As the sixth-generation (6G) networks evolve towards a deep integration of communication and computation (ICC), they face challenges of inherent interference and resource competition between heterogeneous services. To address this issue, this paper investigates an uplink ICC system enhanced by a stacked intelligent metasurface (SIM), where SIM’s unique multi-layer structure transforms the wireless channel into a controllable, task-oriented medium. The system is designed to support the coexistence of over-the-air computation (AirComp) tasks, which require high-precision results, and traditional tasks that demand high-quality communication. To this end, we formulate a joint optimization framework aiming to minimize the total mean squared error (MSE) of all computation tasks while strictly guaranteeing the communication quality of service (QoS). To solve the highly non-convex problem of synergistically designing the system resources, we propose an efficient alternating optimization (AO) algorithm. Simulation results demonstrate that the proposed algorithm not only converges rapidly but also achieves up to a 95.2% reduction in total computation MSE compared to an ICC system without SIM, while also significantly outperforming other benchmark schemes, validating the great potential of SIM in proactively managing multi-service conflicts and enabling efficient ICC.

Joint communication and sensing has been a key technology in 6G. By integrating sensing into maritime communications, ships can communicate with the base station while sensing the surrounding environment to ensure safe navigation. In this paper, we introduce an integrated satellite-terrestrial maritime system (ISTMS) with joint communication and sensing based on the same radio-frequency signals. Specifically, the terrestrial base station (TBS) and low Earth orbit (LEO) satellite provide communication services for near-shore users (NSUs) and offshore users (OSUs), respectively, while simultaneously performing target sensing. Based on a differential evolution method (DE), we propose a sensing algorithm, which can enhance the location accuracy and reduce resource consumption. Furthermore, we derive the key performance metrics for both communication and sensing. Through joint beamforming optimization of the TBS and LEO satellite, we maximize the sum rate of maritime users while satisfying target localization accuracy requirements and transmit power constraints. Finally, extensive simulation results demonstrate the effectiveness of the proposed algorithms in terms of location accuracy and transmission rate compared with the baseline algorithms.

With the growing demand for satellite sensing and communication, the limited wireless resources are difficult to support multiple satellite systems. Therefore, it is desired to investigate integrated sensing and communication (ISAC) in low Earth orbit (LEO) satellite systems to enable multi-functionality within a single satellite, thereby saving both spectrum and orbital resources. In this paper, a framework for ISAC in LEO satellite systems is established, where a satellite can simultaneously sense multiple targets and serve multiple communication users (CUs) over the same spectrum. Considering the limited onboard energy of satellite, a novel robust beamforming design algorithm is developed with the goal of minimizing total transmit power while satisfying the mean squared error (MSE) requirements for sensing and signal-to-interference-plus-noise ratio (SINR) requirements for communication in presence of channel phase uncertainty which exacerbates the cross-functional interference. According to theoretical analysis, the proposed algorithm for ISAC in LEO satellite systems is effective. Moreover, extensive simulations confirm the superiority of the proposed algorithm over baselines.

Next-generation low Earth orbit (LEO) satellite constellations face critical bottlenecks in spectral efficiency and onboard hardware complexity. To overcome these limitations, this paper introduces a novel architecture enabled by metasur-face antennas (MAs) at the LEO satellites. In particular, MAs are metasurface-integrated feed antennas that perform highprecision beamforming directly in the wave domain, thereby effectively mitigating multi-user interference. Based on such an antenna architecture, a weighted sum rate (WSR) maximization problem is formulated by jointly optimizing the scheduling of feed antennas to terrestrial users (TUs) and the passive beamforming of the metasurface for system performance enhancement. To address this mixed-integer nonlinear programming (MINLP) challenge, an alternating optimization (AO)-based joint scheduling and beamforming algorithm is proposed. On the one hand, the proposed algorithm incorporates a polynomial-time minimum-cost maximum-flow (MCMF) method, which is dedicated to the optimal scheduling of feed antennas and TUs. On the other hand, it adopts a weighted minimum mean square error (WMMSE) method integrated with semidefinite relaxation (SDR) technique, which is tailored for metasurface beamforming design. Simulation results confirm the effectiveness of the proposed algorithm for MA-enabled LEO satellite constellation communications.

To solve the problems of low system security and poor transmission quality in integrated satellite-terrestrial communication systems due to eavesdroppers and channel uncertainties, a robust resource allocation (RA) problem is studied. First, considering the constraints of users’ quality of service, the maximum transmit power of the satellite and the base station (BS), and the bounded channel uncertainties, an RA optimization problem is established by jointly optimizing the beamforming vectors, artificial noise (AN) vectors, and power allocation factors. Then, S-Procedure, successive convex approximation (SCA), and alternating optimization are adopted to convert the nonconvex problem with parameter perturbation into a convex one that can be solved efficiently. Finally, a robust RA algorithm based on the alternating approach is proposed to obtain the solutions. Simulation results show that the proposed algorithm has good security and robustness, and the outage probability is reduced by 9.12% compared to the traditional nonrobust algorithms without AN.

Low earth orbit (LEO) satellite constellations are becoming a cornerstone of next-generation satellite networks, enabling worldwide high-precision navigation and high-quality remote sensing. This paper proposes a novel dual-function LEO satellite constellation frame structure that effectively integrating navigation and remote sensing. Then, the Cramer-Rao bound (CRB)-based positioning, velocity measurement, and timing (PVT) error and the signal-to-ambiguity-interference-noise ratio (SAINR) are derived as performance metrics for navigation and remote sensing, respectively. Based on it, a joint beamforming design is proposed by minimizing the average weighted PVT error for navigation user equipments (UEs) while ensuring SAINR requirement for remote sensing. Simulation results validate the proposed multi-satellite cooperative beamforming design, demonstrating its effectiveness as an integrated solution for next-generation multi-function LEO satellite constellations.

In this paper, we propose to exploit movable elements of intelligent reflecting surface (IRS) to enhance the overall performance of integrated sensing and communication (ISAC) systems. Firstly, focusing on a single-user scenario, we reveal the function of movable elements by performance analysis, and then design a joint beamforming and element position optimization scheme. Further, we extend it to a general multi-user scenario, and also propose an element position optimization scheme according to the derived performance expressions. Finally, simulation results confirm that the movement of IRS elements can improve the communication rate and the sensing accuracy, and especially broaden the coverage of ISAC.

Orthogonal delay-Doppler division multiplexing (ODDM) modulation has recently been regarded as a promising technology to provide reliable communications in high-mobility situations. Accurate and low-complexity channel estimation is one of the most critical challenges for massive multiple input multiple output (MIMO) ODDM systems, mainly due to the extremely large antenna arrays and high-mobility environments. To overcome these challenges, this paper addresses the issue of channel estimation in downlink massive MIMO-ODDM systems and proposes a low-complexity algorithm based on memory approximate message passing (MAMP) to estimate the channel state information (CSI). Specifically, we first establish the effective channel model of the massive MIMO-ODDM systems, where the magnitudes of the elements in the equivalent channel vector follow a Bernoulli-Gaussian distribution. Further, as the number of antennas grows, the elements in the equivalent coefficient matrix tend to become completely random. Leveraging these characteristics, we utilize the MAMP method to determine the gains, delays, and Doppler effects of the multi-path channel, while the channel angles are estimated through the discrete Fourier transform method. Finally, numerical results show that the proposed channel estimation algorithm approaches the Bayesian optimal results when the number of antennas tends to infinity and improves the channel estimation accuracy by about 30% compared with the existing algorithms in terms of the normalized mean square error.

In this paper, we explore the integration of communication and synthetic aperture radar (SAR)-based remote sensing in low Earth orbit (LEO) satellite systems to provide real-time SAR imaging and information transmission. Considering the high-mobility characteristics of satellite channels and limited processing capabilities of satellite payloads, we propose an integrated communication and remote sensing architecture based on an orthogonal delay-Doppler division multiplexing (ODDM) signal waveform. Both communication and SAR imaging functionalities are achieved with an integrated transceiver onboard the LEO satellite, utilizing the same waveform and radio frequency (RF) front-end. Based on such an architecture, we propose a transmission protocol compatible with the 5G NR standard using downlink pilots for joint channel estimation and SAR imaging. Furthermore, we design a unified signal processing framework for the integrated satellite receiver to simultaneously achieve high-performance channel sensing, low-complexity channel equalization and interference-free SAR imaging. Finally, the performance of the proposed integrated system is demonstrated through comprehensive analysis and extensive simulations in the sub-6 GHz band. Moreover, a software-defined radio (SDR) prototype is presented to validate its effectiveness for real-time SAR imaging and information transmission in satellite direct-connect user equipment (UE) scenarios within the millimeter-wave (mmWave) band.

To provide multiple-satellite coverage for global Internet of Things (IoT), a low Earth orbit (LEO) satellite IoT constellation usually contains multiple-layer orbits with different altitudes. However, the performance of multiple-layer LEO satellite IoT constellations under practical Rician fading satellite channels remains unknown due to complex theoretical modeling and intractable mathematical analysis. To address these challenges, this paper proposes a stochastic geometry-based modeling and analysis framework for multiple-layer LEO satellite IoT constellations, integrating Rician channel modeling and Cox point processes. Specifically, we introduce a novel channel approximation method to overcome the intractable expressions caused by the Rician fading. Building on this method, we derive exact closed-form expressions for key performance metrics, including connectivity probability, coverage probability, and transmission rate, especially in the case of IoT short-packet transmission. Extensive simulation results validate the accuracy and effectiveness of the proposed model and reveal significant design insights. The results not only provide new theoretical perspectives for modeling and analysis of LEO satellite IoT constellations but also offer practical guidance for system deployment and optimization.

Low Earth orbit (LEO) satellite Internet of Things (IoT) has been identified as one of the important components of the sixth-generation (6G) non-terrestrial networks (NTN) to provide ubiquitous connectivity. Due to the low orbit altitude and high mobility, a massive number of satellites are required to form a global continuous coverage constellation, leading to a high construction cost. To this end, this paper proposes a LEO satellite IoT constellation design algorithm with the goal of minimizing the total cost while satisfying quality of service (QoS) requirements in terms of coverage ratio and communication quality. Specifically, with a novel fitness function and efficient algorithm’s operators, the proposed algorithm converges more quickly and achieves lower constellation construction cost compared to baseline algorithms under the same QoS requirements. Theoretical analysis proves the global and fast convergence of the proposed algorithm due to a novel fitness function. Finally, extensive simulation results confirm the effectiveness of the proposed algorithm in LEO satellite IoT constellation design.

Joint source-channel coding (JSCC) offers a promising avenue for enhancing transmission efficiency by jointly incorporating source and channel statistics into the system design. A key advancement in this area is the deep joint source and channel coding (DeepJSCC) technique that designs a direct mapping of input signals to channel symbols parameterized by a neural network, which can be trained for arbitrary channel models and semantic quality metrics. This paper advances the DeepJSCC framework toward a semantics-aligned, high-fidelity transmission approach, called semantics-guided diffusion DeepJSCC (SGD-JSCC). Existing schemes that integrate diffusion models (DMs) with JSCC face challenges in transforming random generation into accurate reconstruction and adapting to varying channel conditions. SGD-JSCC incorporates two key innovations: (1) utilizing some inherent information that contributes to the semantics of an image, such as text description or edge map, to guide the diffusion denoising process; and (2) enabling seamless adaptability to varying channel conditions with the help of a semantics-guided DM for channel denoising. The DM is guided by diverse semantic information and integrates seamlessly with DeepJSCC. In a slow fading channel, SGD-JSCC dynamically adapts to the instantaneous channel state information (CSI) directly estimated from the channel output, thereby eliminating the need for additional pilot transmissions for channel estimation. In a fast fading channel, we introduce a training-free denoising strategy, allowing SGD-JSCC to effectively adjust to fluctuations in channel gains. Numerical results demonstrate that, guided by semantic information and leveraging the powerful DM, our method outperforms existing DeepJSCC schemes, delivering satisfactory reconstruction performance even at extremely poor channel conditions. The proposed scheme highlights the potential of incorporating diffusion models in future communication systems. The code and pretrained checkpoints will be publicly available at https://github.com/MauroZMJ/SGDJSCC, allowing integration of this scheme with existing DeepJSCC models, without the need for retraining from scratch.

To further enhance the performance of intelligent reflecting surface (IRS) aided integrated sensing and communication (ISAC) system, the conception of movable reflection elements is introduced to IRS. We derive the performance bound of both communication and sensing in the context of movable reflection elements, and then design a joint transmit beamforming and element position design algorithm. Numerical results demonstrate that the proposed algorithm, using angular information, aligns with the performance of both communicationonly systems and sensing-only systems with perfect channel state information (CSI).

In this paper, we investigate the integration of communication and synthetic aperture radar (SAR)-based remote sensing in low Earth orbit (LEO) satellite systems. To address the high-mobility characteristic of LEO satellites, we propose an integrated system architecture based on an orthogonal delay-Doppler division multiplexing (ODDM) signal waveform. Specifically, we provide a wireless frame compatible with the 5G NR standard for signal sharing and design a unified channel sensing scheme that utilizes shared ODDM signals for both channel estimation in communication and interference-free range reconstruction in SAR imaging. Finally, numerical simulation results confirm the effectiveness of the proposed scheme.

Low earth orbit (LEO) satellite Internet of Things (IoT) has been recognized as a pivotal element within the realm of sixth-generation (6 G) non-terrestrial networks (NTN), aimed at delivering ubiquitous connectivity. Due to the low orbit altitude and fast movement speed, a massive number of satellites are needed to form a satellite constellation, resulting in substantial construction costs. To this end, this paper proposes a LEO satellite IoT constellation design algorithm with the goal of minimizing the total cost while satisfying quality of service (QoS) requirements in terms of coverage ratio and communication quality. Simulation results validate the efficiency of the proposed algorithm in LEO satellite IoT constellation.

In this paper, we present an integrated terrestrial and satellite maritime communication system, where a shore-based terrestrial base station (TBS) and a low Earth orbit (LEO) satellite cooperatively provide wide-area communication services to maritime users. We conduct performance analysis for the integrated satellite-terrestrial maritime communication system. Specifically, we analyze the transmission rate and coverage probability of near-shore and off-shore users respectively according to the maritime communication environment. Besides, in order to better understand the impact of some key parameters, we also make asymptotic analysis in some special cases. Further, we design an optimization algorithm to maximize the coverage probability of near-shore users by adjusting the transmission power of TBS and the LEO satellite, while ensuring the both off-shore and near-shore users can meet the minimum communication rate requirements. Finally, extensive numerical analysis results verify the accuracy of the theoretical results and the effectiveness of the proposed optimization algorithm in the maritime communication system.

Presents corrections to the paper, Coverage Rate Analysis for Integrated Sensing and Communication Networks.

Semantic communication (SemCom) is emerging as a key technology for future sixth-generation (6G) systems. Unlike traditional bit-level communication (BitCom), SemCom directly optimizes performance at the semantic level, leading to superior communication efficiency. Nevertheless, the task-oriented nature of SemCom renders it challenging to completely replace BitCom. Consequently, it is desired to consider a semantic-bit coexisting communication system, where a base station (BS) serves SemCom users (sem-users) and BitCom users (bit-users) simultaneously. Such a system faces severe and heterogeneous inter-user interference. In this context, this paper provides a new semantic-bit coexisting communication framework and proposes a spatial beamforming scheme to accommodate both types of users. Specifically, we consider maximizing the semantic rate for semantic users while ensuring the quality-of-service (QoS) requirements for bit-users. Due to the intractability of obtaining the exact closed-form expression of the semantic rate, a data driven method is first applied to attain an approximated expression via data fitting. With the resulting complex transcendental function, majorization minimization (MM) is adopted to convert the original formulated problem into a multiple-ratio problem, which allows fractional programming (FP) to be used to further transform the problem into an inhomogeneous quadratically constrained quadratic programs (QCQP) problem. Solving the problem leads to a semi-closed form solution with undetermined Lagrangian factors that can be updated by a fixed point algorithm. This method is referred to as the MM-FP algorithm. Additionally, inspired by the semi-closed form solution, we also propose a low-complexity version of the MM-FP algorithm, called the low-complexity MM-FP (LP-MM-FP), which alleviates the need for iterative optimization of beamforming vectors. Extensive simulation results demonstrate that the proposed MM-FP algorithm outperforms conventional beamforming algorithms such as zero-forcing (ZF), maximum ratio transmission (MRT), and weighted minimum mean-square error (WMMSE). Moreover, the proposed LP-MMFP algorithm achieves comparable performance with the WMMSE algorithm but with lower computational complexity.

In practical intelligent reflecting surface (IRS)-assisted multiuser communication systems, inevitable imperfections such as hardware impairments, imperfect channel state information (CSI), and the limited resolution of the IRS phase shifts would introduce interference and thus cause significant performance degradation. As an interference management strategy, rate-splitting multiple access (RSMA) employs the rate-splitting (RS) principle to partition user information into common and private parts, thereby offering enhanced robustness. Accounting for practical imperfections, this study investigates robust beamforming design in IRS-assisted multiuser systems under the RSMA architecture. First, we introduce a system model that captures these non-ideal factors and evaluate their impacts on communication performance. To enhance the performance of the considered system, a weighted sum rate maximization problem is formulated, for which a sample average approximation (SAA)-based robust algorithm is proposed to jointly optimize the IRS phase shifts and the beamforming matrix at the base station (BS). Simulation results demonstrate that the IRS-assisted RSMA system exhibits superior robustness compared to the IRS-assisted space division multiple access (SDMA) system in the presence of inevitable imperfections. Furthermore, the proposed SAA-based robust algorithm outperforms existing benchmark algorithms, highlighting its effectiveness and robustness.

Low Earth orbit (LEO) satellite constellations play a pivotal role in sixth-generation (6G) wireless networks by providing global coverage, massive connections, and huge capacity. In this paper, we present a novel LEO satellite constellation communication framework, where a reconfigurable intelligent surface-mounted unmanned aerial vehicle (RIS-UAV) is deployed to improve the communication quality of multiple terrestrial user equipments (UEs) under the condition of long distance between satellite and ground. To reduce the overhead for channel state information (CSI) acquisition with multiple-satellite collaboration, statistical CSI (sCSI) is utilized in the system. In such a situation, we first derive an approximated but exact expression for ergodic rate of each UE. Then, we aim to maximize the minimum approximated UE ergodic rate by the proposed alternating optimization (AO)-based algorithm that jointly optimizes LEO satellite beamforming, RIS phase shift, and UAV trajectory. Finally, extensive simulations are conducted to demonstrate the superiority of the proposed algorithm in terms of spectrum efficiency over baseline algorithms.

With the explosive growth of maritime activities, it is expected to provide seamless communications with quality of service (QoS) guarantee over broad sea area. In the context, this paper proposes a space-air-ground-sea integrated maritime communication architecture combining satellite, unmanned aerial vehicle (UAV), terrestrial base station (TBS) and unmanned surface vessel (USV). Firstly, according to the distance away from the shore, the whole marine space is divided to coastal area, offshore area, middle-sea area and open-sea area, the maritime users in which are served by TBS, USV, UAV and satellite, respectively. Then, by exploiting the potential of integrated maritime communication system, a joint beamforming and trajectory optimization algorithm is designed to maximize the minimum transmission rate of maritime users. Finally, theoretical analysis and simulation results validate the effectiveness of the proposed algorithm.

Integrated sensing and communication (ISAC) has emerged as a promising technology to facilitate high-rate communications and super-resolution sensing, particularly operating in the millimeter wave (mmWave) band. However, the vulnerability of mmWave signals to blockages severely impairs ISAC capabilities and coverage. To tackle this, an efficient and low-cost solution is to deploy distributed reconfigurable intelligent surfaces (RISs) to construct virtual links between the base stations (BSs) and users in a controllable fashion. In this paper, we model the generalized RIS-assisted mmWave ISAC networks considering the blockage effect, and examine the beneficial impact of RISs on the coverage rate utilizing stochastic geometry. Based on the proposed beam patterns and user association policies, we derive the conditional coverage probability and ergodic rate of communication and sensing dual functions for two association cases, as well as the marginal coverage rate using the distance-dependent thinning method. Taking into account the coupling effect of ISAC dual functions within the same network topology, we further calculate the joint coverage probability of ISAC performance. Simulation results verify the accuracy of derived theoretical formulations, and illustrate the impact of the RIS aperture, blockage, BS and RIS densities on ISAC coverage rates, which provide valuable guidelines for the practical network deployment. Specifically, our results indicate the superiority of the RIS deployment with the density of 40 km${}^{-2}$BSs, and that the joint coverage rate of ISAC performance exhibits potential growth from 62% to 97% with the deployment of RISs.

Integrated sensing and communication (ISAC) and ubiquitous connectivity are two usage scenarios of sixth generation (6G) networks. In this context, low earth orbit (LEO) satellite constellations, as an important component of 6G networks, is expected to provide ISAC services across the globe. In this paper, we propose a novel dual-function LEO satellite constellation framework that realizes information communication for multiple user equipments (UEs) and location sensing for interested target simultaneously with the same hardware and spectrum. In order to improve both information transmission rate and location sensing accuracy within limited wireless resources under dynamic environment, we design a multiple-satellite cooperative information communication and location sensing algorithm by jointly optimizing communication beamforming and sensing waveform according to the characteristics of LEO satellite constellation. Finally, extensive simulation results are presented to demonstrate the competitive performance of the proposed algorithms.

Orthogonal delay-Doppler division multiplexing (ODDM) modulation has recently been considered a promising technology for enhancing communication system performance in high-mobility scenarios. Accurate and low-complexity channel estimation is one of the most significant challenges for massive multiple-input multiple-output (MIMO) ODDM systems, mainly due to the massive antenna arrays and high-mobility environments. In this paper, we focus on the downlink massive MIMO-ODDM communication systems, and propose a two-stage low-complexity channel estimation algorithm. Specifically, we first derive the effective channel model of the massive MIMO-ODDM systems, where the elements of the channel matrix do not follow a Bernoulli-Gaussian distribution, but their magnitudes do. Utilizing this characteristic, we employ the memory approximate message passing method to estimate the gains, delay, and Doppler of the multi-path channel, while the angles of the channel are estimated using the discrete Fourier transform method, achieving low-complexity Bayes-optimal results. Finally, numerical results demonstrate that the proposed algorithm can achieve improved estimation results, surpassing existing algorithms by approximately 2 dB.

Integrated sensing and communication (ISAC) is increasingly recognized as a pivotal technology for next-generation cellular networks, offering mutual benefits in both sensing and communication capabilities. This advancement necessitates a re-examination of the fundamental limits within networks where these two functionalities coexist via shared spectrum and infrastructures. However, traditional stochastic geometry-based performance analyses are confined to either communication or sensing networks separately. This paper bridges this gap by introducing a generalized stochastic geometry framework in ISAC networks. Based on this framework, we define and calculate the coverage rate of sensing and communication performance under resource constraints. Further, we present theoretical results for the coverage rate of unified ISAC performance, taking into account the coupling effects of dual functions in coexistence networks. Extensive numerical results validate the accuracy of all theoretical derivations, and also indicate that denser networks significantly enhance ISAC coverage. Specifically, increasing the base station density from 1 km-2to 10 km-2can boost the ISAC coverage rate from 1.4% to 39.8%.

This paper investigates an intelligent reflecting surface (IRS) assisted integrated sensing and communication (ISAC) with high-mobility systems, where the orthogonal time frequency space (OTFS) modulation is employed to leverage the Delay- Doppler (DD) spread. We propose a subspace-based beamforming design algorithm, which optimizes the phase shifts at the IRS and the combining vector at the base station (BS) to enhance the communication performance subject to the constraint on the sensing accuracy. Moreover, we derived closed-form solutions for the optimization problems. Numerical results affirm the effectiveness of our proposed beamforming design algorithm in high-mobility scenarios.

This paper introduces an intelligent reflecting surface (IRS)-aided integrated sensing and communications (ISAC) framework for joint signal demodulation and localization in multiuser millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. A time block is divided into uplink estimation stage and downlink transmission stage. In the uplink estimation stage, a joint active beamforming at the BS and passive beamforming at the IRSs are designed based on estimated angle of arrival (AoA) information. In the downlink transmission stage, a joint signal demodulation and location sensing algorithm at the users is proposed by exploiting the statistical properties of the received signals. Numerical results demonstrate that the proposed ISAC framework can achieve centimeter-level localization accuracy while maintaining communication performance compared to communication-only systems with perfect channel state information (CSI).

In this paper, we provide a novel dual-function low earth orbit (LEO) satellite constellation architecture that provides information communication services while enabling location sensing of potential target. In order to improve both information transmission rate and location sensing accuracy, we propose a joint communication beamforming and sensing waveform design algorithm. Finally, numerical results and Monte Carlo simulations are presented to demonstrate the competitive performance of the proposed algorithm.

In mission-critical Industrial Internet of Things (IIoT), multiple sensors make independent observations at different locations and then transmit them to the base station (BS) to obtain a global system state vector. Uploading observation information to the BS by active sensors is a multiple access process. As the task is to complete state estimation instead of maximizing the physical-layer capacity, conventional multiple access schemes cannot be applied directly to mission-critical IIoT applications. Therefore, next generation multiple access (NGMA) techniques are urgently needed to realize the key performance indicators for the design of IIoT networks. Note that, in mission-critical IIoT systems, each sensor can only obtain the observation of a subset of state variables, and the BS only cares about the state information embedded in that observation not the identities of the sensors. This indicates that the whole process of data transmission and state estimation can be totally unsourced, thus resulting in a highly efficient IIoT system implementation. Based on this crucial finding, in this article, we propose an unsourced multiple access (UMA)-based mission-critical IIoT system. Moreover, a decoupled UMA (D-UMA) scheme is proposed to improve transmission efficiency and state estimation performance. We analyse the fundamental aspects of how our design affects and guarantees the controllability, observability, and stability of an IIoT control system. Simulation results verify the remarkable performance of the proposed scheme compared with the conventional orthogonal multiple access (OMA) and nonorthogonal multiple access (NOMA) schemes.

In order to provide wireless services for wide sea area, this article designs an integrated satellite-terrestrial maritime communication framework. Specifically, the terrestrial base station (TBS) serves near-shore users, while the low Earth orbit (LEO) satellite communicates with off-shore users. We aim to improve the overall performance of integrated satellite-terrestrial maritime communication system. Thus, it makes sense to jointly optimize transmit beamforming at the TBS and LEO satellite. Due to sea wave fluctuation, the obtained channel state information (CSI) is often imperfect. In this context, a robust beamforming design algorithm is proposed with the goal of minimizing the total power consumption of integrated satellite-terrestrial maritime communication system while satisfying Quality-of-Service (QoS) requirements. Both theoretical analysis and simulation results confirm the effectiveness of the proposed algorithm in maritime communications.

Integrated sensing and communication (ISAC) is increasingly recognized as a pivotal technology for next-generation cellular networks, offering mutual benefits in both sensing and communication capabilities. This advancement necessitates a re-examination of the fundamental limits within networks where these two functions coexist via shared spectrum and infrastructures. However, traditional stochastic geometry-based performance analyses are confined to either communication or sensing networks separately. This paper bridges this gap by introducing a generalized stochastic geometry framework in ISAC networks. Based on this framework, we define and calculate the coverage and ergodic rate of sensing and communication performance under resource constraints. Then, we shed light on the fundamental limits of ISAC networks by presenting theoretical results for the coverage rate of the unified performance, taking into account the coupling effects of dual functions in coexistence networks. Further, we obtain the analytical formulations for evaluating the ergodic sensing rate constrained by the maximum communication rate, and the ergodic communication rate constrained by the maximum sensing rate. Extensive numerical results validate the accuracy of all theoretical derivations, and also indicate that denser networks significantly enhance ISAC coverage. Specifically, increasing the base station density from$1~\text {km}^{-2}$to$10~\text {km}^{-2}$can boost the ISAC coverage rate from 1.4% to 39.8%. Further, results also reveal that with the increase of the constrained sensing rate, the ergodic communication rate improves significantly, but the reverse is not obvious.

The combination of terahertz and massive multiple-input multiple-output (MIMO) is promising for meeting the increasing data rate demand of future wireless communication systems thanks to the significant bandwidth and spatial degrees of freedom. However, unique channel features, such as the near-field beam split effect, make channel estimation particularly challenging in terahertz massive MIMO systems. On one hand, adopting the conventional angular domain transformation dictionary designed for low-frequency far-field channels will result in degraded channel sparsity and destroyed sparsity structure in the transformed domain. On the other hand, most existing compressive sensing based channel estimation algorithms cannot achieve high performance and low complexity simultaneously. To alleviate these issues, in this study, we first adopt frequency-dependent near-field dictionaries to maintain good channel sparsity and sparsity structure in the transformed domain under the near-field beam split effect. Then, a deep unfolding based wideband terahertz massive MIMO channel estimation algorithm is proposed. In each iteration of the approximate message passing-sparse Bayesian learning algorithm, the optimal update rule is learned by a deep neural network (DNN), whose architecture is customized to effectively exploit the inherent channel patterns. Furthermore, a mixed training method based on novel designs of the DNN architecture and the loss function is developed to effectively train data from different system configurations. Simulation results validate the superiority of the proposed algorithm in terms of performance, complexity, and robustness.

Approximate message passing (AMP) algorithms are iterative methods for signal recovery in noisy linear systems. In some scenarios, AMP algorithms need to operate within a distributed network. To address this challenge, the distributed extensions of AMP (D-AMP, FD-AMP) and orthogonal/vector AMP (D-OAMP/D-VAMP) were proposed, but they still inherit the limitations of centralized algorithms. In this letter, we propose distributed memory AMP (D-MAMP) to overcome the IID matrix limitation of D-AMP/FD-AMP, as well as the high complexity and heavy communication cost of D-OAMP/D-VAMP. We introduce a matrix-by-vector variant of MAMP tailored for distributed computing. Leveraging this variant, D-MAMP enables each node to execute computations utilizing locally available observation vectors and transform matrices. Meanwhile, global summations of locally updated results are conducted through message interaction among nodes. For acyclic graphs, D-MAMP converges to the same mean square error performance as the centralized MAMP.

This paper delves into the potential of intelligent reflecting surfaces (IRSs) in enabling integrated sensing and communication (ISAC) in multi-user multi-path scenarios. We introduce a three-dimensional (3D) multi-user ISAC framework with distributed IRSs, which offers simultaneous signal demodulation, channel estimation, and localization. The transmission is divided into a user access stage and a downlink transmission stage. In the first stage, we propose an algorithm for simultaneous uplink signal demodulation and angles of arrival (AoA) estimation at the semi-passive IRS. Moreover, a joint active and passive beamforming scheme inspired by radar-communication, is proposed to enhance both communication and localization performance in the downlink stage, while eliminating the need for distinct localization reference signals. Numerical results demonstrate that the proposed ISAC framework achieves centimeter-level localization accuracy while maintaining comparable communication performance to communication-only systems, thus validating its effectiveness.

In this paper, we explore an efficient uncoupled unsourced random access (UURA) scheme for 6G massive communication. UURA is a typical framework of unsourced random access that addresses the problems of codeword detection and message stitching, without the use of check bits. Firstly, we establish a framework for UURA, allowing for immediate decoding of sub-messages upon arrival. Thus, the processing delay is effectively reduced due to the decreasing waiting time. Next, we propose an integrated decoding algorithm for sub-messages by leveraging matrix information geometry (MIG) theory. Specifically, MIG is applied to measure the feature similarities of codewords belonging to the same user equipment, and thus sub-message can be stitched once it is received. This enables the timely recovery of a portion of the original message by simultaneously detecting and stitching codewords within the current sub-slot. Furthermore, we analyze the performance of the proposed integrated decoding-based UURA scheme in terms of computational complexity and convergence rate. Finally, we present extensive simulation results to validate the effectiveness of the proposed scheme in 6G wireless networks.

In this paper, we investigate the issue of satellite-terrestrial computing in the sixth generation (6G) wireless networks, where multiple terrestrial base stations (BSs) and low earth orbit (LEO) satellites collaboratively provide edge computing services to ground user equipments (GUEs) and space user equipments (SUEs) over the world. In particular, we design a complete process of satellite-terrestrial computing in terms of communication and computing according to the characteristics of 6G wireless networks. In order to minimize the weighted total energy consumption while ensuring delay requirements of computing tasks, an energy-efficient satellite-terrestrial computing algorithm is put forward by jointly optimizing offloading selection, beamforming design and resource allocation. Finally, both theoretical analysis and simulation results confirm fast convergence and superior performance of the proposed algorithm for satellite-terrestrial computing in 6G wireless networks.

In high-dynamic low earth orbit (LEO) satellite communication (SATCOM) systems, frequent channel state information (CSI) acquisition consumes a large number of pilots, which is intolerable in resource-limited SATCOM systems. To tackle this problem, we propose to track the state-dependent parameters including Doppler shift and channel angles, by exploiting the physical and approximate on-orbit mobility characteristics for LEO satellite and ground users (GUs), respectively. As a prerequisite for tracking, we formulate the state evolution models for kinematic (state) parameters of both satellite and GUs, along with the measurement models that describe the relationship between the state-dependent parameters and states. Then the rough estimation of state-dependent parameters is initially conducted, which is used as the measurement results in the subsequent state tracking. Concurrently, the measurement error covariance is predicted based on the formulated Cramér-Rao lower bound (CRLB). Finally, with the extended Kalman filter (EKF)-based state tracking as the bridge, the Doppler shift and channel angles can be further updated and the CSI can also be acquired. Simulation results show that compared to the rough estimation methods, the proposed joint parameter and channel tracking (JPCT) algorithm performs much better in the estimation of state-dependent parameters. Moreover, as to the CSI acquisition, the proposed algorithm can utilize a shorter pilot sequence than benchmark methods under a given estimation accuracy.

In this paper, we investigate integrated sensing and communication (ISAC) in high-mobility systems with the aid of an intelligent reflecting surface (IRS). To exploit the benefits of Delay-Doppler (DD) spread caused by high mobility, orthogonal time frequency space (OTFS)-based frame structure and transmission framework are proposed. In such a framework, we first design a low-complexity ratio-based sensing algorithm for estimating the velocity of mobile user. Then, we analyze the performance of sensing and communication in terms of achievable mean square error (MSE) and achievable rate, respectively, and reveal the impact of key parameters. Next, with the derived performance expressions, we jointly optimize the phase shift matrix of IRS and the receive combining vector at the base station (BS) to improve the overall performance of integrated sensing and communication. Finally, extensive simulation results confirm the effectiveness of the proposed algorithms in high-mobility systems.

In this paper, we investigate the issue of uplink integrated sensing and communication (ISAC) in 6G wireless networks where the sensing echo signal and the communication signal are received simultaneously at the base station (BS). To effectively mitigate the mutual interference between sensing and communication caused by the sharing of spectrum and hardware resources, we provide a joint sensing transmit waveform and communication receive beamforming design with the objective of maximizing the weighted sum of normalized sensing rate and normalized communication rate. It is formulated as a computationally complicated non-convex optimization problem, which is quite difficult to be solved by conventional optimization methods. To this end, we first make a series of equivalent transformation on the optimization problem to reduce the design complexity, and then develop a deep learning (DL)-based scheme to enhance the overall performance of ISAC. Both theoretical analysis and simulation results confirm the effectiveness and robustness of the proposed DL-based scheme for ISAC in 6G wireless networks.

Data collection and processing timely is crucial for mobile crowd integrated sensing, communication, and computation (ISCC) systems with various applications such as smart home and connected cars, which requires numerous integrated sensing and communication (ISAC) devices to sense the targets and offload the data to the base station (BS) for further processing. However, as the number of ISAC devices grows, there exists intensive interactions among ISAC devices in the processes of data collection and processing since they share the common network resources. In this paper, we consider the environment sensing problem in the large-scale mobile crowd ISCC systems and propose an efficient waveform precoding design algorithm based on the mean field game (MFG). Specifically, to handle the complex interactions among large-scale ISAC devices, we first utilize the MFG method to transform the influence from other ISAC devices into the mean field term and derive the Fokker-Planck-Kolmogorov equation, which models the evolution of the system state. Then, we derive the cost function based on the mean field term and reformulate the waveform precoding design problem. Next, we utilize the G-prox primal-dual hybrid gradient algorithm to solve the reformulated problem and analyze the computational complexity of the proposed algorithm. Finally, simulation results demonstrate that the proposed algorithm can solve the interactions among large-scale ISAC devices effectively in the ISCC process. In addition, compared with other baselines, the proposed waveform precoding design algorithm has advantages in improving communication performance and reducing cost function.

In this paper, we propose an innovative learning-based channel prediction scheme so as to achieve higher prediction accuracy and reduce the requirements of huge amounts and strict sequential format of channel data. Inspired by the idea of the neural ordinary differential equation (Neural ODE), we first prove that the channel prediction problem can be modeled as an ODE problem with a known initial value by analyzing the physical process of electromagnetic wave propagation within a mobile environment. Then, we design a novel physics-inspired spatial channel gradient network (SCGnet), which represents the derivative process of channel varying as a special neural network and can obtain the gradients at any relative displacement needed for the ODE solving. With the SCGnet, the static channel at any location served by the base station is accurately inferred through consecutive propagation and integration. Finally, we design an efficient recurrent positioning algorithm based on some prior knowledge of user mobility to obtain the velocity vector and propose an approximate Doppler compensation method to make up the instantaneous angular-delay domain channel. Only discrete historical channel data is needed for the training, whereas only a few fresh channel measurements are needed for the prediction, which ensures the scheme’s practicability. Comprehensive evaluations show that the proposed scheme is most efficient in representing, learning, and predicting mobile wireless channels.

Low earth orbit (LEO) satellite internet of things (IoT) is a promising way achieving global Internet of Everything, and thus has been widely recognized as an important component of sixth-generation (6G) wireless networks. Yet, due to high-speed movement of the LEO satellite, it is challenging to acquire timely channel state information (CSI) and design effective multibeam precoding for various IoT applications. To this end, this paper provides a deep learning (DL)-based joint channel prediction and multibeam precoding scheme under adverse environments, e.g., high Doppler shift, long propagation delay, and low satellite payload. Specifically, this paper first designs a DL-based channel prediction scheme by using convolutional neural networks (CNN) and long short term memory (LSTM), which predicts the CSI of current time slot according to that of previous time slots. With the predicted CSI, this paper designs a DL-based robust multibeam precoding scheme by using a channel augmentation method based on variational auto-encoder (VAE). Finally, extensive simulation results confirm the effectiveness and robustness of the proposed scheme in LEO satellite IoT.

Reconfigurable intelligent surface (RIS) has become a promising technology to realize the programmable wireless environment via steering the incident signal in fully customizable ways. However, a major challenge in RIS-aided communication systems is the simultaneous design of the precoding matrix at the base station (BS) and the phase shifting matrix of the RIS elements. This is mainly attributed to the highly non-convex optimization space of variables at both the BS and the RIS, and the diversity of communication environments. Generally, traditional optimization methods for this problem suffer from the high complexity, while existing deep learning based methods are lacking in robustness in various scenarios. To address these issues, we introduce a gradient-based manifold meta learning method (GMML), which works without pre-training and has strong robustness for RIS-aided communications. Specifically, the proposed method fuses meta learning and manifold learning to improve the overall spectral efficiency, and reduce the overhead of the high-dimensional signal process. Unlike traditional deep learning based methods which directly take channel state information as input, GMML feeds the gradients of the precoding matrix and phase shifting matrix into neural networks. Coherently, we design a differential regulator to constrain the phase shifting matrix of the RIS. Numerical results show that the proposed GMML can improve the spectral efficiency by up to 7.31%, and speed up the convergence by 23 times faster compared to traditional approaches. Moreover, they also demonstrate remarkable robustness and adaptability in dynamic settings.

Recently, low-Earth orbit (LEO) satellite Internet of Things (IoT) has received considerable interests due to its global coverage for massive IoT devices distributed over a large area, especially in remote areas, e.g., ocean, desert, and forest. Considering relatively long transmission distance between IoT devices and LEO satellite, we propose a low latency and small overhead sourced grant-free random access (GF-RA) framework, where active devices send their data signals directly without the grant of LEO satellite. In order to detect active device and recover the corresponding data, we design a joint device and data detection algorithm for massive GF-RA in LEO satellite IoT. In particular, the active device maps the data to a codeword of a predetermined and unique codebook, and then sends it to the LEO satellite. By detecting the codeword via maximizing the likelihood function of the received signal, the LEO satellite obtains the active device and recovers the corresponding data. Theoretical analysis shows that the proposed algorithm has a fast convergence behavior and low computational complexity. Finally, we provide extensive simulation results to confirm the effectiveness of the proposed algorithm over baseline ones in LEO satellite IoT.

In this paper, we investigate the issue of integrated communication and computation for multiple time-sensitive computation-intensive user equipments (UEs) with different types of tasks in edge-intelligent networks. Especially, we consider a practical edge-intelligent network with both communication and computation uncertainties, where channel state information (CSI) is partially obtained by the base station (BS) and the task complexity is inaccurately estimated by the mobile edge computing (MEC) server. To effectively mitigate the influences of these unfavorable uncertainties and guarantee user fairness, a task-driven robust design algorithm for integrated communication and computation with the objective of minimizing the maximum system delay among all UEs is put forward by jointly optimizing transmit power at the UEs, receive beamforming at the BS and computing resources at the MEC server based on task types. Both theoretical analysis and simulation results confirm the robustness and the effectiveness of the proposed algorithm for edge-intelligent networks.

Frame slotted Aloha (FSA) is the de facto MAC layer standard protocol in many ultra-low-power IoT applications, such as Radio Frequency Identification (RFID) and Machine to Machine (M2M) communications. As the age of information (AoI) is an emerging and critical metric for quantifying the freshness of the status update information collected in time-sensitive IoT applications, systematic analysis of AoI for FSA is called for. However, very limited work has been done on this topic despite its both theoretical and practical implications for the operation and optimization of FSA. To fill this void, this paper delivers a comprehensive analysis of AoI for four versions of FSA, namely synchronous and asynchronous FSA with and without retransmission. The core technique of our analysis is to model the AoI for FSA as Markov chains to derive statistics on the delay and inter-delivery time. Our central results consist of the lower bounds of AoI, the exact AoI expressions in the four FSA protocols and the optimum frame length for the AoI of FSA. Our analysis reveals the impact of the arrival rate and the protocol parameters on AoI, and also shows that the retransmission would improve AoI when the arrival rate is small.

With the rapid development of Internet of Things (IoT), low earth orbit (LEO) satellite IoT is expected to provide low power, massive connectivity and wide coverage IoT applications. In this context, this paper provides a massive grant-free random access (GF-RA) scheme for LEO satellite IoT. This scheme does not need to change the transceiver, but transforms the received signal to a tensor decomposition form. By exploiting the characteristics of the tensor structure, a Bayesian learning algorithm for joint active device detection and channel estimation during massive GF-RA is designed. Theoretical analysis shows that the proposed algorithm has fast convergence and low complexity. Finally, extensive simulation results confirm its better performance in terms of error probability for active device detection and normalized mean square error for channel estimation over baseline algorithms in LEO satellite IoT. Especially, it is found that the proposed algorithm requires short preamble sequences and support massive connectivity with a low power, which is appealing to LEO satellite IoT.

In this paper, we investigate the issue of integrated localization and computing (ILAC) in edge-intelligent networks. By exploiting the dual-function radio frequency (RF) signals, we put forward a unified design framework for ILAC in edge-intelligent networks where the localization task and the computing task are conducted cooperatively by multiple user equipments (UEs) and multiple base stations (BSs). In particular, a joint resource allocation algorithm is proposed for ILAC by optimizing the available radio and computing resources with the goal of minimizing the weighted total energy consumption while ensuring the performance requirements of the localization task and the computing task. Finally, numerical results verify the effectiveness of the proposed algorithm over baseline ones.

In this article, we investigate an unsourced random access scheme for massive machine-type communications (mMTC) in the sixth-generation (6G) wireless networks with sporadic data traffic. First, we establish a general framework for massive unsourced random access based on a two-layer signal coding, i.e., an outer code and an inner code. In particular, considering Rician fading in the scenario of mMTC, we design a novel codeword activity detection algorithm for the inner code of unsourced random access based on the distribution of received signals by exploiting the maximum-likelihood (ML) method. Then, we analyze the performance of the proposed codeword activity detection algorithm exploiting Fisher Information Matrix, which facilitates the derivative of the approximated distribution of the estimation error of the codeword activity vector when the number of base station (BS) antennas is sufficiently large. Furthermore, for the outer code, we propose an optimization algorithm to allocate the lengths of message bits and parity check bits, so as to strike a balance between the error probability and the complexity required for outer decoding. Finally, extensive simulation results validate the effectiveness of the proposed detection algorithm and the optimized length allocation scheme compared with an existing detection algorithm and a fixed-length allocation scheme.

The core requirement of massive Machine-Type Communication (mMTC) is to support reliable and fast access for an enormous number of machine-type devices (MTDs). In many practical applications, the base station (BS) only concerns the list of received messages instead of the source information, introducing the emerging concept of unsourced random access (URA). Although some massive multiple-input multiple-output (MIMO) URA schemes have been proposed recently, the unique propagation properties of millimeter-wave (mmWave) massive MIMO systems are not fully exploited in conventional URA schemes. In grant-free random access, the BS cannot perform receive beamforming independently as the identities of active users are unknown to the BS. Therefore, only the intrinsic beam division property can be exploited to improve the decoding performance. In this paper, a URA scheme based on beam-space tree decoding is proposed for mmWave massive MIMO system. Specifically, two beam-space tree decoders are designed based on hard decision and soft decision, respectively, to utilize the beam division property. They both leverage the beam division property to assist in discriminating the sub-blocks transmitted from different users. Besides, the first decoder can reduce the searching space, enjoying a low complexity. The second decoder exploits the advantage of list decoding to recover the miss-detected packets. Simulation results verify the superiority of the proposed URA schemes compared to the conventional URA schemes in terms of error probability.

Intelligent reflecting surface (IRS) has emerged as a promising technology to reconfigure the radio propagation environment by dynamically controlling wireless signal’s amplitude and/or phase via a large number of reflecting elements. In contrast to the vast literature on studying IRS’s performance gains in wireless communications, we study in this paper a new application of IRS for sensing/localizing targets in wireless networks. Specifically, we propose a newself-sensing IRSarchitecture where the IRS controller is capable of transmitting probing signals that are not only directly reflected by the target (referred to as the direct echo link), but also consecutively reflected by the IRS and then the target (referred to as the IRS-reflected echo link). Moreover, dedicated sensors are installed at the IRS for receiving both the direct and IRS-reflected echo signals from the target, such that the IRS can sense the direction of its nearby target by applying a customized multiple signal classification (MUSIC) algorithm. However, since the angle estimation mean square error (MSE) by the MUSIC algorithm is intractable, we propose to optimize the IRS passive reflection for maximizing the average echo signals’ total power at the IRS sensors and derive the resultant Cramer-Rao bound (CRB) of the angle estimation MSE. Last, numerical results are presented to show the effectiveness of the proposed new IRS sensing architecture and algorithm, as compared to other benchmark sensing systems/algorithms.

In this paper, we aim to efficiently and accurately predict the static channel impulse response (CIR) with only the user’s position information and a set of channel instances obtained within a certain wireless communication environment. Such a problem is by no means trivial since it needs to reconstruct the high-dimensional information (here the CIR everywhere) from the extremely low-dimensional data (here the location coordinates), which often results in overfitting and large prediction error. To this end, we resort to a novel physics-inspired generative approach. Specifically, we first use a forward deep neural network to infer the positions of all possible images of the source reflected by the surrounding scatterers within that environment, and then use the well-known Gaussian Radial Basis Function network (GRBF) to approximate the amplitudes of all possible propagation paths. We further incorporate the most recently developed sinusoidal representation network (SIREN) into the proposed network to implicitly represent the highly dynamic phases of all possible paths, which usually cannot be well predicted by the conventional neural networks with non-periodic activators. The resultant framework of Cosine-Gaussian Radial Basis Function network (C-GRBFnet) is also extended to the MIMO channel case. Key performance measures including prediction accuracy, convergence speed, network scale and robustness to channel estimation error are comprehensively evaluated and compared with existing popular networks, which show that our proposed network is much more efficient in representing, learning and predicting wireless channels in a given communication environment.

Owing to the inherent central information processing and resource management ability, the cloud radio access network (C-RAN) is a promising network structure for an intelligent and simplified sixth-generation (6G) wireless network. Nevertheless, to further enhance the capacity and coverage, more radio remote heads (RRHs) as well as high-fidelity and low-latency fronthaul links are required, which may lead to high implementation cost. To address this issue, we propose to exploit the intelligent reflecting surface (IRS) as an alternative way to enhance the C-RAN, which is a low-cost and energy-efficient option. Specifically, we consider the uplink transmission where multi-antenna users communicate with the baseband unit (BBU) pool through multi-antenna RRHs and multiple IRSs are deployed between the users and RRHs. RRHs can conduct either point-to-point (P2P) compression or Wyner-Ziv coding to compress the received signals, which are then forwarded to the BBU pool through fronthaul links. We investigate the joint design and optimization of user transmit beamformers, IRS passive beamformers, and fronthaul compression noise covariance matrices to maximize the uplink sum rate subject to fronthaul capacity constraints under P2P compression and Wyner-Ziv coding. By exploiting the Arimoto-Blahut algorithm and semi-definite relaxation (SDR), we propose a successive convex approximation approach to solve non-convex problems, and two iterative algorithms corresponding to P2P compression and Wyner-Ziv coding are provided. Numerical results verify the performance gain brought about by deploying IRS in C-RAN and the superiority of the proposed joint design.

In this work, we study the covert communications in ambient backscatter systems (ABS) with uncontrollable RF source on AWGN channels. In contrast to the prior works impractically assuming the existence of acontrollableRF excitation source, our work arms the receiver of covert information with the full-duplex ability. The covert receiver can emit artificial noise (AN) with variable power to cover up the modulation action of a tag on the excitation signals while receiving the backscattered information. Specifically, we first derive the warden’s optimum power-detection threshold that minimizes the detection error probability. To against the optimal warden, we design the covert backscatter communication policy that determines the feasible region of the AN power at the covert receiver depending on the transmission power of the RF source and the reflection coefficient of the tag and guarantees the covertness constraint. We analyze the performance of the policy and provide the closed-form maximum covert rate and maximum detection error probability at the warden, revealing their tradeoff. The numerical analysis shows the increasing transmission power of the RF source and tag’s reflection coefficient would degrade the covertness of ABS when the covert receiver has to increase the AN power.

Mass data traffics, low-latency wireless services and advanced artificial intelligence (AI) technologies have driven the emergence of a new paradigm for wireless networks, namely edge-intelligent networks, which are more efficient and flexible than traditional cloud-intelligent networks. Considering users’ privacy, model sharing-based federated learning (FL) that migrates model parameters but not private data from edge devices to a central cloud is particularly attractive for edge-intelligent networks. Due to multiple rounds of iterative updating of high-dimensional model parameters between base station (BS) and edge devices, the communication reliability is a critical issue of FL for edge-intelligent networks. We reveal the impacts of the errors generated during model broadcast and model aggregation via wireless channels caused by channel fading, interference and noise on the accuracy of FL, especially when there exists channel uncertainty. To alleviate the impacts, we propose a robust FL algorithm for edge-intelligent networks with channel uncertainty, which is formulated as a worst-case optimization problem with joint device selection and transceiver design. Finally, simulation results validate the robustness and effectiveness of the proposed algorithm.

The roll-out of various emerging wireless services has triggered the need for the sixth-generation (6G) wireless networks to provide functions of target sensing, intelligent computing and information communication over the same radio spectrum. In this paper, we provide a unified framework integrating sensing, computing, and communication to optimize limited system resource for 6G wireless networks. In particular, two typical joint beamforming design algorithms are derived based on multi-objective optimization problems (MOOP) with the goals of the weighted overall performance maximization and the total transmit power minimization, respectively. Extensive simulation results validate the effectiveness of the proposed algorithms. Moreover, the impacts of key system parameters are revealed to provide useful insights for the design of integrated sensing, computing, and communication (ISCC).

The challenge in massive Machine Type Communication (mMTC) is to support reliable and instant access for an enormous number of machine-type devices (MTDs). In some particular applications of mMTC, the access point (AP) only has to know the messages received, but not where they source from, thus giving rise to the concept of unsourced random access (URA). In this paper, we propose a novel URA scheme exploiting the elegant properties of Reed-Muller (RM) sequences. Specifically, after dividing the message of an active user into several information chunks, RM sequences are used to carry those chunks, for exploiting the vast sequence space to improve the spectral efficiency, and their nested structure to enable reliable and efficient sequence detection. Next, we further explore a novel structural property of RM sequences for designing sparse patterns which carry part of the information and serve as the hints of coupling the information chunks of a single user. The factors affecting the performance of our slot-based RM detection are characterized. Besides, the complexity of the proposed message stitching method is analyzed and compared to the commonly used tree coding approach. Our simulation results verify the enhanced performance of the proposed URA scheme in error probability and computational complexity compared to the existing counterpart.

This paper investigates a reconfigurable intelligent surface (RIS)-aided unsourced random access (URA) scheme for the sixth-generation (6G) wireless networks with massive sporadic traffic devices. First of all, this paper proposes a novel joint active device separation (the message recovery of active device) and channel estimation architecture for the RIS-aided URA. Specifically, the RIS passive reflection is optimized before the successful device separation. Then, by associating the data sequences to multiple rank-one tensors and exploiting the angular sparsity of the RIS-BS channel, the detection problem is cast as a high-order coupled tensor decomposition problem without the need of exploiting pilot sequences. However, the inherent coupling among multiple sparse device-RIS channels, together with the unknown number of active devices make the detection problem at hand deviate from the widely-used coupled tensor decomposition format. To overcome this challenge, this paper judiciously devises a probabilistic model that captures both the element-wise sparsity from the angular channel model and the low-rank property due to the sporadic nature of URA. Then, based on such a probabilistic model, a iterative detection algorithm is developed under the framework of sparse variational inference, where each update iteration is obtained in a closed-form and the number of active devices can be automatically estimated for effectively avoiding the overfitting of noise. Extensive simulation results confirm the excellence of the proposed URA algorithm, especially for the case of a large number of reflecting elements for accommodating a significantly large number of devices.

Millimeter-wave (mmW)/Terahertz (THz) wideband communication employing a large-scale antenna array is a promising technique of the sixth-generation (6G) wireless network for realizing massive machine-type communications (mMTC). To reduce the access latency and the signaling overhead, we design a grant-free random access scheme based on joint active device detection and channel estimation (JADCE) for mmW/THz wideband massive access. In particular, by exploiting the simultaneously sparse and low-rank structure of mmW/THz channels with spreads in the delay-angular domain, we propose two multi-rank aware JADCE algorithms via applying the quotient geometry of product of complex rank-$L$matrices with the number of clusters$L$. It is proved that the proposed algorithms require a smaller number of measurements than the currently known bounds on measurements of conventional simultaneously sparse and low-rank recovery algorithms. Statistical analysis also shows that the proposed algorithms can linearly converge to the ground truth with low computational complexity. Finally, extensive simulation results confirm the superiority of the proposed algorithms in terms of the accuracy of both activity detection and channel estimation.

This paper investigates the problem of joint massive devices separation and channel estimation for a reconfigurable intelligent surface (RIS)-aided unsourced random access (URA) scheme in the sixth-generation (6G) wireless networks. In particular, by associating the data sequences to a rank-one tensor and exploiting the angular sparsity of the channel, the detection problem is cast as a high-order coupled tensor decomposition problem. However, the coupling among multiple devices to RIS (device-RIS) channels together with their sparse structure make the problem intractable. By devising novel priors to incorporate problem structures, we design a novel probabilistic model to capture both the element-wise sparsity from the angular channel model and the low rank property due to the sporadic nature of URA. Based on the this probabilistic model, we develop a coupled tensor-based automatic detection (CTAD) algorithm under the framework of variational inference with fast convergence and low computational complexity. Moreover, the proposed algorithm can automatically learn the number of active devices and thus effectively avoid noise overfitting. Extensive simulation results confirm the effectiveness and improvements of the proposed URA algorithm in large-scale RIS regime.

Mobile channel estimation is very challenging as usually it requires more pilots and channel observations to obtain the channel state information (CSI) and the resultant estimation accuracy may decrease with the number of antennas and sub-carriers. Through exploring the long-and-short-term intrinsic spatial and temporal correlation among a set of historic channel instances randomly obtained within a certain communication environment, channel prediction can help increase the CSI accuracy w.r.t. to that obtained from only the pilots, and thus save signaling overhead and computational cost. In this paper, we propose a novel generative Periodic-Activator-enabled Auto Encoder-LSTM network (GPAE-LSTMnet) for accurate channel prediction of mobile MIMO channels, which first compresses the high dimensional channel matrix with high-frequency features to a low dimensional space with relatively low-frequency feature space that has high data smoothness and is suitable for time-series sequence prediction. After that, a LSTM network is used to predict the channel in the low dimensional space, which ensures high accuracy and low computational cost. Experimental results show that our proposed learning structure outperforms existing methods especially when the dimension of CSI to be predicted is relatively high, the time interval of the CSI sequence is relatively long and the number of network parameters is highly limited.

Millimeter-wave (mmWave) imaging has attracted much attention due to its potential applications in next generation wireless networks. However, how to design robust and efficient signaling and reconstruction algorithm still remains very challenging. In this paper, with the aid of the newly emerged intelligent reflecting surfaces (IRS), we propose a novel millimeter-wave computational imaging method exploiting the enormous illuminating patterns provided by Reed-Muller (RM) sequences. In particular, we construct a deterministic sensing matrix using the RM sequences with which to stimulate the objects via an intelligent reflecting surface, so as to modulate the amplitude and phase of the incident wave indirectly and increase the electromagnetic degrees of freedom. Compared with Zadoff-Chu sequence, Hadamard sequence and random sequence modulated signal illumination method, our proposed approach converges faster and achieves nearly the same accuracy as the illuminations increase, and has less hardware overhead.

In this paper, we try to solve the problem of wireless channel prediction in a fixed area based only on position information of user's equipment. It is the first time that such a problem is proposed and discussed. Different from recent channel prediction methods which need a sequence of measured channel state information (CSI) as known factor, we view this task as a generative problem. A large amount of CSI data measured in the historical communication process can be made use of directly. For solving this problem in a data-driven way, a novel physics-inspired learning structure (C-GRBF) is proposed which fits the physics process of channel impulse response formulating perfectly. Scattering environment information is learned as parameters of the network and the principle of electromagnetic wave propagation is implicit represented by the structure of the network. In the meantime, the reason why conventional universal learning structures fail in solving this problem is analyzed. Experimental results show great performance in prediction accuracy, convergence speed and network robustness of the proposed learning structure.

Recently, millimeter wave (mmWave) imaging has received widespread attention. However, due to its nonlinearity and ill-posedness, it is challenging to reconstruct the precise electromagnetic properties of unknown targets from the measured scattered fields. In this paper, a new concentrative intelligent reflecting surface (IRS) aided computational imaging scheme is proposed. In the scheme, by dividing the region of imaging (ROI) into pixels, the imaging process is transformed into a compressed sensing problem. This paper proposes a fast block sparse Bayesian learning (BSBL) algorithm, which exploits the block sparsity of the reflection vector of ROI, and reduces the computational complexity through the generalized approximate message passing (GAMP) algorithm. Finally, the simulation results validate the performance advantages of the proposed algorithm and the efficiency of IRS in the imaging process.

In this article, we investigate an integrated satellite-terrestrial (IST) Internet-of-Things (IoT) network, where satellite networks provide wireless services to the IoT devices uncovered by terrestrial cellular networks, so as to realize seamless coverage across the world. In order to support massive access of a large number of IoT devices, the low Earth orbit (LEO) satellites and the terrestrial base station (BS) both adopt power-domain nonorthogonal multiple access (NOMA) techniques designed according to channel state information (CSI). Due to the limited capacity of feedback links, there exists channel uncertainty at the LEO satellites and BSs, resulting in internetwork interference inevitably. In order to guarantee the performance of the IST-IoT network, we formulate a optimization problem to minimize the total transmit power of the IST-IoT network subject to outage probability constraints on the rates for the IoT devices associated to both satellite and terrestrial networks. Since the original problem is mathematically intractable, we utilize a series of transformations to obtain an approximate equivalent convex problem. Then, we propose an iterative penalty function (IPF)-based algorithm to jointly design robust beamforming for the satellite and BSs. Finally, we provide extensive simulation results to reveal the impacts of important system parameters and confirm the effectiveness of the proposed robust design algorithm for IST-IoT networks.

In this article, we investigate the issue of massive access in a beyond fifth-generation (B5G) multibeam low-Earth orbit (LEO) satellite Internet-of-Things (IoT) network in the presence of channel phase uncertainty due to channel-state information (CSI) conveyance from the devices to the satellite via the gateway. Rather than time-division multiple access (TDMA) or frequency-division multiple access (FDMA) with multicolor pattern, a new nonorthogonal multiple access (NOMA) scheme is adopted to support massive IoT distributed over a very wide range. Considering the limited energy on the LEO satellite, two robust beamforming algorithms against channel phase uncertainty are proposed for minimizing the total power consumption in the scenarios of noncritical IoT applications and critical IoT applications, respectively. Both theoretical analysis and simulation results validate the effectiveness and robustness of the proposed algorithms for supporting massive access in satellite IoT.

Massive access, also known as massive connectivity or massive machine-type communication (mMTC), is one of the main use cases of the fifth-generation (5G) and beyond 5G (B5G) wireless networks. A typical application of massive access is the cellular Internet of Things (IoT). Different from conventional human-type communication, massive access aims at realizing efficient and reliable communications for a massive number of IoT devices. Hence, the main characteristics of massive access include low power, massive connectivity, and broad coverage, which require new concepts, theories, and paradigms for the design of next-generation cellular networks. This paper presents a comprehensive survey of massive access design for B5G wireless networks. Specifically, we provide a detailed review of massive access from the perspectives of theory, protocols, techniques, coverage, energy, and security. Furthermore, several future research directions and challenges are identified.

With the increasing development of Internet of Things (IoT), the upcoming sixth-generation (6G) wireless network is required to support grant-free random access of a massive number of sporadic traffic devices. In particular, at the beginning of each time slot, the base station (BS) performs joint activity detection and channel estimation (JADCE) based on the received pilot sequences sent from active devices. Due to the deployment of a large-scale antenna array and the existence of a massive number of IoT devices, conventional JADCE approaches usually have high computational complexity and need long pilot sequences. To solve these challenges, this paper proposes a novel deep learning framework for JADCE in 6G wireless networks, which contains a dimension reduction module, a deep learning network module, an active device detection module, and a channel estimation module. Then, prior-feature learning followed by an adaptive-tuning strategy is proposed, where an inner network composed of the Expectation-maximization (EM) and back-propagation is introduced to jointly tune the precision and learn the distribution parameters of the device state matrix. Finally, by designing the inner layer-by-layer and outer layer-by-layer training method, a feature-aided adaptive-tuning deep learning network is built. Both theoretical analysis and simulation results confirm that the proposed deep learning framework has low computational complexity and needs short pilot sequences in practical scenarios.

In this article, we propose a novel distributed alternating direction method of multipliers (ADMM) algorithm with synergetic communication and computation, called SCCD-ADMM, to reduce the total communication and computation cost of the system. Explicitly, in the proposed algorithm, each node interacts with only part of its neighboring nodes, the number of which is progressively determined according to a heuristic searching procedure, which takes into account both the predicted convergence rate and the communication and computation costs at each iteration, resulting in a trade-off between communication and computation. Then the node chooses its neighboring nodes according to an importance sampling distribution derived theoretically to minimize the variance with the latest information it locally stores. Finally, the node updates its local information with a new update rule which adapts to the number of communication nodes. We prove the convergence of the proposed algorithm and provide an upper bound of the convergence variance brought by randomness. Extensive simulations validate the excellent performances of the proposed algorithm in terms of convergence rate and variance, the overall communication and computation cost, the impact of network topology as well as the time for evaluation, in comparison with the traditional counterparts.

In this article, we investigate the issue of integrated sensing, computation and communication (SCC) in beyond fifth-generation (B5G) cellular internet of things (IoT) networks. According to the characteristics of B5G cellular IoT, a comprehensive design framework integrating SCC is put forward for massive IoT. For sensing, highly accurate sensed information at IoT devices are sent to the base station (BS) by using non-orthogonal communication over wireless multiple access channels. Meanwhile, for computation, a novel technique, namely over-the-air computation (AirComp), is adopted to substantially reduce the latency of massive data aggregation via exploiting the superposition property of wireless multiple access channels. To coordinate the co-channel interference for enhancing the overall performance of B5G cellular IoT integrating SCC, two joint beamforming design algorithms are proposed from the perspectives of the computation error minimization and the weighted sum-rate maximization, respectively. Finally, extensive simulation results validate the effectiveness of the proposed algorithms for B5G cellular IoT over the baseline ones.

This paper proposes an angle domain framework for the design of an intelligent reflecting surface (IRS) system. The maximum likelihood (ML) principle is applied to derive the estimators for the effective angles among the base station (BS), IRS and user. It is demonstrated that the accuracy of the estimated angles improves with the number of BS antennas. Also, deploying the IRS closer to the BS increases the accuracy of the estimated angle from the IRS to the user. Then, exploiting the estimated angles, we propose a joint design of BS beamforming and IRS beamforming. Simulation results show that our proposed algorithm, which only needs few angle information, achieves nearly the same performance as the algorithm requiring full channel state information (CSI). Moreover, the optimized BS beam becomes more focused towards the IRS direction as the number of reflecting elements increases.

This paper proposes a novel location information aided design framework for multiple intelligent reflecting surface (IRS) systems. Assuming practical and imperfect user location information, the effective angles from the IRS to the users are estimated, which is then used to design the transmit beam and IRS beam. Furthermore, closed-form expressions for the achievable rate are derived. The analytical findings indicate that the achievable rate can be improved by increasing the number of base station (BS) antennas or reflecting elements. Specifically, a power gain of order NM2is achieved, where N is the number of BS antennas and M is the number of reflecting elements. Moreover, with a large number of reflecting elements, the individual signal to interference plus noise ratio (SINR) is proportional to M. Also, it has been shown that high location uncertainty would significantly degrade the achievable rate. Besides, IRSs should be deployed at distinct directions (relative to the BS) and be far away from each other to reduce the interference from multiple IRSs.

This paper designs a cooperative activity detection framework for massive grant-free random access in the sixth-generation (6G) cell-free wireless networks based on the covariance of the received signals at the access points (APs). In particular, multiple APs cooperatively detect the device activity by only exchanging the low-dimensional intermediate local information with their neighbors. The cooperative activity detection problem is non-smooth and the unknown variables are coupled with each other for which conventional approaches are inapplicable. Therefore, this paper proposes a covariance-based algorithm by exploiting the sparsity-promoting and similarity-promoting terms of the device state vectors among neighboring APs. An approximate splitting approach is proposed based on the proximal gradient method for solving the formulated problem. Simulation results show that the proposed algorithm is efficient for large-scale activity detection problems while requires shorter pilot sequences compared with the state-of-art algorithms in achieving the same system performance.

Millimeter-wave/Terahertz (mmW/THz) communications have shown great potential for wideband massive access in next-generation cellular internet of things (IoT) networks. To decrease the length of pilot sequences and the computational complexity in wideband massive access, this paper proposes a novel joint activity detection and channel estimation (JADCE) algorithm. Specifically, after formulating JADCE as a problem of recovering a simultaneously sparse-group and low rank matrix according to the characteristics of mmW/THz channel, we prove that jointly imposing l0norm and low rank on such a matrix can achieve a robust recovery under sufficient conditions, and verify that the number of measurements derived for the mmW/THz wideband massive access system is significantly smaller than currently known measurements bound derived for the conventional simultaneously sparse and low-rank recovery. Furthermore, we propose a multi-rank aware method by exploiting the quotient geometry of product of complex rank-Lmaxmatrices with the maximum number of scattering clusters Lmax. Theoretical analysis and simulation results confirm the superiority of the proposed algorithm in terms of computational complexity, detection error rate, and channel estimation accuracy.

In the mMTC scenario, enormous devices turn active sporadically or frequently to seek for opportunities to transmit short packets. In this highly dynamic situation, it is critical to design efficient random access (RA) procedures to cope both with the flood of simultaneous access requests and with the potential access failures. In this paper, we propose an incremental RA scheme exploiting the nested Reed-Muller (RM) sequences. Specifically, the users who suffer from access failures expand their RM sequences following the given expansion rule, which utilizes both the nested structure and the cross-correlation property of RM sequences. At the receiver, a recursive detection algorithm is proposed, which exploits the discrepancy in the sequence length to detect the retransmission users progressively. On the other hand, new active users continuously spring up in the system, thus causing the incremental number of users seeking for access. In this case, the proposed scheme can detect newly active users together with retransmission ones with great detection capability and low access latency. Our simulation results verify the superior performance of the proposed RA scheme.

In this paper, we design a multi-beam low earth orbit (LEO) satellite internet of things (IoT) for beyond fifth-generation (B5G) wireless networks. Rather than time division multiple access (TDMA), a new non-orthogonal multiple access (NOMA) scheme is adopted to support massive IoT over a very wide range. In order to reduce the power consumption of multi-beam satellite, a spot beam design algorithm is proposed with the goal of minimizing the total power consumption subject to quality-of-service (QoS) requirements. Furthermore, considering high computational complexity of spot beam design in the context of massive IoT, a simple multi-beam design algorithm is provided. Finally, simulation results confirm the effectiveness of the proposed algorithms over conventional ones.

The beyond fifth-generation (B5G) cellular internet of things (IoT) network is required to support low-power and wide-coverage wireless access for a massive number of devices. However, the severe co-channel interference caused by massive access decreases the reliability and coverage. To solve this problem, we design a large intelligent reflecting surface (IRS) aided massive access framework, including channel estimation and information transmission. Furthermore, we analyze the performance of the proposed framework, and reveal the role of the large IRS. It is found that the large IRS is beneficial to improve the performance of edge-devices, and thus enhance the coverage. Moreover, we find that the refection coefficient should be carefully chosen according to channel conditions and system parameters to improve the performance.

This paper considers a multi-antenna multicast system with programmable metasurface (PMS) based transmitter. Taking into account of the finite-resolution phase shifts of PMSs, a novel beam training approach is proposed, which achieves comparable performance as the exhaustive beam searching method but with much lower time overhead. Then, a closed-form expression for the achievable individual rate is presented, which is valid for arbitrary system configurations. Besides, assuming a large number of reflecting elements, a simple approximated expression for the multicast rate is derived. A closed-form solution is obtained for the optimal power allocation scheme, and it is shown that equal power allocation is optimal when the number of reflecting elements is sufficiently large. The analytical findings indicate that, increasing the number of radio frequency (RF) chains or reflecting elements can significantly improve the multicast rate, and as the phase shift number becomes larger, the multicast rate improves first and gradually converges to a limit. Moreover, increasing the number of users would significantly degrade the multicast rate, but this rate loss can be compensated by implementing a large number of reflecting elements.

In this paper, we investigate the issue of integrated computation and communication in beyond fifth-generation (B5G) cellular internet of things (IoT) networks with massive connectivity. By exploiting the open nature of wireless channels, a comprehensive deign framework integrating computation and communication over the same spectrum is first put forward for massive IoT. To achieve efficient integration of computation and communication under practical but adverse conditions, a robust algorithm is proposed by jointly optimizing transmit power and receive beamforming, with the goal of minimizing the computation error of computation signals while guaranteeing the requirement of communication signals. Finally, extensive simulations validate the robustness and effectiveness of the proposed algorithm for B5G cellular IoT.

In this paper, we investigate an energy-efficient grant-free random access protocol for beyond fifth-generation (B5G) cellular internet of things (IoT) with sporadic traffic. A design framework, including device activity information (DAI) and channel state information (CSI) acquisition and uplink data transmission, is first provided to support massive access over limited radio spectrum. Then, considering the low-power requirement of IoT devices, we propose a robust massive access scheme by jointly optimizing pilot transmit power, data transmit power and receive vector at the base station (BS) for maximizing the energy efficiency in the presence of channel uncertainty. Finally, extensive simulation results validate the effectiveness and robustness of the proposed scheme.

In this article, we investigate the issue of massive access in a beyond fifth-generation (B5G) cellular Internet of Things (IoT) network. To reduce the overhead of channel state information acquisition and the complexity of transceiver design, an integrated framework of massive beam-division multiple access (BDMA) is proposed according to the characteristics of beamspace propagation. Then, we analyze the performance of the proposed massive BDMA scheme and derive a closed-form expression for the weighted sum rate in terms of channel conditions and system parameters. To improve the overall performance, we propose a massive access algorithm by jointly optimizing transmit power and receive vector. It is found that the optimal receive vector for each IoT device is the base beam corresponding to the arrival of angle of the IoT device's signal in the beamspace, which significantly simplifies the design of the receiver. Considering the constraint of radio-frequency (RF) chains in practical networks, we propose a clustering-based massive access algorithm, which allocates an RF chain for a cluster. Finally, extensive simulation results confirm that the proposed algorithms can provide small overhead and low complexity massive access schemes for B5G cellular IoT.

This article addresses the issue of symbol detection in ambient backscatter communication systems with the multiantenna reader. Focusing on the ON-OFF keying modulation, the optimal detector minimizing the bit error rate (BER) is devised based on the maximum a posteriori principle. We then analyze the exact closed-form BER expression for the optimal detector. Moreover, simple approximate BER expressions are derived in certain asymptotic regimes. Furthermore, a simple energy detector is analyzed as a benchmark scheme, and the asymptotic BER is devised in a closed form. The findings of this article suggest that implementing multiple antennas at the reader is an effective means to enhance the BER performance and extend the tag-reader communication range. Also, the optimal detector always outperforms the energy detector. In particular, the optimal detector can avoid the error floor phenomenon, which is inevitable for the energy detector in the high SNR regime.

In this article, we apply the large intelligent reflecting surface (IRS) technique in beyond fifth-generation (B5G) cellular Internet of Things (IoT) to satisfy the requirements of massive connectivity, low power, and wide coverage. First, we design a framework for the large IRS-aided B5G cellular IoT, including channel estimation, uplink data transmission, and downlink data transmission. Then, we analyze the performance of the proposed framework, and reveal the impacts of key parameters of the large IRS on the spectral efficiency. Next, we propose a low-complexity time-length allocation algorithm to minimize the total energy consumption of B5G cellular IoT. Finally, extensive simulation results validate the accuracy of the derived theoretical expressions and the effectiveness of the proposed algorithm.

This paper considers a multi-antenna multicast system with programmable metasurface (PMS) based transmitter. Taking into account of the finite-resolution phase shifts of PMSs, a novel beam training approach is proposed, which achieves comparable performance as the exhaustive beam searching method but with much lower time overhead. Then, a closed-form expression for the achievable multicast rate is presented, which is valid for arbitrary system configurations. In addition, for certain asymptotic scenario, simple approximated expressions for the multicase rate are derived. Closed-form solutions are obtained for the optimal power allocation scheme, and it is shown that equal power allocation is optimal when the pilot power or the number of reflecting elements is sufficiently large. However, it is desirable to allocate more power to weaker users when there are a large number of RF chains. The analytical findings indicate that, with large pilot power, the multicast rate is determined by the weakest user. Also, increasing the number of radio frequency (RF) chains or reflecting elements can significantly improve the multicast rate, and as the phase shift number becomes larger, the multicast rate improves first and gradually converges to a limit. Moreover, increasing the number of users would significantly degrade the multicast rate, but this rate loss can be compensated by implementing a large number of reflecting elements.

This article proposes a novel location information aided multiple intelligent reflecting surface (IRS) systems. Assuming imperfect user location information, the effective angles from the IRS to the users are estimated, which is then used to design the transmit beam and IRS beam. Furthermore, closed-form expressions for the achievable rate are derived. The analytical findings indicate that the achievable rate can be improved by increasing the number of base station (BS) antennas or reflecting elements. Specifically, a power gain of order N M2is achieved, where N is the antenna number and M is the number of reflecting elements. Moreover, with a large number of reflecting elements, the individual signal to interference plus noise ratio (SINR) is proportional to M, while becomes proportional to M2as non-line-of-sight (NLOS) paths vanish. Also, it has been shown that high location uncertainty would significantly degrade the achievable rate. Besides, IRSs should be deployed at distinct directions (relative to the BS) and be far away from each other to reduce the interference from multiple IRSs. Finally, an optimal power allocation scheme has been proposed to improve the system performance.

In beyond fifth-generation (B5G) cellular internet of things (IoT) networks, energy supply and data aggregation of a massive number of devices are two vitally challenging issues. To address these challenges, we propose a wireless powered MIMO over-the-air computation (AirComp) design framework. Firstly, wireless power transfer (WPT) is utilized to charge massive IoT devices simultaneously by exploiting the open nature of wireless broadcast channel. Then, AirComp is adopted to reduce latency of massive data aggregation via exploring the superposition property of wireless multiple-access channel. To realize efficient convergence of energy supply and data aggregation in practical IoT networks, a robust design algorithm is provided by jointly optimizing beamforming of both WPT and AirComp. Finally, extensive simulation results validate the robustness and effectiveness of the proposed algorithm over the baseline ones.

Active device detection is a precondition of realizing grant-free random access in beyond fifth-generation (B5G) cellular Internet-of-Things (IoT). However, due to the deployment of a large antennas array and the existence of a huge number of IoT devices, activity detection usually has high computational complexity and needs long pilot sequences. To overcome these challenges, we first propose a dimension deduction method by projecting the original device state matrix to a much lower dimension space. Then, we develop an optimized design framework with a logarithmic smoothing objective function and a coupled full column rank constraint. Under that framework, we transform the original interested matrix to a positive semidefinite matrix, followed by proposing a Riemannian trust-region algorithm to solve the problem in complex field. Simulation results show that the proposed algorithm outperforms the state-of-art algorithms in terms of device detection performance.

This paper investigates the performance of angle-domain millimeter-wave (mmWave) multi-input multi-output (MIMO) non-orthogonal multiple access (NOMA) systems in the presence of angular estimation error. A closed-form expression for the achievable rate of the system is derived. Based on which, a simple asymptotic approximation is obtained. The findings of paper suggest that, with a large number of BS antennas, the user rate is mainly constrained by the antenna number to beam number ratio (ANTBNR) and the spatial direction distance. In particular, increasing the ANTBNR would cause a severe rate loss, and the achievable rate is an increasing function with respect to the spatial direction distance. Capitalizing on this key observation, a novel cluster grouping scheme is designed to reduce the inter-cluster interference, which shows significant performance gain over a random cluster grouping scheme. Finally, simulation results are provided to corroborate the analytical results.

In order to support massive connections over limited radio spectrum for the cellular Internet-of-Things (IoT) in the fifth-generation (5G) wireless network, we propose a new non-orthogonal beamspace multiple access framework. First, we analyze the performance of the proposed non-orthogonal beamspace multiple access scheme, and derive an upper bound on the weighted sum rate in terms of channel conditions and system parameters. Then, we provide a transmit beam construction algorithm for further improving the overall performance. Finally, extensive simulation results show that substantial performance gain can be obtained by the proposed non-orthogonal beamspace multiple access scheme over the baseline ones.

With the increasing development of cellular internet of things (IoT), the upcoming fifth generation (5G) wireless network is required to support massive IoT with sporadic traffic. In order to realize massive access over limited radio spectrum, a three-phase transmission protocol which consists of device detection and channel estimation, uplink data transmission and downlink data transmission is designed for the cellular IoT. In particular, we analyze the performance of the proposed transmission protocol, and reveal the impact of system parameters on the sum rate. Finally, simulation results validate the effectiveness of the theoretical claims.

Ambient backscatter communication is a newly emerged ultra-low-power technology for the internet of things network. In this paper, we study the symbol detection of multi-antenna ambient backscatter communication system. In particular, the maximum-eigenvalue detector is derived from general likelihood ratio test, and the approximative BER expressions are characterized. The analytical results show that the BER of the proposed detector decreases with sampling rate N, signal-to-noise ratio γ and number of receiving antenna M, however, settles in high γ or M regime. In addition, we find the proposed detector eliminates the knowledge of noise power, which is of uncertainty and difficult to estimate. Then the simulation results validate the correctness of the theoretical analysis, and show that the proposed detector outperforms the existing energy detector in terms of BER performance.

In this paper, we investigate the issue of simultaneous wireless information and power transfer in the cellular Internet of Things (IoT) with a massive number of different access devices, e.g., information decoding devices, energy harvesting (EH) devices, and hybrid devices. Especially, we consider a practical scenario of the cellular IoT, where the IoT devices have a nonlinear EH receiver and perform imperfect successive interference cancellation (SIC) due to a limited capability. The benefits offered by a multiple-antenna base station are exploited to enhance the efficiency of both information transmission and power transfer. In particular, we propose to jointly optimize the spatial beam, transmit power, and power splitting ratio to alleviate the impacts of both nonlinear EH and imperfect SIC. To this end, two effective algorithms are designed from the perspectives of maximizing the weighted sum rate and minimizing the total power consumption, respectively. Finally, extensive simulation results are presented to validate the effectiveness of the proposed algorithms.

With the increasing development of the cellular Internet of Things (IoT), the upcoming fifth-generation wireless network is required to support massive access of sporadic traffic devices. In this context, we design a three-phase transmission protocol which consists of device detection and channel estimation, uplink data transmission, and downlink data transmission for the cellular IoT, so as to realize massive access over limited radio spectrum. We analyze the performance of the proposed transmission protocol and derive closed-form expressions for the uplink and downlink achievable rates in terms of channel conditions and system parameters. Moreover, to improve the overall performance, we propose a length allocation algorithm by coordinating the three-phase transmission protocol in the unified sense. Extensive simulation results show that substantial performance gain can be obtained by the proposed algorithm.

As a promising physical layer technique, nonorthogonal multiple access (NOMA) can admit multiple users over the same space-time resource block, and thus improve the spectral efficiency and increase the number of access users. Specifically, NOMA provides a feasible solution to massive Internet of Things (IoT) in 5G and beyond-5G wireless networks over a limited radio spectrum. However, severe co-channel interference and high implementation complexity hinder its application in practical systems. To solve these problems, multiple-antenna techniques have been widely used in NOMA systems by exploiting the benefits of spatial degrees of freedom. This study provides a comprehensive review of various multiple-antenna techniques in NOMA systems, with an emphasis on spatial interference cancellation and complexity reduction. In particular, we provide a detailed investigation on multiple-antenna techniques in two-user, multiuser, massive connectivity, and heterogeneous NOMA systems. Finally, future research directions and challenges are identified.

In this paper, we address a practical but adverse problem that successive interference cancellation (SIC) is imperfect in a massive non-orthogonal multiple access (NOMA) system. The benefits of a multiple-antenna base station are exploited to support massive access through user clustering in the spatial domain and alleviate the impact of imperfect SIC. In particular, transmit beams and powers are jointly optimized to mitigate the intra-cluster and inter-cluster interference, so as to improve the overall performance in the presence of imperfect SIC. Specifically, we design the joint optimization algorithms from the perspectives of maximizing the weighted sum rate and minimizing the total power consumption, respectively. Moreover, in order to reduce the computational complexity, we design the massive NOMA algorithms with zero-forcing beamforming fixedly. The impacts of imperfect SIC on the design of massive NOMA algorithms are revealed, and it is found that the proposed algorithms are still applicable even if SIC is perfect. Finally, simulations results validate the theoretical claims and show that obvious performance gain can be obtained over the baseline algorithms.

This paper investigates the achievable performance of cell-free massive multiple-input multiple-output (MIMO) systems with low resolution analog-to-digital converters (ADCs) at both the access points (APs) and users. A closed-form expression for the achievable rate is derived, which enables the study of the effects of AP number, antenna number per AP, user number, and ADC resolution on the achievable rate. In addition, a simple asymptotic approximation for the individual user rate is presented, which shows that the user rate is mainly constrained by the ADC resolution at the user. Moreover, we propose an ADC resolution bits allocation scheme aiming at maximizing the sum rate subject to the total ADC resolution bits constraint, which substantially outperforms the equal ADC resolution bits allocation scheme. Furthermore, a max-min power control scheme is proposed, which not only ensures user fairness, but also improves the achievable rate. Finally, simulation results are provided to corroborate the analytical results.

In this paper, we investigate the issue of low-cost design of massive access for cellular internet of things (IoT) over spatially correlated Rician fading channels. Specifically, by exploiting a low-overhead transmission protocol, a base station (BS) equipped with a large-scale antenna array and low-resolution analog-to-digital converters (ADCs) is deployed to serve a massive number of IoT devices with low-complexity successive interference cancellation (SIC) receivers. We first analyze the impacts of the low-cost design on the system performance and derive closed-form expressions for uplink and downlink spectral efficiencies of the cellular IoT. Then, for alleviating the negative impacts of the low-cost design, we propose an algorithm allocating the time for channel estimation, uplink data transmission, and downlink data transmission in a data frame. Finally, extensive simulation results confirm the effectiveness of the proposed low-cost design for the cellular IoT.

In existing communication systems, the channel state information of each user equipment (UE) should be repeatedly estimated when it moves to a new position or when another UE takes its place. The underlying ambient information, including the specific layout of potential reflectors, which provides more detailed information about all UEs' channel structures, has not been fully explored and exploited. In this paper, we rethink the mm-wave channel estimation problem in a new and indirect way, i.e., instead of estimating the resultant composite channel response at each time, and for any specific location, we first conduct the ambient perception exploiting the fascinating radar capability of a mm-wave antenna array and then accomplish the location-based sparse channel reconstruction. In this way, the sparse channel for a quasi-static UE arriving at a specific location can be rapidly synthesized based on the perceived ambient information, thus greatly reducing the signaling overhead and online computational complexity. Based on the reconstructed mm-wave channel, single-beam mm-wave communication is designed and evaluated which shows an excellent performance. Such an approach, in fact, integrates the radar with communication, which may possibly open a new paradigm for future communication system design.

In this paper, we investigate the issue of sustainable communications for beyond fifth-generation (B5G) cellular internet of things (IoT) networks under adverse but practical conditions. A massive number of simple IoT devices without batteries harvest requisite energy from a part of the received signal. A design framework including channel state information (CSI) acquisition, signal construction, information decoding and energy harvesting, is first provided for sustainable communications of massive IoT. Then, based on the proposed design framework, we reveal the impacts of practically adverse factors, e.g., channel uncertainty, successive interference cancellation (SIC) and non-linear energy harvesting, on the performance of B5G cellular IoT. Furthermore, in order to effectively alleviate the impacts of these adverse factors, an outage-constrained robust algorithm is designed to maximize the overall performance of sustainable B5G cellular IoT. Finally, extensive simulation results validate the robustness and effectiveness of the proposed algorithm over the baseline ones.

The ambient backscatter communication is a newly rising paradigm for the Internet-of-Things networks, which enables the connection of low-cost devices. This paper proposes a novel MFSK modulation for the Tag of ambient backscatter communications systems, and the corresponding detectors are designed depending on the capability of the Reader. In the case the Reader is not capable of removing the direct interference from the ambient source, a maximum likelihood detector is proposed. In another case, leveraging on the frequency shift feature of the MFSK modulation, the Reader can remove the direct interference. Then, a simple energy detector is proposed, and the closed-form expressions for the symbol error rate (SER) and outage probability of the system are derived. The findings of this paper suggest that the proposed MFSK modulation outperforms the popular ON-OFF keying modulation, and the impact of modulation order on the SER performance depends heavily on the operating bit signal to noise ratio. Moreover, it is shown that it is desirable to place the Tag close to the Reader in terms of minimizing the outage probability.

This paper deals with symbol detection in ambient backscatter communication systems with multi-antenna reader. Unlike most of the existing works which assume deterministic ambient radio frequency (RF) signals, we consider another important scenario with complex Gaussian RF signals. Focusing on the on-off keying modulation, the optimal detector minimizing the bit error rate (BER) is devised based on the maximum a posteriori principle, and an exact closed-form expression for the BER is derived. In addition, a simple energy detector is proposed to serve as a performance benchmark. Simulation results show that, implementing multiple antennas at the reader is an effective means to enhance the BER performance. Also, the proposed optimal detector always outperforms the energy detector. Furthermore, unlike the energy detector, whose BER settles in the high signal to noise ratio regime, no error floor exists for the proposed optimal detector.

We study the effects of low resolution analog-to-digital converters (ADCs) on cell-free massive multiple-input multiple-output (MIMO) systems. We first derive a closed-form expression for the achievable rate, which enables efficient evaluation of the impact of key parameters on system performance. Then, we give a simple approximate closed-form expression for the individual user rate, which indicates that the ADC resolution at the user is the main constraining factor for the user rate. Furthermore, we study the allocation of ADC resolution bits among different access points (APs) with fixed total resolution bits. It has been shown that our proposed ADC resolution bits allocation scheme is substantially superior to the equal ADC resolution bits allocation scheme. Finally, we provide simulation results to verify our analytical results.

This paper considers the security issue of the fifth-generation wireless networks with massive connections, where multiple eavesdroppers aim to intercept the confidential messages through active eavesdropping. To realize secure massive access, non-orthogonal channel estimation and non-orthogonal multiple access techniques are combined to enhance the signal quality at legitimate users, while the inter-user interference is harnessed to deliberately confuse the eavesdroppers even without exploiting artificial noise. We first analyze the secrecy performance of the considered secure massive access system and derive a closed-form expression for the ergodic secrecy rate. In particular, we reveal the impact of some key system parameters on the ergodic secrecy rate via asymptotic analysis with respect to a large number of antennas and a high transmit power at the base station. Then, to fully exploit the inter-user interference for security enhancement, we propose to optimize the transmit powers in the stages of channel estimation and multiple access. Finally, extensive simulation results validate the effectiveness of the proposed secure massive access scheme.

This paper considers the application of relay to enhance the performance of massive non-orthogonal multiple access (NOMA) systems and solve the challenge of channel state information acquisition in the case of a massive number of users. First, we design a general framework for a multiple-relay-aided massive NOMA system. Then, we analyze the performance of the multiple-relay-aided massive NOMA system, and derive a closed-form expression for a lower bound on the spectral efficiency. In particular, we reveal the impact of system parameters on the spectral efficiency via asymptotic analysis in three important scenarios, e.g., a large number of antennas at the base station (BS), a high transmit power at the BS or the relays, and a large number of relays. To further improve the spectral efficiency in the context of massive access, we propose two effective schemes to optimize the transmit power at the BS and relays, respectively. Finally, extensive simulation results validate the effectiveness of the proposed multiple-relay-aided massive NOMA scheme.

To achieve spectral-efficient massive access in future wireless networks, this paper proposes a comprehensive fully non-orthogonal communication framework. First, we design a fully non-orthogonal communication scheme which consists of non-orthogonal channel estimation and non-orthogonal multiple access. Then, we analyze the performance of the proposed fully non-orthogonal communication, and derive a tight lower bound on the spectral efficiency in terms of key system parameters and channel conditions. Meanwhile, several novel insights are provided on spectral efficiency via asymptotic analysis in three important cases, i.e., a large number of base station (BS) antennas, a high BS transmit power, and perfect channel state information (CSI) at the BS. Finally, we optimize the performance of the proposed fully non-orthogonal communication and present two simple but efficient optimization algorithms for maximizing the weighted sum of spectral efficiency. Extensive simulation results validate the effectiveness of the proposed schemes.

Despite the increased capabilities of mobile devices, resource-demanded mobile applications still transcend what can be accomplished on a single device. As such, mobile device cloud (MDC), an environment that enables computation-intensive tasks to be performed among a set of nearby mobile devices, offers a promising architecture to support real-time mobile applications. To stimulate mobile devices to execute tasks for others, it is essential to design an incentive mechanism that appropriately charges the owners of the tasks, acted as the buyers, and rewards the mobile devices, acted as the sellers. In this paper, we propose two truthful auction mechanisms for two different task models, heterogeneous and homogeneous task models, which assume the different and the same resource requirements of the tasks, respectively. Specifically, for heterogeneous task model, we propose an efficient heuristic winning bids determination algorithm to allocate the tasks, and decide the payment of each seller for its winning bids. For homogeneous task model, we design an optimal winning bid determination algorithm, and propose a Vickrey-Clarke-Groves (VCG) based auction mechanism to determine the payment of each bid. Both theoretical analysis and simulations show that the proposed auction mechanisms achieve several desirable properties such as individual rationality, truthfulness and computational efficiency.

We study the beamforming design for multiuser systems with simultaneous wireless information and power transfer (SWIPT). Employing a practical non-linear energy harvesting (EH) model, the design is formulated as a non-convex optimization problem for the maximization of the minimum harvested power across several energy harvesting receivers. The proposed problem formulation takes into account imperfect channel state information (CSI) and a minimum required signal-to-interference-plus-noise ratio (SINR). The globally optimal solution of the design problem is obtained via the semidefinite programming (SDP) relaxation approach. Interestingly, we can show that at most one dedicated energy beam is needed to achieve optimality. Numerical results demonstrate that with the proposed design a significant performance gain and improved fairness can be provided to the users compared to two baseline schemes.

In this study, the problem of energy‐efficient power allocation (EEPA) for secrecy wireless information and power transfer in a massive multiple‐input multiple‐output relay aided secure communication system is well addressed. The relay forwards the signal sent from a source to a legitimate destination with the harvested energy based on a decode‐and‐forward relaying protocol, while a passive eavesdropper intends to intercept the message. The authors first derive a closed‐form expression for the secrecy energy efficiency of the considered system under practical conditions, i.e. no instantaneous eavesdropper channel state information (CSI) and only imperfect legitimate CSI. Then, they propose an EEPA scheme for maximising the secrecy energy efficiency. Finally, simulation results validate the effectiveness of the proposed scheme.

This paper aims to provide a comprehensive solution for the design, analysis, and optimization of a multiple-antenna non-orthogonal multiple access (NOMA) system for multiuser downlink communication with both time duplex division and frequency duplex division modes. First, we design a new framework for multiple-antenna NOMA, including user clustering, channel state information (CSI) acquisition, superposition coding, transmit beamforming, and successive interference cancellation. Then, we analyze the performance of the considered system, and derive exact closed-form expressions for average transmission rates in terms of transmit power, CSI accuracy, transmission mode, and channel conditions. For further enhancing the system performance, we optimize three key parameters, i.e., transmit power, feedback bits, and transmission mode. Especially, we propose a low-complexity joint optimization scheme, so as to fully exploit the potential of multiple-antenna techniques in NOMA. Moreover, through asymptotic analysis, we reveal the impact of system parameters on average transmission rates, and hence present some guidelines on the design of multiple-antenna NOMA. Finally, simulation results validate our theoretical analysis, and show that a substantial performance gain can be obtained over traditional orthogonal multiple access technology under practical conditions.

This letter investigates the performance of a legitimate surveillance system, where a legitimate monitor aims to eavesdrop on a dubious decode-and-forward relaying communication link. In order to maximize the effective eavesdropping rate, two strategies are proposed, where the legitimate monitor adaptively acts as an eavesdropper, a jammer, or a helper. In addition, the corresponding optimal jamming beamformer and jamming power are presented. Numerical results demonstrate that the proposed strategies attain better performance compared with intuitive benchmark schemes. Moreover, it is revealed that the position of the legitimate monitor plays an important role on the eavesdropping performance for the two strategies.

This paper investigates the impact of the channel state information (CSI) and antenna correlation at the multi-antenna relay on the performance of wireless powered dual-hop amplify-and-forward relaying systems. Depending on the available CSI at the relay, two different scenarios are considered, namely, instantaneous CSI and statistical CSI where the relay has access only to the antenna correlation matrix. Adopting the power-splitting architecture, we present a detailed performance study for both cases. Closed-form analytical expressions are derived for the outage probability and ergodic capacity. In addition, simple high signal-to-noise ratio (SNR) outage approximations are obtained. Our results show that, antenna correlation itself does not affect the achievable diversity order, the availability of CSI at the relay determines the achievable diversity order. Full diversity order can be achieved with instantaneous CSI, while only a diversity order of one can be achieved with statistical CSI. In addition, the transmit antenna correlation and receive antenna correlation exhibit different impacts on the ergodic capacity. Moreover, the impact of antenna correlation on the ergodic capacity also depends heavily on the available CSI and operating SNR.

Spreading a large file consisting of many packets over a mobile network is challenging due to the short meeting duration for each transmission. Moreover, two typical causes of inefficient file spreading are duplicate packet reception at the destination nodes and excessive overhead exchanges. We propose to employ fountain codes at the source node to jointly addresses the three issues: 1) each coded packet can be small enough to fit into the meeting duration; 2) duplicate packet reception is significantly reduced since each coded packet is innovative; and 3) overhead is greatly saved by using file-level ACK instead of packet-level ACK. We conduct performance analysis in terms of the source-to-destination file delay and source-to-destination file spreading time in both non-relaying and relaying scenarios. While packet duplication can be eliminated in the former scenario, there is still a non-trivial duplication probability if relaying is allowed. Therefore, we propose a fountain-coded two-hop relaying (FTTR) protocol to further reduce the packet duplication ratio so that the spreading performance does not degrade with network size. The file spreading time and packet duplication ratio of FTTR are derived in closed form and verified through simulations.

This paper investigates the impact of antenna correlation on wireless powered dual-hop multi-antenna relaying systems with instantaneous channel state information (CSI) or statistical CSI at the relay. Considering the power-splitting architecture, we study the outage probability as well as the achievable diversity order of the system for amplify-and-forward protocol. Our results show that, antenna correlation itself does not affect the achievable diversity order, the availability of CSI at the relay determines the achievable diversity order. Full diversity order can be achieved with instantaneous CSI, while only unit diversity order can be achieved with statistical CSI. In addition, with instantaneous CSI, antenna correlation is detrimental in the moderate and high SNR regime while it results in a better performance when the operating SNR is low. On the contrary, antenna correlation is always beneficial with only statistical CSI.

In this paper we investigate the physical layer security for downlink MU-MIMO systems where the transmitter and the eavesdropper are equipped with multiple antennas, while the legitimate users have single antenna. This is a more challenging topic because the eavesdropper appears to be more powerful than the legitimate users. We propose a transmission scheme able to enhance secure transmission for legitimate users, by intentionally applying different beamforming matrices to pilot signals and data signals. The beamforming matrix for data signals is constructed in a way that legitimate users can derive the channel matrix experienced by data signals based on pilot signals, whereas the eavesdropper fails to obtain the channel matrix experienced by data signals even with the aid of pilot signals. Therefore coherent detection at the eavesdropper is impossible and the signal-plus-interference-to-noise (SINR) of the eavesdropper is significantly degraded. In addition, the proposed beamforming design is formulated as an optimization problem to maximize the minimum SINR. We theoretically prove that with this formulation, the resultant optimal SINR is not affected by the proposed format of beamforming matrix for data signals. Numerical results show that, the proposed beamforming design outperforms the conventional beamforming design in terms of ergodic secrecy sum rate. Furthermore, with suitable beamforming designs, the ergodic secrecy sum rate achieved will not be noticeably affected by the number of antennas at the eavesdropper, although it is significantly degraded with conventional beamforming design.

This paper considers a wirelessly powered wiretap channel, where an energy constrained multi-antenna information source, powered by a dedicated power beacon, communicates with a legitimate user in the presence of a passive eavesdropper. Based on a simple time-switching protocol, where power transfer and information transmission are separated in time, we investigate two popular multi-antenna transmission schemes at the information source, namely, maximum ratio transmission and transmit antenna selection. Closed-form expressions are derived for the achievable secrecy outage probability and average secrecy rate for both schemes. In addition, simple approximations are obtained at the high signal-to-noise ratio (SNR) regime. Our results demonstrate that by exploiting the full knowledge of channel state information (CSI), we can achieve a better secrecy performance, e.g., with full CSI of the main channel, the system can achieve substantial secrecy diversity gain. On the other hand, without the CSI of the main channel, no diversity gain can be attained. Moreover, we show that the additional level of randomness induced by wireless power transfer does not affect the secrecy performance in the high SNR regime. Finally, our theoretical claims are validated by the numerical results.

In this paper, we address the problem of joint power and time allocation for secure communications in a decode-and-forward massive multiple-input multiple-output (M-MIMO) relaying system in the presence of a passive eavesdropper. We apply the M-MIMO relaying technique to enhance the secrecy performance under very practical and adverse conditions, i.e., no availability of instantaneous eavesdropper channel state information (CSI) and only imperfect instantaneous legitimate CSI. We first provide a performance analysis of secrecy outage capacity, which reveals the minimum required number of relay antennas for achieving a positive secrecy outage capacity. Then, we propose an optimization framework to jointly optimize source transmit power, relay transmit power, and transmission time in each hop, with the goal of maximizing the secrecy outage capacity. Although the secrecy outage capacity is not a concave function with respect to the optimization variables, we show that it can be maximized by first maximizing over some of the variables, and then maximizing over the rest. To this end, we first derive a closed-form solution of optimal relay transmit power, afterward obtain that of optimal source transmit power, and then derive the optimal ratio of the first-hop duration to a complete transmission time. Moreover, several important system design insights are provided through asymptotic performance analysis. Finally, simulation results validate the effectiveness of the proposed joint resource allocation scheme.

Abstract In this paper, a joint spectrum sensing and accessing optimization framework for a multiuser cognitive network is proposed to significantly improve spectrum efficiency. For such a cognitive network, there are two important and limited resources that should be distributed in a comprehensive manner, namely feedback bits and time duration. First, regarding the feedback bits, there are two components: sensing component (used to convey various users' sensing results) and accessing component (used to feedback channel state information). A large sensing component can support more users to perform cooperative sensing, which results in high sensing precision. However, a large accessing component is preferred as well, as it has a direct impact on the performance in the multiuser cognitive network when multi‐antenna technique, such as zero‐forcing beamforming, is utilized. Second, the tradeoff of sensing and accessing duration in a transmission interval needs to be determined, so that the sum transmission rate is optimized while satisfying the interference constraint. In addition, the aforementioned two resources are interrelated and inversive under some conditions. Specifically, sensing time can be saved by utilizing more sensing feedback bits for a given performance objective. Hence, the resources should be allocation in a jointly manner. Based on the joint optimization framework and the intrinsic relationship between the two resources, we propose two joint resource allocation schemes by maximizing the average sum transmission rate in a multiuser multi‐antenna cognitive network. Simulation results show that, by adopting the joint resource allocation schemes, obvious performance gain can be obtained over the traditional fixed strategies.Copyright © 2014 John Wiley & Sons, Ltd.

In this paper, we address the problem of optimal power allocation at the relay in two-hop secure communications under practical conditions. To guarantee secure communication during the long-distance transmission, the massive MIMO (M-MIMO) relaying techniques are explored to significantly enhance wireless security. The focus of this paper is on the analysis and design of optimal power assignment for a decode-and-forward (DF) M-MIMO relay, so as to maximize the secrecy outage capacity and minimize the interception probability, respectively. Our study reveals the condition for a nonnegative the secrecy outage capacity, obtains closed-form expressions for optimal power, and presents the asymptotic characteristics of secrecy performance. Finally, simulation results validate the effectiveness of the proposed schemes.

In this paper, we address the problem of optimal power allocation at the relay in two-hop secure communications. In order to solve the challenging issue of short-distance interception in secure communications, the benefit of large-scale MIMO (LS-MIMO) relaying techniques is exploited to improve the secrecy performance significantly, even in the case without eavesdropper channel state information (CSI). The focus of this paper is on the analysis and design of optimal power allocation for the relay, so as to maximize the secrecy outage capacity. We reveal the condition that the secrecy outage capacity is positive, prove that there is one and only one optimal power, and present an optimal power allocation scheme. Moreover, the asymptotic characteristics of the secrecy outage capacity is carried out to provide some clear insights for secrecy performance optimization. Finally, simulation results validate the effectiveness of the proposed scheme.

This paper studies the capacity scaling law of the multi-pair relay network with K source-destination pairs and M relays, where each node is equipped with a single antenna and works in half duplex mode. With the conventional two-slot relaying, the capacity was found to scale as K/2 log (M)+O(1) for fixed K and M → ∞. This paper shows that the capacity scaling law can be further improved to K log (M)+O(1) with successive relaying, as if the relays were full duplex. This scaling law can be achieved by a distributed coherent amplify-and-forward scheme, which only requires local channel state information (CSI) at each relay and statistical CSI at the sources and destinations.

This paper investigates the average throughput of a wireless-powered communications system, where an energy constrained source, powered by a dedicated power beacon (PB), communicates with a destination. It is assumed that the PB is capable of performing channel estimation, digital beamforming, and spectrum sensing as a communication device. Considering a time-splitting approach, the source first harvests energy from the PB equipped with multiple antennas, and then transmits information to the destination. Assuming Nakagami-m fading channels, analytical expressions for the average throughput are derived for two different transmission modes, namely, delay tolerant and delay intolerant. In addition, closed-form solutions for the optimal time split, which maximize the average throughput are obtained in some special cases, i.e., high-transmit power regime and large number of antennas. Finally, the impact of cochannel interference is studied. Numerical and simulation results have shown that increasing the number of transmit antennas at the PB is an effective tool to improve the average throughput and the interference can be potentially exploited to enhance the average throughput, since it can be utilized as an extra source of energy. Also, the impact of fading severity level of the energy transfer link on the average throughput is not significant, especially if the number of PB antennas is large. Finally, it is observed that the source position has a great impact on the average throughput.

In this paper, we consider a large scale multiple input multiple output (LS-MIMO) relaying system, where an information source sends the message to its intended destination aided by an LS-MIMO relay, while a passive eavesdropper tries to intercept the information forwarded by the relay. The advantage of a large scale antenna array is exploited to improve spectral efficiency and enhance wireless security. In particular, the challenging issue incurred by short-distance interception is well addressed. Under very practical assumptions, i.e., no eavesdropper channel state information (CSI) and imperfect legitimate CSI at the relay, this paper gives a thorough secrecy performance analysis and comparison of two classic relaying techniques, i.e., amplify-and-forward (AF) and decode-and-forward (DF). Furthermore, asymptotical analysis is carried out to provide clear insights on the secrecy performance for such an LS-MIMO relaying system. We show that under large transmit powers, AF is a better choice than DF from the perspectives of both secrecy performance and implementation complexity, and prove that there exits an optimal transmit power at medium regime that maximizes the secrecy outage capacity.

Abstract This paper introduces the limited feedback precoding into the distributed antenna system and proposes to adapt the predetermined orthogonal space time block codes to the available channel state information at the transmitter. The optimal representation of precoding information, namely the precoder, with least bits therefore becomes the key problem. Inspired by the characteristics of the distributed antenna system, we focus our work on the precoder construction, adaptable in response to the large and small scale fading, such that the symbol error probability is significantly reduced over that of a fixed, non‐adaptive, independent and identically distributed precoder codebook design. Furthermore, a suboptimal power‐loading strategy is presented by minimizing the derived tight upper bound on the average pairwise error probability of the precoded orthogonal space time block codes, which approaches the optimal performance asymptotically without additional channel knowledge other than the available feedback information. We prove that the proposed precoded orthogonal space time transmission scheme can achieve full diversity order. In particular, the robustness of our proposed transmission scheme to channel estimation error and feedback delay is respectively investigated in some detail, and numerical results show that it obviously improves the link reliability and obtains substantial gains even with few bits of feedback in comparison with conventional antenna selection scheme. Copyright © 2013 John Wiley & Sons, Ltd.

In this paper, we address the problem of secure wireless information and power transfer in a large-scale multiple-input multiple-output (LS-MIMO) amplify-and-forward (AF) relaying system. The advantage of LS-MIMO relay is exploited to enhance wireless security, transmission rate and energy efficiency. In particular, the challenging issues incurred by short interception distance and long transfer distance are well addressed simultaneously. Under very practical assumptions, i.e., no eavesdropper's channel state information (CSI) and imperfect legitimate channel CSI, this paper investigates the impact of imperfect CSI, and obtains an explicit expression of the secrecy outage capacity in terms of transmit power and channel condition. Then, we propose an optimal power splitting scheme at the relay to maximize the secrecy outage capacity. Finally, our theoretical claims are validated by simulation results.

In this paper, we study the problem of physical layer security in large-scale multiple input multiple output (LS-MIMO) systems. The large number of antenna elements in LS-MIMO system is exploited to enhance transmission security and improve system performance, especially when the eavesdropper is closer to the information source and has more antennas than the legitimate user. However, in practical systems, the problem becomes challenging because the eavesdropper channel state information (CSI) is usually unavailable without cooperation and the legitimate CSI may be imperfect due to channel estimation error. In this paper, we first analyze the performance of physical layer security without eavesdropper CSI and with imperfect legitimate CSI, and then propose an energy-efficient power allocation scheme to meet the demand for wireless security and quality of service (QoS) simultaneously. Finally, numerical results validate the effectiveness of the proposed scheme.

In this paper, we analyze the ergodic sum rate in a multiuser multi-cell downlink. Coordinated beamforming is employed to mitigate the interference, including intra-cell and inter-cell interference. However, due to the limited capacity of the backhaul link, only partial channel state information (CSI) is obtained at the base stations (BSs), resulting in residual interference even with coordinated beamforming. By quantifying the impact of imperfect CSI, we derive closed-form ergodic sum rate results for a multiuser multi-cell downlink in terms of i. CSI accuracy, ii. transmit signal-to-noise ratio (SNR) and iii. channel condition. Furthermore, through asymptotic analysis of the performance loss induced by imperfect CSI, we obtain some clear insights on the performance. Finally, our theoretical claims are validated through extensive simulations.

To upgrade the systems or fix the security issues, some physical machines (PMs) in data centers are required to undergo a maintenance process, which might disable the continuous services of the virtual machines (VMs) run on them for a few time slots. To reduce the waiting delay, one may migrate the VMs to other active PMs. However, it will incur extra migration cost, e.g., bandwidth or memory used to move data. To balance the tradeoff between delay and migration cost, we formulate a two-objective optimization problem, which minimizes both delay and migration cost according to a certain weightage, so as to decide whether the VMs should be migrated to other active PMs or should wait their own maintained PMs to be back. We first prove that the proposed problem is NP-hard. For a special case, where each VM requires the same size of resource, we show that the defined problem can be converted to minimum weighted bipartite matching problem in an auxiliary bipartite graph. A lower bound of the delay is derived for a specific setting. For the general case of the problem, we also design an efficient heuristic algorithm. Finally, simulation results demonstrate the effectiveness of the proposed scheme.

Wireless information and energy transfer (WIET) is a prominent technology to prolong the lifetime of battery-charging wireless networks. In this paper, we exploit the benefit of massive MIMO for WIET under external interference, and propose the antenna partition for information decoding and energy harvesting. Considering the effects of the external interference, i.e., interfering the information reception and benefiting the energy harvesting, we analyze the tradeoff between the data rate and the harvested energy, and obtain the achievable rate-energy (R-E) region. Then, we propose a low-complexity receive antenna partition algoritinterference mitigationhm for WIET in massive MIMO systems with the consideration of interference mitigation. The algorithm maximizes the data rate while guaranteeing a minimum harvested energy. It is found that the SNR of the low-complexity algorithm is at least an approximable half of the optimal SNR. Simulation results verify our theoretical claims and show the effectiveness of the proposed low-complexity antenna partition algorithm.

In this paper, we study the problem of physical layer security in a large-scale multiple-input multiple-output (LS-MIMO) relaying system. The advantage of LS-MIMO relaying systems is exploited to enhance both wireless security and spectral efficiency. In particular, the challenging issue incurred by short interception distance is well addressed. Under very practical assumptions, i.e., no eavesdropper's channel state information (CSI) and imperfect legitimate channel CSI, this paper gives a thorough investigation of the impact of imperfect CSI in two classic relaying systems, i.e., amplify-and-forward (AF) and decode-and-forward (DF) systems, and obtain explicit expressions of secrecy outage capacities for both cases. Finally, our theoretical claims are validated by the numerical results.

Two‐path successive relaying (TPSR) is an effective way to reduce the multiplex loss induced by the half‐duplex operation of the relay node in a conventional relay network. One crucial issue in TPSR network is that the listening relay always suffers inevitable inter‐relay interference (IRI), which degrades detection performance at the destination. In this study, a concatenated channel‐and‐network coding approach is proposed to solve the problem. In particular, a highly flexible channel code, namely, rateless code, is employed at the source to provide resilience to the residual IRI and reduce the retransmissions, which might break the system steady state of successive relaying. Then recognising the special interference structure, physical‐layer network coding is incorporated into the forwarding scheme of the relay nodes to exploit network diversity and improve system efficiency. By extrinsic information transfer analysis, the minimum number of required code symbols for successful data recovery are calculated, and degree distribution of the rateless code is optimised.

In this study, the authors propose a new class of rateless codes applicable to noisy channels, that is, the so‐called accumulate rateless (AR) codes, which concatenate the low density generator matrix (LDGM) rateless code with a simple post‐code, that is, a post‐position accumulator. The new coding structure is not only effective in reducing the ‘error floor’ as observed in traditional LDGM rateless codes such as Luby transform (LT) codes, but also quite simple for realisation as compared with the rateless codes using a pre‐coding structure such as Raptor codes. The extrinsic information transfer charts and the corresponding projection of intersectant curves for both the systematic and non‐systematic AR codes are analysed. Based on these, their convergence performance and optimal degree distributions are investigated. Simulation results show that, the performance of AR codes over additive white Gaussian noise channel is comparable to that of Raptor codes.

In this paper, we propose a joint spectrum sensing and spectrum access tradeoff framework for a multi-user multiple-input single-output (MISO) cognitive radio network with limited feedback in order to improve spectrum efficiency. Firstly, we reduce the feedback information of requests for data retransmission by employing the recently developed incremental redundancy channel code, namely rateless code, for the data transmission of secondary users. Then by jointly considering the effects of multi-user cooperative sensing and multi-user MISO beamforming to the system performances, we propose a feedback bit allocation method for both the spectrum sensing results and the quantized channel state information under a sum feedback amount constraint, so as to maximize the throughput of cognitive radio network while protecting primary user from interference. Finally, simulation results are shown to validate the effectiveness of our proposed method.

Rateless coding is a new type of feed-forward incremental redundancy channel coding technique which can be incorporated with MIMO technology to exploit both the diversity and coding gain with possibly reduced channel feedback. In this paper, the benefits of limited feedback beamforming and rateless coding are investigated jointly in a multiple-input single-output (MISO) downlink system. Based on weight enumerator analysis and with the goal of improving the effective channel gain, we propose an adaptive bit allocation scheme by taking advantage of the inherent relationship between the feedback codebook size and the number of transmitted coded bits. Given the feedback codebook size, we derive the required minimum number of coded bits for a reliable data recovery. In addition, for the service with time delay constraint, the required feedback codebook size is also determined. Finally, numerical results are presented to validate our theoretical analysis.

Transmit power and feedback bandwidth are two limited and interrelated resources which are crucial to system performance in wireless channel with feedback, so it is necessary to maximize their utilization efficiencies in the joint sense. In this paper, we investigate the inherent relationship between transmit power and codebook size in multiuser limited feedback MISO system by making use of Grassmann line packing theory, as an effort to provide an insight on how to jointly distribute the two resources to fulfill the diverse requirements. Then, the impact of feedback delay on the tradeoff relation is characterized in detail, and we find that even with relatively small delay, there is considerable performance loss with respect to the ideal case. Thereby, much more transmit power or feedback bandwidth should be consumed to achieve the same performance target. Finally, the theoretical claims are validated by numerical results.

The problem of diversity-multiplexing-rate trade-off (DMRT) in limited feedback beamforming systems is addressed here. A major difference from previous works is that the authors consider the trade-off in finite-signal-to-noise ratio (SNR) regime. Under this condition, the feedback rate has a great impact on the conventional diversity-multiplexing relationship. Hence, the release of the trade-off among diversity, multiplexing and feedback rate has a practical benefit to the design and adoption of space–time signals in limited feedback beamforming systems. Asymptotical analysis shows that the obtained trade-off is consistent with the conventional one when feedback rate equals to zero and the SNR approaches infinity. Furthermore, considering the channel dynamics, the authors investigate the effect of imperfect channel state information (CSI) caused by channel estimation error and feedback delay on the DMRT. It is found that in the presence of imperfect CSI, besides the system parameters, such as the number of transmit and receive antennas, maximum diversity order and multiplexing gain are also limited by SNR and correlation coefficient between the obtained CSI and the real CSI. Finally, our theoretical claims are validated by the numerical results.

In this paper, the benefit of rateless codes combining with multi-antenna technique is exploited to provide quality-of-service (QoS) guarantee while maximizing the spectral efficiency in a limited feedback multiuser MIMO downlink. Critical to the design of such a system is the achievement of channel state information (CSI) at the base station (BS) to schedule the optimal users and pre-cancel the interuser interference. In order to reduce feedback amount and decrease scheduling complexity simultaneously, we propose to adopt multi-beam opportunistic beamforming (MOBF) and opportunistic space division multiple access (OSDMA) for the cases of noise and interference limited, respectively. Through theoretical analysis, it is found that, in order to satisfy QoS requirement, the admissible number of users has an upper bound, which is a function of data arrival rate and QoS requirement. With the purpose of balancing the achievable rate and feedback amount, we introduce a new concept, namely netput, as the difference of the above focused factors. By maximizing the netput, we derive the optimal feedback thresholds for the two transmission modes, respectively. Furthermore, according to the characteristics of the considered system, we obtain the optimal switch thresholds for the two modes in terms of the number of users and the SNR, respectively. Finally, our theoretical claims are validated by the numerical results.

A novel Distributed Spectrum-Aware Clustering (DSAC) scheme is proposed in the context of Cognitive Radio Sensor Networks (CRSN). DSAC aims at forming energy efficient clusters in a self-organized fashion while restricting interference to Primary User (PU) systems. The spectrum-aware clustered structure is presented where the communications consist of intra- cluster aggregation and inter-cluster relaying. In order to save communication power, the optimal number of clusters is derived and the idea of groupwise constrained clustering is introduced to minimize intra-cluster distance under spectrum-aware constraint. In terms of practical implementation, DSAC demonstrates preferable scalability and stability because of its low complexity and quick convergence under dynamic PU activity. Finally, simulation results are given to validate the proposed scheme.

A novel concept of Joint Source and Channel Sensing (JSCS) is introduced in the context of Cognitive Radio Sensor Networks(CRSN). Every sensor node has two basic tasks: application-oriented source sensing and ambient-oriented channel sensing. The former is to collect the application-specific source information and deliver it to the access point within some limit of distortion, while the latter is to find the vacant channels and provide spectrum access opportunities for the sensed source information. With in-depth exploration, we find that these two tasks are actually interrelated when taking into account the energy constraints. The main focus of this paper is to minimize the total power consumed by these two tasks while bounding the distortion of the application-specific source information. Firstly, we present a specific slotted sensing and transmission scheme, and establish the multi-task power consumption model. Secondly, we jointly analyze the interplay between these two sensing tasks, and then propose a proper sensing and power allocation scheme to minimize the total power consumption. Finally, simulation results are given to validate the proposed scheme.

In this paper, we consider the adaptive power allocation in the MIMO cognitive network with limited feedback. It is well known that, with the view of guaranteeing the link quality of primary receiver (PRx), the transmit power of second transmitter (STx) is strictly constrained, resulting in that it is difficult to provides QoS guarantee to second receiver (SRx) in the fading channel. This paper focuses on searching a feasible scheme to satisfy the QoS of PRx and SRx currently. First, we prove that the capacity of SRx can be obviously improved without adding interference to PRx by exploiting the spatial degrees of freedom of MIMO. Then, in order to making use of the benefit of MIMO, joint maximum ratio transmission (MRT) and maximum ratio combination (MRC) with finite rate channel information feedback, a way to adapt the signal to the instantaneous channel condition, is adopted. The relationship of QoS provisioning and feedback amount is researched so as to meet the performance requirements with the least feedback bits. Next, based on the above MIMO setting, an adaptive power allocation strategy is proposed to maximize the average capacity of SRx while satisfy the dual QoS requirements. Finally, numerical results reconfirms the effectiveness of the proposed adaptive strategy.

In this paper, we investigate a channel selection algorithm for distributed spectrum access in a multichannel multiuser cognitive radio (CR) network employing rateless codes. Each secondary user (SU) uses rateless codes to increase the tolerance of interference either from other SUs or from the primary user (PU). However, due to the unpredictable arrival of PU and the inaccuracy of spectrum sensing, the more the number of channels each SU selects, the more interference to PU will be caused. With the purpose of protecting PU from interference, we derive the optimal number of channels selected by SU to maximize the throughput of SU. Both theoretical analysis and simulation results demonstrate the efficiency of the proposed algorithm.

In this paper, a framework of opportunistic beamforming for multiuser MIMO downlink employing rateless codes is considered. By exploiting the benefits of a combination of opportunistic beamforming and rateless codes, the system performance is obviously improved while guaranteeing QoS requirement, such as maximum average delay. It is proved that, in order to satisfying delay constraint, the maximum number of admissible users is bounded. With the purpose of achieving a balance between throughput and feedback, a novel concept of netput is introduced accordingly. Through maximizing the netput, the optimal feedback threshold in terms of the number of user, the maximum delay constraint, transmit power and the arrival rate of user data at the base station (BS) is derived. Moreover, the impact of feedback delay on the netput is investigated in detail and the corresponding feedback threshold is given. Finally, our theoretical claims are validated by the numerical results.

In this paper, based on the effective capacity theory, we identify the maximal arrival rate of secondary user that an arbitrary ON/OFF primary channel can sustain, in the presence of sensing errors. We find that, the arrival rate of secondary user with statistical QoS requirement, is limited by both the effective capacity provided by primary channel, and the packet collision probability constraint of primary user. In general, the above two constraints result in unequal arrival rates, exhibiting different impacts on spectrum utilization. Based on this observation, two spectrum utilization approaches, i.e., η-probability random access and sensing parameter adjustment, respectively, are proposed to fully utilize the transmission opportunities, depending on whether the sensing parameters could be adjusted or not. In specific, the η-probability random access approach reserves more transmission opportunities for other secondary users, which increases network throughput, while sensing parameter adjustment approach yields improved arrival rate for specific secondary user. Performances of the proposed approaches are validated by numerical results.

Recently, distributed antenna system (DAS) has received considerable attentions due to its promising potential in various metrics compared with traditional co-located MIMO (C-MIMO) systems. Aiming to further improve the performance of the DAS, linear precoding along with distributed space time codes is proposed to exploit the diversity and coding gains simultaneously. The focus of this paper is on analyzing and designing a simple but powerful codebook suitable for the DAS to quantize the precoding information. Furthermore, a suboptimal power allocation strategy that requires no additional information at the base station (BS), is derived by minimizing the upper bound on the average pairwise error probability (PEP) of the preceded space time codewords. Numerical results show that obvious gains can be obtained over conventional transmission schemes, such as antenna selection.