Low Earth orbit (LEO) satellites are a promising technology for providing low-latency, high-data-rate, and wide-coverage communication services. However, with growing demand for data transmission, future non-terrestrial networks (NTNs) require high spectral efficiency especially with low-gain antennas at the ground devices. This motivates the adoption of in-band full-duplex (FD) systems. In addition, the potential imbalance between downlink (DL) and uplink (UL) transmissions necessitates flexibility in resource allocation. To overcome these challenges, we propose an FD LEO satellite system, where the non-reciprocal beyond-diagonal reconfigurable intelligent surfaces (NR-BD-RIS) and multiple transmit and receive antennas are attached to the LEO satellite. NR-BD-RIS reflects the DL and UL signals by passive beamforming. By incorporating non-reciprocal components into the impedance network of RIS, the NR-BD-RIS breaks channel reciprocity, facilitating simultaneous support for multiple beam directions. To cover a wide coverage, we propose a time-sharing scheduling framework in which the NR-BD-RIS simultaneously serves multiple DL and multiple UL ground devices within each time slot. An optimization problem is defined to maximize the weighted sum-rate over the entire scheduling period. Numerical results demonstrate that the proposed NR-BD-RIS significantly performs better than both conventional BD-RIS and diagonal RIS (D-RIS) with respect to DL and UL sum-rate performance under both single-user (SU) and multiple-user (MU) cases. Additionally, NR-BD-RIS requires less frequent reconfiguration compared to the other two types of RIS, making it more practical for implementation.

Vector Quantization (VQ)-based digital semantic communication is gaining prominence due to its compatibility with existing digital systems. However, it faces a critical trade-off between computational efficiency and the risk of codeword overfitting. To address this challenge, we propose ResUME, a novel digital semantic communication framework designed for both rate-controllability and computational efficiency. ResUME integrates residual vector quantization (RVQ) with unequal modulation to achieve robust and flexible encoding of latent representations. Our framework leverages the hierarchical structure of RVQ, where layers encode information of varying significance. We analyze this property from an information-theoretic perspective to optimize the modulation order. This optimization provides a form of unequal error protection, maximizing mutual information under spectral efficiency constraints. Furthermore, to mitigate the risk of overfitting, we introduce a variational loss function inspired by the information bottleneck principle. Experimental results demonstrate that ResUME significantly outperforms conventional modulation-assisted methods across various AWGN channel conditions. An ablation study further validates the contribution of each component in our proposed loss function.

Dual-polarization is a promising strategy to enhance spectral efficiency by enabling frequency reuse across orthogonal polarization domains. In this work, we address the beamforming design problem for mixed dual-polarized integrated satellite-terrestrial networks (MDP-ISTN), in which circularly polarized satellite links coexist with linearly polarized terrestrial sub-networks. To effectively manage both inter- and intra-network interference, we propose an advanced rate-splitting multiple access (RSMA) framework that integrates inter-network rate-splitting (RS) with a super-common message, initially decodable by all users, alongside intra-network RS. The beamforming vector is determined using generalized mutual information to ensure robustness against satellite beam-pointing errors and channel state information (CSI) uncertainty. Our MDP-ISTN model explicitly incorporates polarization mismatches induced by beam-pointing misalignment and Faraday rotation. Simulation results under realistic MDP-ISTN conditions demonstrate the superiority of the proposed RSMA-based scheme.

Low Earth Orbit (LEO) satellite communications (SATCOM) offer high-throughput, low-latency global connectivity to a large number of users. To accommodate this demand with limited hardware resources, beam hopping (BH) has emerged as a prominent approach in LEO SATCOM; however, its time-domain switching mechanism confines coverage to a small fraction of the service area during each time slot, exacerbating uplink throughput bottlenecks and latency issues as the user density increases. Meanwhile, wideband systems experience the beam-squint effect, where analog beamforming (BF) directions vary with subcarrier frequencies, hindering the performance of wideband SATCOM. In this paper, we put forth 3D rainbow BF, employing a joint phase-time array (JPTA) antenna with true time delay (TTD) to intentionally widen the beam-squint angle, steering frequency-dependent beams toward distributed directions. This novel approach enables the satellite to serve its entire coverage area in a single time slot. By doing so, the satellite simultaneously receives uplink signals from a massive number of users, significantly boosting throughput and reducing latency. To realize 3D rainbow BF, we formulate a JPTA beamformer optimization problem and address the non-convex nature of the optimization problem through a novel joint alternating and decomposition-based optimization framework. Through numerical evaluations, we demonstrate that the proposed rainbow BF-empowered LEO SATCOM achieves up to 3.8-fold increase in uplink throughput compared to conventional BH systems.

Integrated Terrestrial-Satellite Backhaul Networks (ITSBN) offer wide coverage and cost effectiveness, especially in remote or underserved areas, where backhaul links are facilitated through a multi-beam mechanism. In this context, a Beam Hopping Plan (BHP) can enhance resource efficiency by dynamically activating beams based on traffic demand, improving coverage flexibility. However, despite its several advantages, ITSBN may encounter congestion challenges during periods of high traffic demand. Content caching may be a solution to alleviate the network load, enabling content delivery at the network edge and reducing backhaul traffic. In this work, we consider ITSBN with caching at local base stations (BSs) and aim to maximize the system throughput. To achieve this, we propose a double-layer iterative algorithm. The outer layer employs our proposed$\varepsilon$-Beam Hopping Plan and Content Selection algorithm ($\varepsilon$-BHPCS), leveraging the matching game approach. This method jointly designs the BHP and selects content carried by each beam, balancing accuracy and execution time through the parameter$\varepsilon \in[0, 1]$. For inner layer, we propose the Alternating Direction Method of Multipliers Based Power Allocation and Caching (ADMM-PAC) scheme, optimizing power across beams and managing caching at BSs in a distributed fashion, using BSs' resources and local information for parallel optimization processes without requiring global information as in a centralized method. Simulations show that$\varepsilon$-BHPCS achieves performance similar to the optimal exhaustive search, while the distributed ADMM-PAC aligns closely with standard centralized interior-point optimization algorithm.

In global navigation satellite system (GNSS) positioning, Doppler shift-based methods suffer from low accuracy due to the low dynamics and high altitude of medium Earth orbit (MEO) satellites. In contrast, low Earth orbit (LEO) satellites, such as Starlink, exhibit high dynamics and low altitude, leading to a significant Doppler shift effect that can enhance user terminal (UT) positioning accuracy. However, the accuracy of Doppler-based positioning is influenced mainly by the Doppler dilution of precision (DDOP) of the satellites, which quantifies the influence of satellite velocity direction and geometric diversity. Unlike conventional geometric dilution of precision (GDOP), which only quantifies the satellite's geometric diversity, DDOP accounts for velocity vectors, making it a critical factor in Doppler-based positioning. This paper presents an intuitive analysis of DDOP and drives the Doppler geometry matrix. To evaluate the DDOP performance of Starlink satellites and GNSS, we calculate the DDOP values for evenly-spaced random places on Earth. A simulator GUI is designed to calculate the DDOP value on any place on Earth. Moreover, we establish the correlation between DDOP and Doppler-based positioning accuracy, demonstrating that lower DDOP values yield higher positioning accuracy. Our numerical analysis also confirms that Starlink LEO satellites offer significantly better DDOP than GNSS, underscoring its potential as an alternative positioning solution, particularly in the GNSS-denied environment.

This paper proposes a novel proactive deployment framework for multiple aerial base stations (ABSs) to enhance the resilience of cellular networks against post-disaster disruptions. Unlike conventional approaches that optimize the ABS deployment separately for pre and post-disaster conditions, our proposed framework jointly optimizes the ABS deployment providing high-quality services continuously from predisaster to post-disaster environments. By formulating the joint optimization problem, our strategy maximizes the minimum user satisfaction before disasters and the user coverage after disasters. Our simulation results demonstrated that our strategy significantly outperformed the existing strategies by achieving the pre and post-disaster network performance while requiring only a marginal increase in the number of ABSs.

Space-air-ground integrated networks (SAGIN) combine the wide-area coverage of satellites with airborne platforms acting as relays, enabling efficient data delivery for large-scale Internet of Things (IoT) sensing applications. To further enhance transmission efficiency, this paper incorporates airborne onboard processing to reduce communication workloads and proposes an intelligent beam hopping strategy tailored to spatially uneven traffic demands. Specifically, we design a multi-agent deep reinforcement learning (MADRL)-based beam hopping framework, where satellite agents coordinate beam scheduling while considering spatial service heterogeneity and the diverse computational capacities of airborne platforms. To reduce the complexity introduced by individual task requirements, we integrate a summarized statistical profile of the computation tasks into the agent’s observation space, including average and maximum computation efficiencies across tasks. Simulation results demonstrate that the proposed scheme significantly accelerates convergence and reduces the data backlog by up to 80%, especially under scenarios with considerable heterogeneity in service demands and airborne platform computational capabilities.

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.

Integrating feeder- and user-links in multi-beam satellite communications significantly enhances system flexibility but requires effective resource allocation to fully realize its potential. Multi-agent deep reinforcement learning (MADRL) has emerged as a scalable solution for beam hopping, by allowing each agent to optimize the transmission parameters for one beam. However, integrating feeder- and user-links introduces complicated dependencies, including resource competition between feeder- and user-links and data-flow coupling between uplinks and downlinks, dramatically deteriorating agent cooperation. To approach the performance limit, this paper introduces a dependency-elimination MADRL framework incorporating model decomposition, link decoupling, and novel agent-level collaboration mechanisms to allocate beams, power, and bandwidth with reduced complexity. Specifically, to facilitate beam-level agent reuse for complexity reduction under the heterogeneity of feeder- and user-links, characterized by data-flow aggregation and division, we decouple bandwidth allocation from the learning model. The uplink-downlink dependencies in the bandwidth allocation is then resolved using a generalized water-filling strategy based on the performance upper bounds. Furthermore, we improve agent cooperation efficiency through state and reward decomposition and a novel non-cooperation penalty. Evaluations show that our method improves the system performance by up to 57.7% compared to sota MADRL methods while reducing training complexity by more than 50%.

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.

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.

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.

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.

Low Earth orbit (LEO) satellite-based positioning is emerging due to the available LEO constellation and high Doppler shift opportunity. However, positioning with LEO satellites utilizing signals of opportunity (SOPs)-based approach suffers from ephemeris error as the orbital element is estimated using two-line element (TLE) files that induce error over time. In the differential Doppler positioning method, an additional base receiver along with its known position with the user terminal (UT), is used to handle this erroneous measurement. The positioning accuracy of UT degrades significantly in the case of the long baseline (the distance between the base receiver and UT) scenario. Moreover, Doppler shift measurement errors also occur due to the clock synchronization issue between the base receiver and UT. Addressing these issues, we propose a robust positioning method, coined 3DPose, that can handle the clock synchronization issue between the base receiver and UT, as well as positioning degradation due to the long baseline. The proposed 3DPose method applies double-difference Doppler shift measurements to eliminate the clock synchronization issue, and a novel ephemeris error correction algorithm to improve UT positioning accuracy in case of the long baseline. To evaluate the performance of the proposed method, we perform a comparison with the existing differential Doppler positioning method. The comparison results show that the proposed 3DPose method outperforms the existing differential Doppler positioning in terms of positioning accuracy.

In this paper, we study a wirelessly-powered communication network (WPCN) composed of several wireless devices (WDs) which rely on an energy access point (EAP) for their supply of energy which is used to transfer data to a data access point (DAP). While the dominant focus of the literature in this area has been on frame-based data rate optimization, it has been known that this approach is greedy and only optimal in additive white Gaussian noise (AWGN) channels. Hence, in this work, we propose an online algorithm that, based on the current state of the batteries and channels, adaptively calculates the transmission time and power allocations to maximize the long-term performance of this system in fading channels. In order to accomplish such a goal, we employ the Twin Delayed DDPG (TD3) approach, which is a deep reinforcement learning (DRL) technique designed for continuous state and action spaces. Additionally, we use the multi-task learning (MTL) techniques to train a DRL agent that creates a composite policy capable of dynamically optimizing not just one specific WPCN configuration, but a continuum of WPCN problems, which we call a composite environment. Each of these environments has different parameters such as WDs’ distances to the DAP and EAP, batteries’ size, rectifying antenna (rectenna) energy harvesting (EH) model linearity, as well as different uplink (UL) and downlink (DL) channel specular components. Simulation results confirm that the TD3 algorithm can easily learn to achieve a much higher common throughput compared to the traditional optimization schemes.

Integrated terrestrial-satellite networks (ITSNs) play an essential role in providing global and ubiquitous connectivity for next generation networks. Spectral efficiency of ITSNs depends on their integrated architecture and the operational strategies, including interference management and resource allocation. This paper proposes an efficient integrated access and backhaul (IAB) architecture for terrestrial-satellite networks considering both uplink (UL) and downlink (DL) communications. We aim to integrate terrestrial access and satellite backhaul networks by developing a novel optimization framework for their joint operation. In particular, in-band access-backhaul transmission is considered for high spectral efficiency, where a reverse time division duplexing is used to prevent both self-interference and interference between access links and backhaul links. In addition, cooperation among gNodeB is taken into account to overcome harsh propagation conditions such as blockage effects and severe pathloss. A framework for joint optimization of cooperative beamforming and resource allocation is developed to maximize the UL-DL rate region of the in-band IAB. The proposed architecture is verified using the 3rd Generation Partnership Project (3GPP) channel models. Numerical results show that the proposed architecture significantly outperforms the classical out-of-band backhauling while approaching an outer bound of the UL-DL rate region.

Future wireless networks, in particular, 5G and beyond, are anticipated to deploy dense Low Earth Orbit (LEO) satellites to provide global coverage and broadband connectivity. However, the limited frequency band and the coexistence of multiple constellations bring new challenges for interference management. In this paper, we propose a robust multilayer interference management scheme for spectrum sharing in heterogeneous satellite networks with statistical Channel State Information (CSI) at the Transmitter (CSIT) and Receivers (CSIR). In the proposed scheme, Rate-Splitting Multiple Access (RSMA), as a general and powerful framework for interference management and multiple access strategies, is implemented distributedly at Geostationary Orbit (GEO) and LEO satellites, coined Distributed-RSMA (D-RSMA). By doing so, D-RSMA aims to mitigate the interference and boost the user fairness of the overall multilayer satellite system. Specifically, we study the problem of jointly optimizing the GEO/LEO precoders and message splits to maximize the minimum rate among User Terminals (UTs) subject to a transmit power constraint at all satellites. A robust algorithm is proposed to solve the original non-convex optimization problem. Numerical results demonstrate the effectiveness and robustness towards network load and CSI uncertainty of our proposed D-RSMA scheme. Benefiting from the interference management capability, D-RSMA provides significant max-min fairness performance gains compared to several benchmark schemes.

This correspondence is a response to Comments on the above article raised by Wen et al. (2023). The authors in Wen et al. (2023) insist that there might be some errors in the Kalman filter derivation procedure, mathematical expression, and computability of high-order error terms in Ko et al. (2022). However, the arguments are not always true. The authors in Wen et al. (2023) misunderstood the derivation of the Kalman filter by the assumption of an unbiased estimator. In addition, they also misunderstood the assumption of noise distribution for the Kalman filter design. In this correspondence, we analyze and refute the comments in a pointby-point fashion.

Low earth orbit (LEO) satellite systems with sensing functionality are envisioned to facilitate global-coverage service and emerging applications in 6G. Currently, two fundamental challenges, namely, inter-beam interference among users and power limitation at the LEO satellites, limit the full potential of the joint design of sensing and communication. To effectively control the interference, a rate-splitting multiple access (RSMA) scheme is employed as the interference management strategy in the system design. On the other hand, to address the limited power supply at the LEO satellites, we consider low-resolution quantization digital-to-analog converters (DACs) at the transmitter to reduce power consumption, which grows exponentially with the number of quantization bits. Additionally, optimizing the total energy efficiency (EE) of the system is a common practice to save the power. However, this metric lacks fairness among users. To ensure this fairness and further enhance EE, we investigate the max-min fairness EE of the RSMA-assisted integrated sensing and communications (ISAC)-LEO satellite system. In this system, the satellite transmits a quantized dual-functional signal serving downlink users while detecting a target. Specifically, we optimize the precoders for maximizing the minimal EE among all users, considering the power consumption of each radio frequency (RF) chain under communication and sensing constraints. To tackle this optimization problem, we proposed an iterative algorithm based on successive convex approximation (SCA) and Dinkelbach’s method. Numerical results illustrate that the proposed design and RSMA architecture outperforms strategies maximizing the total EE of the system, space-division multiple access (SDMA), and orthogonal multiple access (OMA) in terms of max-min fairness EE and the communication-sensing trade-off.

Low-earth-orbit satellite networks (LSNs) offer an enhanced global connectivity and a wide range of applications such as disaster response and military operations, among others. Each specific application can be represented by a service function chain (SFC) in which each function is considered as a task in the application. Our objective is to optimize the long-term system performance by minimizing the average end-to-end delay of SFC deployments in LSNs. To achieve this, we formulate a dynamic programming (DP) problem to derive an optimal placement policy. To overcome the computational intractability, the need for statistical knowledge of SFC requests, and centralized decision-making challenges, we present a multi-agent Q-learning approach where satellites act as independent agents. To facilitate performance convergence in non-stationary agents' environments, we let agents to collaborate by sharing designated learning parameters. In addition, agents update their Q-tables via two distinct rules depending on selected actions. Extensive experimentation shows that our approach achieves convergence and performance relatively close to the optimum obtained by solving the formulated DP equation.

Integrated satellite-terrestrial networks (ISTNs) are essential for providing ubiquitous mobile ultra-broadband service in beyond 5G networks. The spectral efficiency and reliability of ISTN depend on the integrated architecture and its operational strategies including interference management and resource allocation. Our view is to integrate terrestrial access and satellite backhaul networks and to develop an optimization technique for their joint operation. This paper proposes an efficient integrated access and backhaul (IAB) architecture for satellite-terrestrial networks (STNs) based on reverse time division duplexing (TDD) considering both uplink (UL) and downlink (DL). In particular, in-band backhauling and gNodeB (gNB) cooperation are considered for high spectral efficiency and reliability. A framework for joint optimization of cooperative beamforming and resource allocation is developed to maximize the UL-DL rate region of the in-band IAB. The proposed scheme is verified using the 3rd Generation Partnership Project (3GPP) standard channel models. Results show that the proposed scheme significantly outperforms the conventional wireless backhauling, while approaching to an outer bound of the UL-DL rate region.

Reconfigurable intelligent surfaces (RISs) play a crucial role in improving the coverage and efficiency of millimeter-wave (mmWave) communication systems for Internet of Things (IoT) networks by enhancing signal strength and reducing interference. However, to fully exploit their potential, mathematical models and optimization algorithms are needed to optimize the RIS configuration and control. In this work, we present a mathematical model and optimization algorithm for RIS-aided mmWave communication systems. This paper proposes a mathematical model to capture the effects of RISs on mmWave communication channels, including equations for channel estimation and prediction. We also develop an opti-mization problem to maximize the system throughput, along with a model for optimizing the RIS phase shift using the alternating projection phase shift algorithm. The power allocation algorithm for the mmWave base station is presented using the Karush-Kuhn-Tucker (KKT) approach. Finally, we perform simulations to validate the proposed solution and compare it with ideal and random phase shift solutions. The results show that our proposed solution performs close to the ideal solution, demonstrating the effectiveness of the proposed mathematical model and optimization algorithm.

Abstract Due to the reduction in costs of manufacture and launch of Low‐Earth orbit (LEO) satellites, LEO satellite communication (SATCOM) has become a promising solution to provide high data rates and low latency connectivity for future communications. However, due to the fast movement of LEO satellites, it has to face a large Doppler effect, leading to high inter‐carrier interference (ICI) power and severe received symbol error rate. To tackle this problem, a novel optimisation framework is proposed that boosts the achievable rate in LEO SATCOM networks by utilising passive beamforming of intelligent reflecting surface (IRS) to compensate for the Doppler effect. First, a carrier‐to‐interference‐noise ratio (CINR) was derived for each subcarrier and a joint optimisation problem of both power allocation is formulated to each subcarrier and passive beamforming of IRS. Moreover, a simultaneous iterative‐water‐filling‐algorithm (SIWFA) and semidefinite programing (SDP) are utilised, thereby striking a balance that minimises ICI power and maximises desired signal power. Numerical results demonstrate the superior achievable rate performance of the proposed IRS‐aided Doppler compensation for LEO SATCOM networks compared to benchmark schemes.

In this article, we investigate the performance of hybrid satellite–terrestrial relay networks with multiuser downlink nonorthogonal multiple access. The satellite applies Alamouti space–time block coding, and users exploit a receive antenna selection technique. Communication between the satellite and users is assumed to be established with the aid of a half-duplex terrestrial relay equipped with multiple receive antennas and operating in amplify-and-forward mode, because of the heavy masking effects attributed to environmental obstacles. Subsequently, the satellite–relay link is exposed to shadowed-Rician fading, whereas the relay–users links undergo Nakagami-$m$fading, which is a generic statistical channel model for nonterrestrial networks. To be more practical, we consider imperfect successive interference cancellation. The exact outage probability of each user and the corresponding asymptotic expression at the high signal-to-noise ratio are derived to demonstrate the system performance. The analysis indicates that the shadowing conditions do not affect the array gain when the diversity order is dominated by the multiantenna configuration of the second hop. The theoretical derivations are validated by using Monte Carlo simulations. The numerical results indicate that the proposed scheme significantly improves the performance of each user under various shadowing conditions. It also outperforms orthogonal multiple access when proper power levels are allocated to users.

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 propose a Doppler compensation method in LEO-MIMO communication, where two LEO satellites are used as amplify-and-forward (AF) relays, and line-of-sight (LOS) propagation dominates the communication channel. We first analyze Doppler effects in uplink and downlink, and propose a dual-hop AF relay channel model including Doppler effects. Also we show that Doppler effects in our scenario can be easily compensated at the LEO satellites, without the need of intersatellite link (ISL) communication.

This paper studies the outage performance of a cooperative multiple-input single-output satellite-terrestrial net-work in which the satellite equipped with multiple antennas simultaneously serves two single antenna terrestrial users in a non-orthogonal manner, called MISO-NOMA. Specifically, the satellite utilizes the maximal-ratio transmission technique while the user with strong channel gain (called the strong user) assists to the user with weak channel gain (called the weak user) by acting as a decode-and-forward relay with prior knowledge of weak user. We assume that channels are subjected to Shadowed-Rician and Nakagami-m fading for the satellite and terrestrial links, respectively. In order to characterize the performance behavior of both users, outage probability expressions are derived. Theoretical results validated by simulations demonstrate that user-cooperation has more impact on the performance improvement of the weak user under heavy shadowing condition which is the most crucial channel effect in satellite-terrestrial environment. In addition, with the increasing number of antennas, the weak and strong users receive the best performance improvement in non-cooperation and user-cooperation cases, respectively.

Positioning is the most basic yet important process in a train control system. In practical train systems, the position of a train is determined with a track circuit, a radio frequency (RF) tag called a “balise”, or a tachometer. A train control center gives each train an automatic train protection (ATP) speed profile computed from the positioning information of both the train and the next train in front it. To successfully operate in a conventional train control system such as ATP/ATO, each train derives its own automatic train operation (ATO) speed profile from the ATP speed profile provided by the control center. However, existing train positioning schemes face many difficulties in terms of installation, maintenance, and repair. To address these difficulties, we consider using 5G NR (New Radio) signals which have a high probability for guaranteeing line-of-sight (LOS) as well as a high sampling rate because they do not require the additional installation of any infrastructure for positioning. In this paper, we propose two positioning schemes for high speed trains (HSTs) based on Kalman filters that make use of 5G NR signals. The first is an HST positioning scheme using a modified Kalman filter and the second is an HST positioning scheme using a deep Kalman filter. Simulation results show that the two proposed schemes achieve better performance in nonlinear non-Gaussian systems as well as in nonlinear Gaussian systems in terms of mean and worst 5% position errors when compared to the existing positioning scheme for HSTs that utilize 5G NR signals.

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.

This article studies a multiuser multiple-input-single-output (MU-MISO) Internet-of-Things (IoT) network powered by wireless power transfer (WPT). The network consists of one hybrid data-and-energy access point (HAP) having multiple antennas and several single-antenna IoT nodes. The HAP coordinates energy/information transfer to/from the nodes in the downlink (DL)/uplink (UL) using frequency-division duplexing (FDD). On the one hand, in order for WPT to effectively harness the potential of multiple antennas, it requires such techniques as energy beamforming (EB). On the other hand, efficient EB can only be achieved if channel state information (CSI) is available to the transmitter, which, in FDD systems, can be accomplished through UL feedback. Therefore, the UL channel frames are split into two phases in our scheme: 1) the CSI feedback phase during which the IoT nodes feed CSI back to the HAP and 2) the wireless information transmission (WIT) phase where the HAP performs WIT. To ensure rate fairness among the IoT nodes, we maximize the minimum expected WIT data rate among the nodes. This problem is nonconvex and thus difficult to solve optimally. To tackle this challenge, we decouple the original optimization problem into tractable subproblems and solve them in an alternative manner. Finally, we analyze the behavior of this system when the number of HAP antennas increases. Simulation results corroborate the accuracy of our analysis.

This article considers a one-way decode-and-forward (DF) relay-based Internet-of-Things (IoT) network consisting of a source, a relay, and a destination in wireless energy harvesting (EH) and information transmission (IT) using the time switching-based relaying (TSR) protocol. The one-way DF relay network using the TSR protocol consists of some time slots. This article proposes the time allocation scheme for maximizing the achievable data rate under the total block time constraint. It is assumed that the relay is considered the IoT device used for long periods of time without battery replacement and it is equipped with both nonrechargeable and rechargeable batteries. Based on the supplied power by the nonrechargeable battery, the proposed scheme determines whether or not to harvest energy and operates two alternate time allocation schemes. Since the proposed scheme is based on the closed-form expressions, it provides low complexity. Numerical results show that the achievable data rate of the proposed time allocation scheme is greater than that of the fixed time allocation scheme.

Wireless power transfer is a promising solution for wireless networks composed of Internet-of-Things (IoT) devices that may suffer from insufficient battery capacity. A wireless powered communication network (WPCN) is a framework to design energy-constrained networks such as IoT networks. In this article, we consider WPCNs consisting of a hybrid access point (H-AP) and energy harvesting users, all equipped with multiple antennas. A H-AP transfers energy to the users using energy beamforming in the downlink, and the users transmit information using the harvested energy in the uplink, where nonorthogonal multiple access (NOMA) transmission is employed. For the uplink NOMA transmission, users are grouped into multiple clusters by cluster-specific beamforming. In particular, signal alignment is exploited for the beamforming so that the channels of users in a cluster are aligned in the same direction. By signal alignment, the number of messages decoded by successive interference cancellation (SIC) is reduced, which can be effective at lowering the decoding complexity and SIC error propagation. Due to the difficulty of jointly optimizing cluster-specific beamforming and time/energy resources for sum-throughput maximization, we determine the beamforming relying on signal alignment first, and then the resources are optimized for given beamforming. To be more specific, we propose a novel iterative algorithm for cluster-specific beamforming design followed by the sum-throughput maximization algorithm. Numerical results show the sum-throughput performance of the proposed scheme and its robustness toward SIC error propagation compared to existing schemes.

This article considers a power allocation problem in energy harvesting downlink non-orthogonal multiple access (NOMA) systems in which a transmitter sends desired messages to their respective receivers by using harvested energy. To tackle this problem, we make use of a reinforcement learning approach based on a shallow neural network structure. We prove that the optimal power allocation policy and the optimal action-value function depend monotonically on some of their input variables and the shallow neural network structure is designed based on properties revealed in the proof. Different from inefficient deep learning methods that tend to require tremendous computational resources, this structure is capable of fully capturing the characteristics of the desired function with a single hidden layer. The optimized structure also allows learning agents to be robust and highly reliable in learning about randomly occurring data. Furthermore, we provide comprehensive experimental results in harsh environments where various arbitrary factors are assumed in order to demonstrate the robustness of the proposed learning approach compared with deep neural networks without proper grounds. It is also shown that the proposed learning process converges to a policy that outperforms existing power allocation algorithms.

Currently, there are two representative non-volatile dual in-line memory module (NVDIMM) interfaces: a proprietary Intel DDR-T and the JEDEC NVDIMM-P, which are not supported by existing platforms. Adoption of new platform is costly and measuring its efficiency of migrating to the new platform is much more complex. This study is an alternative way of them—finding a new memory device that can be supported by all existing systems. In this paper, we propose an NVDIMM architecture with several system-wide mechanisms to allow the synchronous DDR4 memory interfaces to support non-deterministic (asynchronous) timing. The proposed memory architecture is implemented as a real device prototype, and also evaluated using synthetic and real workloads on an x86-64 server system.

This paper deals with the power allocation problem for achieving the upper bound of sum-rate region in energy harvesting downlink channels. We prove that the optimal power allocation policy that maximizes the sum-rate is an increasing function for harvested energy, channel gains, and remaining battery, regardless of the number of users in the downlink channels. We use this proof as a mathematical basis for the construction of a shallow neural network that can fully reflect the increasing property of the optimal policy. This scheme helps us to avoid using big neural networks which requires huge computational resources and causes overfitting. Through experiments, we reveal the inefficiencies and risks of deep neural network that are not optimized enough for the desired policy, and shows that our approach learns a robust policy even with the severe randomness of environments.

This work exploits the advantages of two prominent techniques in future communication networks, namely caching and non-orthogonal multiple access (NOMA). Particularly, a system with Rayleigh fading channels and cache-enabled users is analyzed. It is shown that the caching-NOMA combination provides a new opportunity of cache hit which enhances the cache utility as well as the effectiveness of NOMA. Importantly, this comes without requiring users' collaboration, and thus, avoids many complicated issues such as users' privacy and security, selfishness, etc. In order to optimize users' quality of service and, concurrently, ensure the fairness among users, the probability that all users can decode the desired signals is maximized. In NOMA, a combination of multiple messages are sent to users, and the defined objective is approached by finding an appropriate power allocation for message signals. To address the power allocation problem, two novel methods are proposed. The first one is a divide-and-conquer-based method for which closed-form expressions for the optimal resource allocation policy are derived, making this method simple and flexible to the system context. The second one is based on the deep reinforcement learning method that allows all users to share the full bandwidth. Finally, simulation results are provided to demonstrate the effectiveness of the proposed methods and to compare their performance.

A downlink single-input single-output non-orthogonal multiple access setting is considered, in which a base station (BS) is communicating with two legitimate users in two possible scenarios of unsecure environments: existence of an external eavesdropper and communicating through an untrusted relay. For the first scenario, a number of trusted cooperative half-duplex relays is employed to assist with the BS's transmission and secure its signals from the external eavesdropper. Various relaying schemes are proposed and analyzed for that matter: cooperative jamming, decode-and-forward, and amplify-and-forward. For each scheme, secure beamforming signals are devised at the relays to maximize the achievable secrecy rate regions. For the second scenario, with the untrusted relay, achievable secrecy rate regions are derived for two different relaying schemes, compress-and-forward and amplify-and-forward, under two different modes of operation. In the first mode, coined passive user mode, the users receive signals from both the BS and the untrusted relay and combine them to decode their messages. In the second mode, termed the active user mode, the users transmit a cooperative jamming signal simultaneously with the BS's transmission to further confuse the relay. Focusing on half-duplex nodes, the users cannot receive the BS's signal while jamming the relay, i.e., while being active, and rely only on the signals forwarded to them by the relay. It is shown that the best relaying scheme highly depends on the system parameters, in particular the distances between the nodes, and also on the part of the secrecy rate region at which the system is to operate.

In this paper, we consider a power allocation optimization technique for a time-varying fading broadcast channel in energy harvesting communication systems, in which a transmitter with a rechargeable battery transmits messages to receivers using the harvested energy. We first prove that the optimal online power allocation policy for the sum rate maximization of the transmitter is an increasing function of harvested energy, remaining battery, and each user's channel gain. We then construct an appropriate neural network by relying on increasing behavior of the optimal policy. This two-step approach, by using an effective function approximation as well as providing a fundamental guideline for neural network design, can prevent us from wasting the representational capacity of neural networks. On the basis of the neural network, we apply the policy gradient method to solve the power allocation problem. To validate the performance of our approach, we compare it with the closed-form the optimal policy in a partially observable Markov problem. Through further experiments, it is observed that our online solution achieves a performance close to the theoretical upper bound of the performance in a time-varying fading broadcast channel.

A downlink single-input single-output nonorthogonal multiple access system is considered in which a base station (BS) is communicating with two legitimate users in the presence of an external eavesdropper. A group of trusted cooperative half-duplex relay nodes, powered by the BS, is employed to assist the BS's transmission. The goal is to design relaying schemes such that the legitimate users' secrecy rate region is maximized subject to a total power constraint on the BS and the relays' transmissions. Three relaying schemes are investigated: cooperative jamming, decode-and-forward, and amplify-and-forward. Depending on the scheme, secure beamforming signals are carefully designed for the relay nodes that either diminish the eavesdropper's rate without affecting that of the legitimate users, or increase the legitimate users' rates without increasing that of the eavesdropper. The results show that there is no relaying scheme that fits all conditions; the best relaying scheme depends on the system parameters, namely, the relays' and eavesdropper's distances from the BS, and the number of relays. They also show that the relatively simple cooperative jamming scheme outperforms other schemes when the relays are far from the BS and/or close to the eavesdropper.

Social and communication networks interact with each other in multifaceted ways, yet these interactions are often considered to be secondary in throughput, privacy and security analysis for communication networks. In this paper, full-duplex (FD) and half-duplex (HD) multi-antenna cooperative communication systems are studied by taking both physical links and social connections into account. An optimal beamformer for maximizing communication rate in the proposed socio-technological setting aims to balance between the direct link and the cooperating link as well as respecting the trust degree between the users. The resulting optimization problems are nontrivial to solve, even numerically, as they are not convex. The complexity of the problems is significantly reduced by showing that a linear combination of the direct and cooperating links' channel vectors maximizes the achievable rate. Then, a computationally efficient numerical solution is used to maximize the rates both in the FD and HD modes. Numerical results demonstrate that significant gains in communication rates can be obtained with the proposed optimal beamforming design.

In this paper, we investigate a private information retrieval (PIR) problem for secure distributed storage systems in the presence of an eavesdropper. We design the secure distributed database and the corresponding PIR scheme, which protect not only user privacy (concealing the index of the desired message) from the databases, but also data security (concealing the messages themselves) from an eavesdropper. In our proposed scheme, we use a secret sharing scheme in storing the messages for data security at each of the databases. We consider two different scenarios on whether the databases are aware of the index sets of the secret shares stored in other databases. The key idea in designing an efficient PIR procedure is to exploit the secret shares of undesired messages as a side information by means of storing the secret shares at multiple databases. In particular, it is shown that the rates of the proposed PIR schemes are within a constant multiplicative factor from the derived upper-bound on the capacity of PIR problem.

In this paper, beamforming design is investigated for a multi-antenna cooperative communication system in which both physical links and social connections (trust degrees) between nodes are taken into account. An optimal beamformer aims to balance between the direct link and the cooperating link as well as respecting the trust degree. The resulting optimization problem is nontrivial to solve, even numerically, as it is not convex. The complexity of the problem is largely reduced by showing that a linear combination of the direct and cooperating links' channel vectors maximizes the achievable rate. Then, a computationally efficient numerical solution is used to maximize the rate. Numerical results demonstrate that significant gains in communication rates can be obtained with the proposed optimal beamforming design.

A Gaussian multiple-input multiple-output wiretap channel in which the eavesdropper and legitimate receiver are equipped with arbitrary numbers of antennas and the transmitter has two antennas is studied in this paper. It is shown that the secrecy capacity of this channel can be achieved by linear precoding. The optimal precoding and power allocation schemes achieving the secrecy capacity are developed subsequently, and the secrecy capacity is compared with the generalized singular value decomposition (GSVD)-based precoding, which is the best previously proposed precoding for this problem. Numerical results show that substantial gain can be obtained in secrecy rate between the proposed and GSVD-based precodings.

A new relay-aided non-orthogonal multiple access (NOMA) technique is proposed for multi-cell uplink cellular networks in which each cell supports K single-antenna users by its respective base station (BS) equipped with N(4Z ≪K) antennas. Cooperative relaying transmission is used to accommodate more than one user per orthogonal resource block in the context of interference-limited cellular networks. With the proposed relayaided NOMA, an Alamouti structure of the desired symbol can further be generated at each BS free of interference, which gives rise to diversity gain of two. The proposed scheme does not require any channel state information (CSI) at users. Further, only limited CS! is required at relays and BSs, which can greatly reduce the control overhead.

This paper studies full-duplex (FD) cellular networks in which a base station (BS) operated in FD mode with multiple antennas supports multiple uplink and downlink users simultaneously in the same wireless channel. Two typical FD cellular scenarios are considered, one with half-duplex (HD) users and the other with FD users along with the FD BS. For both the cases, a novel constructive method is developed for finding a closed-form interference alignment (IA) solution, namedcyclic IA. The core idea behind this approach is to construct a set of loop-equations enabling IA in acyclicmanner, so that beamforming vectors are sequentially determined by solving an eigenvalue problem. It is shown analytically that the proposed cyclic IA can achieve theoptimalsum degrees-of-freedom (DoF) when the number of user antennas is large enough to meet the derived conditions. In particular, it is shown that the proposed scheme achieves a twofold DoF gain compared with conventional HD cellular networks even in the presence of inter-link interference, provided the number of users becomes large enough compared with the ratio of the number of BSs and user antennas. Simulation results demonstrate that not only are the analytical DoF results valid, but under a practical multi-cell scenario, the proposed cyclic IA offers significant throughput gains depending on the cell radius.

A Gaussian multiple-input multiple-output wiretap channel in which the eavesdropper and legitimate receiver are equipped with arbitrary numbers of antennas and the transmitter has two antennas is studied in this paper. The input covariance matrix that achieves the secrecy capacity is determined. In particular, it is shown that the secrecy capacity of this channel can be achieved by linear precoding. Precoding and power allocation schemes that maximize the achievable secrecy rate, and thus achieve the secrecy capacity, are developed. The secrecy capacity is then compared with the achievable secrecy rate of generalized singular value decomposition (GSVD)-based precoding, which is the best previously proposed technique for this problem. Numerical results demonstrate that substantial gain can be obtained in secrecy rate between the proposed and GSVD-based precodings.

In this paper, we characterize the degrees of freedom (DoF) for K-user M × 1 multiple-input single-output interference channels with reconfigurable antennas, which have N-preset modes at the receivers, assuming linear coding strategies in the absence of channel state information at the transmitters, i.e., blind interference alignment. Our linear DoF converse builds on the lemma that if a set of transmit symbols is aligned at their common unintended receivers, those symbols must have independent signal subspace at their corresponding receivers. This lemma arises from the inherent feature that channel state's changing patterns of the links towards the same receiver are always identical, assuming that the coherence time of the channel is long enough. We derive an upper bound for the linear sum DoF, and propose an achievable scheme that exactly achieves the linear sum DoF upper bound when both of the n*/M = R1and MK/n* = R2are integers, where n* denotes the optimal number of preset modes out of N preset modes. For the other cases, where either R1or R2is not an integer, we only give some guidelines how the interfering signals are aligned at the receivers to achieve the upper bound. As an extension, we also show the linear sum DoF upper bound for downlink/uplink cellular networks.

In this paper, we characterize the linear degrees of freedom (DoF) of a cellular network in which the base station (BS) operates in a full-duplex (FD) mode and the users operate in a half-duplex mode. We assume that the BS and the users are equipped with reconfigurable antennas which can be switched between their preset modes. We consider two practical scenarios for different assumptions on channel state information at the transmit sides (CSIT), referred to as no CSIT and partial CSIT models. To derive the inner-bounds for two scenarios, we propose a new achievable scheme which enables interference alignment between uplink and downlink interference signals at each user via preset mode switching of reconfigurable antennas. The key concept of our scheme is to align the interference signals of uplink transmission at the downlink users, through the identical preset mode pattern over the multiple of downlink transmission periods and silence periods of the BS. We also develop an outer-bound on the linear sum DoF of the cellular network for the no CSIT model, which matches up with the inner-bound. Moreover, we also provide a natural variant of the proposed scheme when considering residual self-interference at the FD BS, which can alleviate the shortcoming of the existing self-interference cancellation techniques.

In this paper, we propose a novel relay-aided successive aligned interference cancellation (RaSAIC) method for a 2 × K X channel with L full-duplex relays. The key idea behind the RaSAIC is to apply a cooperative relays' precoding technique that aligns interference signals at unintended receivers while creating an Alamouti structure for the desired signals. With the proposed cooperative relay transmission technique, it is shown that both the optimal degrees of freedom gain of 2K/K+1 and the diversity gain of two are achievable for constant channels when the numbers of relays' antennas are enough to satisfy the derived feasibility conditions without requiring channel state information (CSI) at transmitters but with global and local CSI at relays and receivers, respectively.

This paper proposes a novel multi-antenna relay-aided interference management technique that can use imperfect channel knowledge for interference relay channels. Using the proposed method, it is shown that KM/K+M-1 degrees of freedom (DoF) are achievable in a K-user multiple-input-single-output interference relay channel when the relay has M antennas with a certain type of limited channel knowledge. By leveraging this result, it is demonstrated that the interference-free DoF of K are asymptotically achieved as M approaches infinity. One major implication of these results is that even under this limited channel knowledge, the use of massive antennas at the relay is sufficient to recover the optimal DoF for relay-aided interference networks with perfect channel knowledge.

We consider M x 2 X channels with M+1 antennas at each node. The proposed scheme achieves both the optimal degrees of freedom (DoF) of 2M for the considered channel and the diversity order of 2 simultaneously. The key to achieving them is to design beamformers using interference alignment (IA) scheme with Alamouti codes. Furthermore, the proposed scheme requires 2 symbol extensions. We assume that each transmitter has local channel state information at transmitter (CSIT) and each receiver has full channel state information at receiver (CSIR).

In this paper, we propose a new retrospective interference alignment for two-cell multiple-input multiple-output (MIMO) interfering multiple access channels (IMAC) with the delayed channel state information at the transmitters (CSIT). It is shown that having delayed CSIT can strictly increase the sum-DoF compared to the case of no CSIT. The key idea is to align multiple interfering signals from adjacent cells onto a small dimensional subspace over time by fully exploiting the previously received signals as side information with outdated CSIT in a distributed manner. Remarkably, we show that the retrospective interference alignment can achieve the optimal sum-DoF in the context of two-cell two-user scenario by providing a new outer bound.

We develop a blind interference alignment (BIA) through staggered antenna switching scheme for more realistic channel assumption. Contrary to the general assumption that the coherence time of the channel is long enough to perform BIA, when the coherence time is not long enough, channel coefficients stay constant for limited block length. Therefore, we propose a dynamic supersymbol design algorithm which can construct a supersymbol with limited block length which is determined by the coherence time of the channel. We demonstrate that the supersymbol length can be reduced significantly by aligning interferences in a hierarchical manner referred to as hierarchical BIA. Furthermore, we also show that the proposed dynamic supersymbol design algorithm achieves higher degrees of freedom than the conventional method with a given coherence time.

We propose a blind interference alignment (BIA) through staggered antenna switching scheme with no ideal channel assumption. Contrary to the ideal assumption that channels remain constant during BIA symbol extension period, when the coherence time of the channel is relatively short, channel coefficients may change during a given symbol extension period. To perform BIA perfectly with realistic channel assumption, we propose a grouping based supersymbol structure for K-user interference channels which can adjust a supersymbol length to given coherence time. It is proved that the supersymbol length could be reduced significantly by an appropriate grouping. Furthermore, it is also shown that the grouping based supersymbol achieves higher degrees of freedom than the conventional method with given coherence time.

In this paper, we propose a new retrospective interference alignment for two-cell multiple-input multiple-output (MIMO) interfering multiple access channels (IMAC) with M receive antennas per base station (BS) and K users per cell each with N transmit antennas. The key idea of the retrospective IA lies in interference-purification during phase 1 and 2, and interference-repetition during phase 3. On the basis of the proposed method, we show that the achievable sum degrees of freedom (sum-DoF) is equal to 2M ([2M/N]+ 1) / (2 [2M/N] + 1) if K M <; NK and min{2KN, M} if NK ≤ M with completely delayed channel state information at the transmitters (CSIT). It is shown that having delayed CSIT can strictly increase the sum-DoF compared to the case with no CSIT for cellular networks, especially for uplink scenarios.

Heterogeneous cellular networks which consist of macrocells and small cells can offer significant capacity gain by utilizing the resources of the small cells. However, to achieve this, the interference between the macrocells and the small cells must be carefully managed. In this work, we propose an uplink intercell interference control (ICIC) scheme which is a unified ICIC approach of handover based interference control and rate-split based interference control. The handover based interference control scheme is a win-win strategy which enhances both interfering user's rate and interfered user's rate. On the other hand, the rate-split based interference control scheme is a yield-win strategy where an interfering user sacrifices his rate to save the interfered user's rate. In this paper, we assume that users have their target QoS such as minimum rate when they send their data. The proposed uplink ICIC scheme reduces the interference as best as possible while it guarantees the minimum QoS. Simulation results shows that the proposed ICIC scheme offers enhanced rate or fairness than legacy ICIC schemes which are not considering user QoS. The proposed ICIC scheme works in distributed manner with low-complexity so that it can be applied to self-organizing network and mobile ad-hoc networks as well as heterogeneous cellular networks.

In heterogeneous cellular networks (HCN) which consists of macrocells and numerous picocells, efficient interference management schemes between macrocells and picocells are so crucial to the overall system performance. We propose a dynamic cooperative silencing control (DCS) scheme for intercell interference control (ICIC). It is a low-complex, low-feedback and distributed algorithm using only strongly interfered neighboring users' information. The system simulation shows that the system performance and in particular the cell-edge throughput is significantly increased with the proposed silencing scheme. It offers 420% and 190% higher average spectral efficiency and edge-user spectral efficiency in compared to macrocell only case, respectively.

This paper focuses on interference issues arising in the downlink of a heterogeneous network (HetNet), where small cells are deployed within a macrocell. Interference scenario in a HetNet varies based on the type of small cell access modes, which can be classified as either closed subscriber group (CSG) or open subscriber group (OSG) modes. For these two types of modes, we propose hierarchical interference alignment (HIA) schemes, which successively determine beamforming matrices for small cell and macro base stations (BSs) by considering a HetNet environment in which the macro BS and small cell BSs have different numbers of transmit antennas. Unlike prior work on interference alignment (IA) for homogeneous networks, the proposed HIA schemes compute the beamforming matrices in closed-form and reduce the feedforward overhead through a hierarchical approach. By providing a tight outer bound of the degrees-of-freedom (DoF), we also investigate the optimality of the proposed HIA schemes with respect to the number of antennas without any time expansion. Furthermore, we propose a new optimization process to maximize the sum-rate performance of each cell while satisfying the IA conditions. The simulation results show that the proposed HIA schemes provide an additional DoF compared to the conventional interference coordination schemes using a time domain-based resource partitioning. Under multi-cell interference environments, the proposed schemes offer an approximately 100% improvement in throughput gain compared to the conventional coordinated beamforming schemes when the interference from coordinated BSs is significantly stronger than the remaining interference from uncoordinated BSs.

In this paper, we study a two cell multiple input single-output interfering broadcast channel with finite rate feedback. In this system, we first derive the rate loss due to the quantization error by considering a coordinated zero-forcing beamforming. In addition, feedback bits allocation methods are proposed to minimize the performance degradation caused by the quantization error. Lastly, we investigate how many feedback bits per user are necessary to maintain the optimal multiplexing gain in MISO-IFBC. Through numerical evaluations, we show that our proposed feedback bits allocation strategy provides significant gain compared to a trivial bits allocation scheme.

In this paper, we study a K-cell multiple input single-output interfering broadcast channel (MISO-IFBC) with finite rate feedback. In this channel, we first derive the rate loss due to the quantization error by considering both a coordinated zero-forcing beamforming and random vector quantization method. Using this result, feedback bits allocation methods are proposed to minimize the performance degradation in K-cell MISO-IFBC. Furthermore, we investigate how many feedback bits per user are necessary to maintain the optimal multiplexing gain in K-cell MISO-IFBC. Through numerical evaluations, we show that our proposed feedback bits allocation strategy provides significant gain compared to a trivial bits allocation scheme.

The interference alignment (IA) is a promising technique to effectively mitigate interferences in wireless communication systems. To show the potential benefits of such an IA scheme, this letter focuses on a two-cell multiple-input multiple-output (MIMO) Gaussian interfering broadcast channels (MIMO-IFBC) with M transmit antennas and N receive antennas. It corresponds to a downlink scenario for cellular networks with two base stations (BSs) with M transmit antennas per BS, and two users with N receive antennas per user, on the cell-boundary of each BS. In this scenario, we propose a novel IA technique jointly designing transmit and receive beamforming vectors in a closed-form expression without iterative computation. It is also analytically shown that the proposed IA algorithm achieves the optimal degrees of freedom (DoF) of 2N in the case of [¾N] ≤ M <; 2N. The simulations demonstrate that not only the analytical results are valid, but the sum-rate of our proposed scheme also outperforms those of conventional techniques, especially in the high signal-to-noise ratio (SNR) regime.

In heterogeneous cellular networks (HTN) which consists of macro-cells and numerous femto-cells, efficient interference management schemes between macro-cells and femto-cells are so crucial to the overall system performance. To mitigate inter-cell interference in the HTN, we propose a new rate-split transmission scheme which has following characteristics. First, it guarantees serving user''s QoS by deciding common message power for an interfered user. Second, it is a low complex scheme using only ISNR (Interference to Signal and Noise Ratio) feedback between a macro-base station and a femto-base station. Third, it operates in a distributed manner. The performance evaluation shows that the proposed algorithm significantly reduces the interference for severely interfered users while guaranteeing serving user''s QoS.

Relaying and orthogonal frequency division multiple access (OFDMA) are the accepted technologies for emerging wireless communications standards. The activities in many wireless standardization bodies and forums, for example IEEE 802.16 j/m and LTE-Advanced, attest to this fact. The availability or lack thereof of efficient radio resource management (RRM) could make or mar the opportunities in these networks. This paper therefore provides a comprehensive RRM algorithm for OFDMA-based multi-cellular fixed relay networks in a way to ensure fairness among users with minimal impact on the network throughput (in contrast, pure opportunistic RRM techniques always favor users with good channel conditions). Unlike the majority of works in the literature, our proposed scheme is queue-aware and jointly performs routing, fair scheduling, and load balancing among cell nodes. The routing strategy has inherent learning ability and it dynamically converges to better routes.

Near maximum-likelihood(ML) detections with reduced complexity are promising techniques for coded multiple- input multiple-output (MIMO) systems. Particularly, the QRM- MLD algorithm is often considered in practical applications since it can easily control receiver's complexity. However, it provides inaccurate log likelihood ratio (LLR) values due to the limited number of candidate symbol vectors. To overcome the problem, this paper presents an efficient LLR computation algorithm. The proposed algorithm calculates approximate LLR values calculated at every stage instead of computing LLR values only at the last stage. At each stage, the proposed algorithm updates approximate LLR values to reflect their reliability on the LLR value. Simulation results show that the proposed algorithm can obtain better performance than conventional LLR calculation scheme especially when low order modulation is employed, or the number of considering candidate vectors is small.