In reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) systems, beamforming design based on minimizing the Cramér-Rao bound (CRB) for target direction-of-arrival (DoA) estimation is pivotal for sensing capability enhancement. However, due to practical hardware limitations, phase-shift errors (PSEs) exist at the RIS reflectors and cause performance deterioration. To reduce the adverse impact of PSEs, we develop a novel stochastic optimization (SO)-based expected CRB (ECRB) minimization framework, where the ECRB is defined as the expectation of CRB taken over random PSEs, statistical channel state information (CSI), and historical DoA estimates following known prior distributions. Specifically, we formulate an SO problem to minimize the ECRB, subject to an ergodic achievable sum-rate (EASR) constraint. To solve this non-convex problem, we propose a novel penalty-based block stochastic gradient descent (PBSGD) method. In this method, we first introduce a penalty factor to move the EASR constraint into the objective function. The optimal penalty factor is rigorously proved to be determinable via a bisection search. Then, we design a projected block stochastic gradient descent process to update transmit beamformers and RIS phase shifts alternately with guaranteed convergence. Simulation results demonstrate that our proposed method outperforms several benchmarks, including random phase-shift design, sensing-only beamforming, and state-of-the-art semidefinite relaxation (SDR)-based CRB optimization techniques, while exhibiting enhanced robustness against PSEs.

Intelligent reflecting surface (IRS)-assisted integrated sensing and communications (ISAC) systems have been extensively studied to meet higher sensing requirements. For detection-oriented IRS-assisted ISAC problems, most studies have overlooked the detection interference caused by clutters and modeled simplified point-like targets. This paper investigates extended target detection in IRS-assisted ISAC systems within clutters. We present an optimal generalized likelihood ratio test detector and derive the corresponding probability of detection (PD) and probability of false alarm in closed form. Then, we jointly optimize the active and passive beamforming of the base station and IRS to maximize the PD under multi-user equipment (UE) communication rate constraints and the total transmit power constraint. We first simplify the complex objective function by proving the invariant property of a subspace projection matrix. We then present a novel alternating optimization (AO)-based algorithm to decouple the original problem into two subproblems, consequently convexified and solved using the semidefinite relaxation method. Simulations demonstrate the convergence of the proposed algorithm. The PD performance and the communication and sensing trade-off are significantly improved, compared to benchmarks.

This paper proposes a new Signal-to-interference-plus-noise ratio (SINR)-based Device selection, Power control, and Reconfigurable intelligent surface (RIS) configuration (SDPR) algorithm, which allows imperfect aggregation of wireless federated learning (FL) in RIS-assisted Non-Orthogonal Multiple Access (NOMA) systems. The SDPR algorithm selects the local models with SINRs within an acceptable range for global aggregations, benefiting FL from involving more local models with tolerable errors. The convergence of FL under the imperfect aggregation is analytically validated, where the influence of the local model quantization and modulation is captured through the translation of the SINR thresholds to the symbol error rates (SERs). Employing successive convex approximation and gradient descent, we jointly optimize the RIS configuration and the transmit powers of participating devices, thereby minimizing the convergence upper bound of FL under imperfect aggregation. Experimental results demonstrate that using SDPR, FL achieves superior convergence and accuracy by effectively utilizing model updates, even if they are received with errors. Moreover, more quantization bits do not necessarily offer better FL accuracy, and need to be tailored under specific SERs.

In dual-functional radar-communication (DFRC) systems, to achieve desirable performance in both direction-of-arrival (DoA) estimation for sensing targets and wireless communication for user equipments (UEs), the Cramér-Rao bound (CRB) for DoA estimation and the communication rates of UEs should be jointly optimized through beamforming design. However, the CRB function is inherently dependent on prior knowledge of DoA, which may only be obtained through existing estimators that introduce unavoidable estimation errors. Such errors inevitably degrade the optimization performance. To address this issue, we propose novel optimization methodologies for CRB minimization and communication rate guarantees, which effectively reduce the adverse impact of target DoA estimation errors. Specifically, considering a bounded DoA error model and a statistical DoA error model, two optimization problems are formulated to optimize the worst-case CRB and the statistical mean of CRB over the DoA error regions, while ensuring that the communication rates of multiple UEs exceed predefined thresholds. To tackle the first problem, we propose a semidefinite relaxation (SDR)-based iterative entropic regularization (SDR-IER) method, acquiring approximate solutions via alternating outer minimization and inner maximization. For the second problem, we develop a vectorial space analysis (VSA)-based projected stochastic gradient descent (VSA-PSGD) approach, featuring closed-form projections per iteration for single-user cases, and successive convex approximation (SCA)-based projections for multi-user cases. Simulation results demonstrate that our proposed methods exhibit enhanced robustness against target DoA estimation errors, compared with the existing benchmarks that do not take these errors into account.

Reconfigurable intelligent surface(RIS)-assisted communication systems have been extensively studied for providing high-precision location services. However, most studies have overlooked the impact ofcarrier frequency offset(CFO) andphase noise(PN) resulting from hardware impairments on localization. This paper presents a novel,alternating optimization(AO)-based algorithm to jointly estimate the CFO, PN, anduser equipment(UE) position inorthogonal frequency division multiplexing(OFDM) systems, where, provided the UE position, closed-form expressions for the CFO and PN are derived per iteration, significantly reducing the complexity and enhancing the stability of the algorithm. Another important aspect is a new RIS phase shift optimization algorithm developed to minimize the analytical lower bound of localization accuracy, hence benefiting localization. The semidefinite relaxation method and Schur complement are utilized to convexify this challenging non-convex optimization problem to a semidefinite program. Simulations demonstrate the effectiveness of the proposed algorithms, with the localization accuracy enhanced by two orders of magnitude. The localization accuracy of the proposed algorithm is close to the analytical lower bound, with a root mean square error of lower than 10−2m.

This article focuses on integrated navigation systems using a combination of Global Navigation Satellite Systems (GNSS) and fifth-generation (5G) technology in urban environments. To address the challenge of accurately estimating the mobile terminal state in multipath environments, a robust evolutionary particle filter (REPF) technique is proposed. First, this article presents a modified clock compensation two-way pseudorange scheme that significantly improves pseudorange accuracy in nonideal line-of-sight/nonline-of-sight (LOS/NLOS) pseudorange environments. Then, this article derives the posterior belief conditioned on the obtained pseudorange measurements and velocity data from GNSS. Utilizing the aforementioned posterior distribution, we introduce a robust particle filter (RPF) algorithm to gauge both the sight state and localization in environments with multipath effects. To address the issue of particle degradation in the RPF algorithm, this article introduces a new evolutionary algorithm based on genetic theory to enhance the diversity of particle filtering. The proposed REPF technique is assessed in 5G ultradense networks, and simulation results demonstrate its achievement of accuracy in positioning and tracking at the meter level for moving targets in both LOS and NLOS environments.

In this paper, we address the key enabling technologies for collaborative global navigation satellite system/fifth generation (GNSS/5G) navigation in urban environments. First, we derive the posterior belief over the state space conditioned on the given ranging measurements in non-ideal ranging environment (e.g., line-of-sight/non-line-of-sight (LOS/NLOS)) and GNSS velocity. Then, building on the premises of non-ideal ranging in LOS/NLOS, a robust particle filter (RPF) algorithm adopted for urban environments is proposed to estimate the sight state and position of the mobile terminal (MT). Statistics on 5G measurement error in a LOS environment and in the presence of NLOS are presented. Finally, comprehensive performance evaluations are carried out in 5G ultra-dense networks. Simulation results demonstrate that meter-scale positioning and tracking accuracy can be achieved in LOS/NLOS using the proposed RPF technique.

The intelligent reflecting surface (IRS) is promising in assisting user localization and wireless communication in the future wireless networks. In this paper, a novel IRS-aided joint localization and communication (L&C) scheme is designed in a millimeter-wave transmission system. For the proposed scheme, the user position/orientation estimation error bound (POEB) and the effective achievable data rate (EADR) are derived in closed-form as L&C performance metrics, which reveal the inherent trade-off between L&C capabilities. To achieve the joint optimal point of the POEB and EADR in consideration of the localization errors, a worst-case robust beamforming and time allocation optimization problem is formulated. To solve the original non-convex problem, a novel joint optimization approach is developed. Specifically, from an equivalent minimax problem, the local optimal solutions of the transceiver beamformers, the IRS phase-shift matrix, and the time allocation ratio between user localization stage (ULS) and effective data transmission stage (EDTS), are obtained in closed-form with respect to the localization errors. Then, the worst-case localization error is iteratively found by a dedicated majorize-minimization (MM) based algorithm. Subsequently, potential extensions to general wireless channels and discrete phase-shift models are discussed in detail. Finally, simulations are carried out to show the optimization results and the L&C performance trade-off. In comparison with the conventional non-robust method, the proposed approach is validated to be robust against the user localization uncertainty.

To exploit the potential of the reconfigurable intelligent surface (RIS) in supporting integrated sensing and communication (ISAC), this paper proposes a novel joint active and passive beamforming design for RIS-enabled ISAC system in consideration of the target size. First, the detection probability for target sensing is derived in closed-form based on the illumination power on an approximated scattering surface area of the target, and a new concept of ultimate detection resolution (UDR) is defined for the first time to measure the target detection capability. Then, an optimization problem is formulated to maximize the signal-to-noise ratio (SNR) at the user-equipment (UE) under a minimum detection probability constraint. To solve this non-convex problem, a novel alternative optimization approach is developed. In this approach, the solutions of the communication and sensing beamformers are obtained by our proposed bisection-search based method. The optimal receive combining vector is derived from an equivalent Rayleigh-quotient problem. To optimize the RIS phase shifts, the Charnes-Cooper transformation is conducted to cope with the fractional objective, and a novel convexification process is proposed to convexify the detection probability constraint with matrix operations and a real-valued first-order Taylor expansion. After the convexification, a successive convex approximation (SCA) based algorithm is designed to yield a suboptimal phase-shift solution. Finally, the overall optimization algorithm is built, followed by detailed analyses on its computational complexity, convergence behavior and problem feasibility condition. Extensive simulations are carried out to testify the analytical properties of the proposed beamforming design, and to reveal two important trade-offs, namely, communication vs. sensing trade-off and UDR vs. sensing-duration trade-off. In comparison with several existing benchmarks, our proposed approach is validated to be superior when detecting targets with practical sizes.

The large overhead arising from conventional channel estimations in reconfigurable intelligent surface (RIS) aided millimeter-wave communication systems, may offset the performance gain brought by the RIS. To tackle this issue, we propose a location information assisted beamforming design without the requirement of the channel training process. First, we establish the geometrical relationship between the channel model and the user location, and mathematically derive an approximate channel state information (CSI) error bound based on the user location error region. Then, for combating the negative impact of the location error on the communication performance, we formulate a worst-case robust beamforming optimization problem to optimize the beamformer at the base station (BS) and the phase-shift matrix at the RIS. To solve this non-convex problem, we develop a novel relaxed alternating optimization process (RAOP) by utilizing various optimization tools, such as the Lagrange multiplier, the matrix inversion lemma, the semidefinite relaxation (SDR), as well as the branch-and-bound (BnB). Additionally, we prove sufficient conditions for the SDR to yield rank-one solutions, and modify the BnB to acquire the phase-shift solution under an arbitrary constraint of possible phase-shift values. Finally, we analyse the convergence and complexity of the proposed RAOP, and carry out simulations for performance evaluations. Compared to the conventional non-robust beamforming, our method performs better and shows strong robustness against the location-error-related CSI uncertainty. Compared to the robust beamforming based on the S-procedure and penalty convex-concave procedure (CCP), our method with BnB shows the advantages of being able to converge faster and handle arbitrary phase-shift argument sets.

The intelligent reflecting surface (IRS) is an artificial metasurface making the communication environment smart and controllable. The IRS on an aerial platform (AIRS) expands the wireless network to the three-dimensional space, thus improving the degree of freedom (DoF) for the signal adjustment. Since the AIRS-enabled wireless channel is generally time-variant in practice, herein, this paper considers the time-varying characteristic of the downlink channels, and proposes a complex-valued ResNet-based deep Q-learning (DQN) algorithm to maximize the sum-rate at user equipment (UE) side, by jointly designing the transmit beamforming at base station (BS) side and the reconfigurable phase shifts at AIRS side. Our results reveal that the proposed complex-valued deep reinforcement learning (DRL) approach shows stronger generalization ability in comparison with the real-valued DRL algorithms, and is validated to be able to mitigate the problem of gradient vanishing and improve the performance over the time-varying downlink channels.

In the wireless communication, deep reinforcement learning (DRL) techniques promise performance optimizations at a low cost. Considering the time-varying property of the wireless downlink channels, this paper proposes a deep deterministic policy gradient (DDPG) approach and a hierarchical DDPG (h-DDPG) approach to optimize the sum-rate at the user equipment (UE) side, by jointly designing the power control and the beam-forming at the base station (BS). Our results demonstrate that the proposed DDPG enables continuous data representation through the deterministic policy functions, while the proposed h-DDPG is able to mitigate the sparse reward problem. Both of the two DRL algorithms are superior to the conventional deep Q-learning (DQN) algorithm, in terms of improving the communication performance over the time-varying wireless downlink channels.

Intelligent reflecting surface (IRS) is envisioned as a promising hardware solution to hardware cost and energy consumption in the fifth-generation (5G) mobile communication network. It exhibits great advantages in enhancing data transmission, but may suffer from performance degradation caused by inherent hardware impairment (HWI). For analysing the achievable rate (ACR) and optimizing the phase shifts in the IRS-aided wireless communication system with HWI, we consider that the HWI appears at both the IRS and the signal transceivers. On this foundation, first, we derive the closed-form expression of the average ACR and the IRS utility. Then, we formulate optimization problems to optimize the IRS phase shifts by maximizing the signal-to-noise ratio (SNR) at the receiver side, and obtain the solution by transforming non-convex problems into semidefinite programming (SDP) problems. Subsequently, we compare the IRS with the conventional decode-and-forward (DF) relay in terms of the ACR and the utility. Finally, we carry out simulations to verify the theoretical analysis, and evaluate the impact of the channel estimation errors and residual phase noises on the optimization performance. Our results reveal that the HWI reduces the ACR and the IRS utility, and begets more serious performance degradation with more reflecting elements. Although the HWI has an impact on the IRS, it still leaves opportunities for the IRS to surpass the conventional DF relay, when the number of reflecting elements is large enough or the transmitting power is sufficiently high.

Here, the authors present a novel, simple, and effective way of improving additive white Gaussian noise resistance and reducing the signal‐to‐noise ratio (SNR) required for modulation recognition. Working on the theoretical basis that the ratio of two logarithmic functions with the same variable is a constant, the authors selected QPSK, 16QAM, and 64QAM for investigation. For each of these, the authors constructed distribution curves of higher‐order cumulants, using SNR as the variable, and examined how they might work as logarithmic curves. First, their logarithmic similarities were measured. Then their former features were divided by logarithmic functions to construct new features whose distribution curves were more parallel to the threshold line. Finally, an algorithm for classifying the selected modulation formats was designed whose computational complexity was then compared with that of adaptive‐threshold classification algorithm, and the recognition rate was assessed statistically. For the purposes of validation, the algorithm was tested experimentally using actual signals. The experiment confirmed that the new logarithmic features with fixed thresholds were able to maintain an efficient recognition rate of 80% when SNR was reduced from 11 to 6 dB, and suffered less computational complexity than traditional cumulants with adaptive thresholds which were achieved by support vector machine.