other baseline methods in terms of the secrecy rate.

The use of low-resolution digital-to-analog and analog-to-digital converters (DACs and ADCs) significantly benefits energy efficiency (EE) at the cost of high quantization noise for massive multiple-input multiple-output (MIMO) systems. This paper considers a precoding optimization problem for maximizing EE in quantized downlink massive MIMO systems. To this end, we jointly optimize an active antenna set, precoding vectors, and allocated power; yet acquiring such joint optimal solution is challenging. To resolve this challenge, we decompose the problem into precoding direction and power optimization problems. For precoding direction, we characterize the first-order optimality condition, which entails the effects of quantization distortion and antenna selection. We cast the derived condition as a functional eigenvalue problem, wherein finding the principal eigenvector attains the best local optimal point. To this end, we propose generalized power iteration based algorithm. To optimize precoding power for given precoding direction, we adopt a gradient descent algorithm for the EE maximization. Alternating these two methods, our algorithm identifies a joint solution of the active antenna set, the precoding direction, and allocated power. In simulations, the proposed methods provide considerable performance gains. Our results suggest that a few-bit DACs are sufficient for achieving high EE in massive MIMO systems.

A cloud radio access network (C-RAN) is a promising cellular network, wherein densely deployed multi-antenna remote-radio-heads (RRHs) jointly serve many users using the same time-frequency resource. By extremely high signaling overheads for both channel state information (CSI) acquisition and data sharing at a baseband unit (BBU), finding a joint transmission strategy with a significantly reduced signaling overhead is indispensable to achieve the cooperation gain in practical C-RANs. In this paper, we present a novel sparse joint transmission (sparse-JT) method for C-RANs, where the number of transmit antennas per unit area is much larger than the active downlink user density. Considering the effects of noisy-and-incomplete CSI and the quantization errors in data sharing by a finite-rate fronthaul capacity, the key innovation of sparse-JT is to find a joint solution for cooperative RRH clusters, beamforming vectors, and power allocation to maximize a lower bound of the sum-spectral efficiency under the sparsity constraint of active RRHs. To find such a solution, we present a computationally efficient algorithm that guarantees to find a local-optimal solution for a relaxed sum-spectral efficiency maximization problem. By system-level simulations, we exhibit that sparse-JT provides significant gains in ergodic spectral efficiencies compared to existing joint transmissions.

In both academia and industry, multi-user multiple-input single-output (MU-MISO) techniques have shown enormous gains in spectral efficiency by exploiting spatial degrees of freedom. So far, an underlying assumption in most of the existing MU-MISO design has been that all the users use infinite blocklength, so that they can achieve the Shannon capacity. This setup, however, is not suitable considering delay-constrained users whose blocklength tends to be finite. In this paper, we consider a heterogeneous setting in MU-MISO systems where delay-constrained users and delay-tolerant users coexist, called a DCTU-MISO network. To maximize the sum spectral efficiency in this system, we present the spectral efficiency for delay-tolerant users and provide a lower bound of the spectral efficiency for delay-constrained users. We consider an optimization problem that maximizes the sum spectral efficiency of delay-tolerant users while satisfying the latency constraint of delay-constrained users, and propose a generalized power iteration (GPI) precoding algorithm that finds a principal precoding vector. Furthermore, we extend a DCTU-MISO network to the multiple time slots scenario and propose a recursive GPI precoding algorithm. In simulation results, we validate proposed methods outperform baseline schemes and present the effect of network parameters on the average sum spectral efficiency.

In this paper, we consider a hierarchical information accessibility (HIA) model, which generalizes conventional physical layer security. In the considered model, multiple layers with different security priorities are assumed, where only the users in a higher priority layer are permitted to decode the message intended to lower priority layers. To maximize the sum secrecy rate of the considered system, we formulate an optimization problem with regard to precoders. To solve the formulated problem, we first approximate the objective function by using the LogSumExp technique and show that finding a local optimum is equivalent to finding a leading eigenvector of the first-order optimality condition of the reformulated problem. Accordingly, we propose a novel algorithm called generalized power iteration for hierarchical information accessibility (GPI-HIA) to obtain a solution. Via simulations, we demonstrate that the proposed method significantly outperforms other baseline schemes under the considered HIA scenario.

This paper introduces block orthogonal sparse su-perposition (BOSS) codes for efficient short-packet commu-nications over Gaussian channels. Unlike conventional sparse superposition codes, an encoder of BOSS code uses multiple unitary matrices as a fat dictionary matrix and maps information bits such that multiple subgroups of codewords are orthogonal. Exploiting this orthogonal property per group, a two-stage maximum a posteriori (MAP) decoding algorithm is presented. The key idea of the two-stage MAP decoder is to successively estimate the non-zero alphabets corresponding to the orthogonal columns in a dictionary matrix and the index of a sub-dictionary matrix containing the columns. This decoding algorithm achieves a near-optimal decoding performance while requiring polynomial time complexity in blocklength. Via simulations, we show that the proposed encoding and decoding techniques achieve enhanced block-error-rate performances in the short blocklength regime compared to the state-of-the-art coded modulation methods.

In both academia and industry, the multiple-input multiple-output (MIMO) techniques have shown enormous gains in spectral efficiency by exploiting the spatial degrees of freedom. So far, an underlying assumption in most of existing multi-user multiple-input multiple-output (MU-MIMO) design has been that all the users in the system use infinite blocklength, so that they can achieve the Shannon capacity. This setup, however, is not suitable considering the presence of delay-constrained users whose the blocklength tends to be finite. In this paper, we consider a heterogeneous setting in a MU-MEMO system where delay tolerant users and delay constrained users coexist. To maximize the sum spectral efficiency in this system, we first present the spectral efficiencies for delay-tolerant user and delay-constrained user as the Rayleigh quotient. We then derive a first-order optimality condition of the optimization problem that maximizes the sum spectral efficiency of total users and satisfies the latency requirement of delay-constrained users, and propose the generalized power iteration preceding algorithm. In the simulation results, we prove that the proposed method outperforms baseline schemes.

In this article, we consider a multiple-input–multiple-output (MIMO) system serving Internet of Things (IoT) devices. To satisfy stringent requirements on the latency of IoT communications, the IoT devices communicate in the finite blocklength regime, wherein the achievable spectral efficiency (SE) has a backoff factor and decoding error probability is nonnegligible. Aiming to jointly optimize the sum SE and the maximum error probability, we first express the achievable SE as a function of the channel coefficients, precoders, and error probabilities. Subsequently, we formulate a problem with respect to precoders and error probabilities. A straightforward approach for the formulated problem, however, is challenging as it not only has multiple objectives but also is nonconvex. To resolve these issues, we first transform the problem as a single-objective optimization by using a weighted sum approach. Based on the reformulation, we propose an algorithm, in which error probabilities and precoders are determined by alternating two phases. Via simulations, we demonstrate that the proposed method offers significant gains compared to baseline methods, in terms of the achievable SE and the maximum error probability. In particular, we show that the communication latency is greatly reduced by using the proposed method.

In this paper, we present a new approach for beam alignment in millimeter wave high speed train systems. The core idea of our approach is using Bayesian multi-armed bandit in the beam search process. In the proposed method, by approximating the signal on each beam direction as a Gaussian random variable, we update the posterior mean and variance once the beam direction is searched. With the obtained posterior mean and variance, we compute the upper and the lower confidence bounds, by which we identify the beam candidate to be searched in the next time. By selecting one whose confidence gap is large, we explore the beam direction that includes large amount of uncertainty, i.e., not much explored yet. Using this method, we also analyze the regret bound. Via simulation, we demonstrate the proposed Bayesian bandit learning approach provides better performance compared to other methods in terms of the beam alignment probability.

We characterize the ergodic spectral efficiency of a cooperative type of K-tier heterogeneous networks (HetNets) with limited feedback. Specifically, a base station (BS) coordination set is formed by using dynamic clustering across the tiers, wherein the intra-cluster interference is mitigated by using multi-cell zero-forcing based on limited feedback. Modeling the network based on stochastic geometry, we derive analytical expressions for the ergodic spectral efficiency as a function of the system parameters. Leveraging the obtained expression, we formulate a feedback allocation problem and obtain a solution to improve the ergodic spectral efficiency. Simulations show the spectral efficiency improvement by using the proposed feedback allocation. One major finding in the obtained solution is that allocating more feedback to stronger intra-cluster BSs is efficient.

We characterize the detection probability of blockage sensing by radars deployed on towers in cellular networks. If the signal-to-interference ratio of the reflected pilot signal is larger than the predefined threshold and there is no other blockage between the radar and the corresponding blockage, the radar successfully detects the blockage. Modeling radar and blockage locations using stochastic geometry, we derive the detection probability as a function of the system parameters, chiefly the radar and blockage densities. Leveraging the obtained expression, we provide some guidelines for efficiently deploying radars to enhance the detection probability.

We characterize the ergodic spectral efficiency of a non-cooperative and a cooperative type ofK-tier heterogeneous network with limited feedback. In the non-cooperative case, a multi-antenna base station (BS) serves a single-antenna user using maximum-ratio transmission based on limited feedback. In the cooperative case, a BS coordination set is formed by using dynamic clustering across the tiers, wherein the intra-cluster interference is mitigated by using multi cell zero-forcing also based on limited feedback. Modeling the network based on stochastic geometry, we derive analytical expressions for the ergodic spectral efficiency as a function of the system parameters. Leveraging the obtained expressions, we formulate feedback partition problems and obtain solutions to improve the ergodic spectral efficiency. Simulations show the spectral efficiency improvement by using the obtained feedback partitions. Our major findings are as follows: 1) in the non-cooperative case, the feedback is only useful in a particular tier if the mean interference is small enough; 2) in the cooperative case, allocating more feedback to stronger intra-cluster BSs is efficient; and 3) in both cases, the obtained solutions do not change depending on the instantaneous signal-to-interference ratio.

Hybrid analog/digital precoding in the downlink of multiuser massive MIMO systems can reduce the number of RF chains hence reducing total cost and improving power efficiency. Having few RF chains, however, makes it difficult for a base station to acquire instantaneous channel state information across all antennas. We develop a hybrid technique that uses only long-term (slowly changing) channel statistics in computing the analog precoding matrix. The proposed analog precoder is designed to maximize signal-to-leakage-plus-noise ratio (SLNR) when combined with a baseband precoder. We also propose a constrained precoder design that reduces the effect of a hardware constraint where the analog precoders are realized with phase shifters.

Millimeter wave (mmWave) cellular systems will enable gigabit-per-second data rates due to the large bandwidth available at mmWave frequencies. Thanks to the small wavelength corresponding to the mmWave frequencies, mmWave systems can benefit from exploiting large antenna arrays at both transmitter and receiver. Although highly directional beamforming has been envisioned to play a key role to realize sufficient link margin, it is also possible to use these large arrays in other ways. For example, a hybrid array architecture can be exploited to either cancel or null an interferer. In this paper we analyse the effect of interference cancellation in downlink mmWave communications. We exploit partial zero forcing (PZF) at the user in order to cancel the interference from a set of interfering base stations (BSs) and derive closed form expression for the probability of coverage. Simulation results show that as the density of base stations increases, interference mitigation through partial zero forcing enhances the probability of coverage implying the necessity of interference mitigation in dense mmWave networks.

We propose a new mixed-analog-to-digital convertor (mixed-ADC) architecture for cloud-RAN (C-RAN) systems. The RRH is equipped with a mixed-ADC pool that includes multiple ADC units with various resolutions. In this pool, the RRH selects the appropriate ADCs and connects the selected ADCs to each antenna to quantize the received signals, thereby each antenna can have a different resolution ADC. The quantized signals are sent to a centralized baseband unit (BBU) via a capacity limited fronthaul pipe. To maximize the spectral efficiency of the considered system in a single-user uplink phase, we formulate an optimization problem for ADC selection by exploiting an approximation of the generalized mutual information (GMI). Subsequently we propose a solution. The simulations show the improvement in the GMI by using the proposed ADC selection. Our major findings are: i) In a C-RAN with limited fronthaul capacity, the proposed mixed-ADC makes more efficient use of the fronthaul capacity. ii) In selecting ADCs, assigning a high resolution ADC to a strong channel is beneficial.

We propose a mixed analog-to-digital converter ADC (mixed-ADC) structure for a cloud-RAN system, where a single-antenna user terminal communicates with a multi-antenna remote radio head (RRH). In the proposed structure, the RRH is equipped with a mixed-ADC pool that includes multiple ADC units with various resolutions. In this pool, the RRH selects the appropriate ADCs and connects the selected ADCs to each antenna to quantize the received signals; thereby each antenna can have a different resolution ADC. The fronthaul capacity is limited, so that the sum of the bits produced in the selected ADCs is also limited. To maximize the spectral efficiency or the energy efficiency of such a system, we propose algorithms for ADC resolution selection based on an approximation of the generalized mutual information in the low signal-to-noise regime. In the proposed algorithms, we show that for spectral efficiency, using high-resolution ADC on the strong channels is beneficial. The results for energy efficiency maximization are similar, though the largest resolutions are reduced to save power. The simulations show that the proposed method provides significant performance improvement.

We consider a multi-antenna downlink cellular network using either single-user maximal ratio transmission (MRT) or multi-user zero-forcing (ZF) transmission. The locations of the base stations are modeled by a Poisson point process to allow the inter-cell interference to be tractably analyzed. A tight lower bound on the optimum number of feedback bits maximizing the net spectral efficiency is derived, whereby the cost of feedback sent via uplink is subtracted from the corresponding gain in downlink spectral efficiency. When using MRT, the optimum number of feedback bits is shown to scale linearly with the number of antennas, and logarithmically with the channel coherence time. With ZF, the optimum amount of feedback scales the same as with MRT, but additionally also increases linearly with the pathloss exponent.

This paper proposes a method for designing base station (BS) clusters and cluster patterns for pair-wise BS coordination. The key idea is that each BS cluster is formed by using the second-order Voronoi region, and the BS clusters are assigned to a specific cluster pattern by using edge-coloring for a graph drawn by Delaunay triangulation. The main advantage of the proposed method is that the BS selection conflict problem is prevented, while users are guaranteed to communicate with their two closest BSs in any irregular BS topology. With the proposed coordination method, analytical expressions for the rate distribution and the ergodic spectral efficiency are derived as a function of relevant system parameters in a fixed irregular network model. In a random network model with a homogeneous Poisson point process, a lower bound on the ergodic spectral efficiency is characterized. Through system level simulations, the performance of the proposed method is compared with that of conventional coordination methods: dynamic clustering and static clustering. Our major finding is that, when users are dense enough in a network, the proposed method provides the same level of coordination benefit with dynamic clustering to edge users.

We propose a low complexity antenna selection algorithm for low target rate users in cloud radio access networks. The algorithm consists of two phases. In the first phase, each remote radio head (RRH) determines whether to be included in a candidate set by using a predefined selection threshold. In the second phase, RRHs are randomly selected within the candidate set made in the first phase. To analyze the performance of the proposed algorithm, we model RRHs' and users' locations by a homogeneous Poisson point process, whereby the signal-to-interference ratio (SIR) complementary cumulative distribution function is derived. By approximating the derived expression, an approximate optimum selection threshold that maximizes the SIR coverage probability is obtained. Using the obtained threshold, we characterize the performance of the algorithm in an asymptotic regime, where the RRH density goes to infinity. The obtained threshold is then modified depending on various algorithm options. A distinguishable feature of the proposed algorithm is that the algorithm complexity keeps constant independent to the RRH density, so that a user is able to connect to a network without heavy computation at baseband units.

We consider a downlink cellular network where multi-antenna base stations (BSs) transmit data to single-antenna users by using one of two linear precoding methods with limited feedback: 1) maximum ratio transmission (MRT) for serving a single user or 2) zero forcing (ZF) for serving multiple users. The BS and user locations are drawn from a Poisson point process, allowing expressions for the signal-to-interference coverage probability and the ergodic spectral efficiency to be derived as a function of system parameters, such as the number of BS antennas and feedback bits, and the pathloss exponent. We find a tight lower bound on the optimum number of feedback bits to maximize the net spectral efficiency, which captures the overall system gain by considering both of downlink and uplink spectral efficiency using limited feedback. Our main finding is that, when using MRT, the optimum number of feedback bits scales linearly with the number of antennas, and logarithmically with the channel coherence time. When using ZF, the feedback scales in the same ways as MRT, but also linearly with the pathloss exponent. The derived results provide system-level insights into the preferred channel codebook size by averaging the effects of short-term fading and long-term pathloss.

In this paper, we propose a threshold-based antenna selection algorithm for uplink dense cloud radio access networks (C- RANs), where a baseband processor unit (BBU) cloud is separately placed from densely deployed remote radio heads (RRHs). The proposed algorithm consists of two phases. In the first phase, each RRH determines whether to be included or not in a candidate set by using a predefined selection threshold. In the second phase, a RRH is randomly selected within the candidate set made in the first phase. By modeling the network with a homogeneous Poisson point process (PPP), the signal- to-interference ratio (SIR) complementary cumulative distribution function (CCDF) is derived when applying the proposed algorithm. Exploiting the derived expression, an approximate optimum selection threshold that maximizes the SIR coverage performance is obtained in terms of relevant system parameters, chiefly the SIR target, the pathloss exponent, and the RRH and user densities. Simulations demonstrate the performance of the obtained selection threshold. The key advantage of the proposed algorithm is its simplicity. Since the complexity caused by selecting a RRH is reasonable irrespective of the density of the RRH while guaranteeing the certain performance, the proposed algorithm is easily applied in a C-RAN where the RRHs are densely deployed.