Abstracts:In this paper, we consider the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted THz communications with three-side beam split. Except for the beam split at the base station (BS), we analyze the double-side beam split at the STAR-RIS for the first time. To relieve the double-side beam split effect, we first propose a time delayer (TD)-based fully-connected structure at the STAR-RIS. As a further advance, a low-hardware complexity and low-power consumption sub-connected structure is developed, where multiple STAR-RIS elements share one TD. Meanwhile, considering the practical scenario, we investigate a multi-STAR-RIS and multi-user communication system, and sum rate maximization problem is formulated by jointly optimizing the hybrid analog/digital beamforming, time delays at the BS as well as the double-layer phase shift coefficients, time delays and amplitude coefficients at the STAR-RISs. Based on this, we first allocate users for each STAR-RIS, and then derive the analog beamforming, time delays at the BS, and the double-layer phase shift coefficients, time delays at each STAR-RIS. Next, we develop an alternative optimization algorithm to calculate the digital beamforming at the BS and amplitude coefficients at the STAR-RISs. Finally, the numerical results verify the effectiveness of the proposed schemes.

Abstracts:Carrier frequency offset (CFO) and channel equalization are two critical problems for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) wireless communication systems in ultra-reliable and low latency communication (URLLC). In this paper, we propose a semi-blind precoding aided structure that includes two CFO estimation approaches and an independent component analysis (ICA) based equalization scheme for MIMO OFDM systems in URLLC, requiring no pilots. We design a non-redundant balanced precoding strategy, killing two birds with one stone, where reference signals are superimposed into source signals to simultaneously allow CFO estimation and ambiguity elimination in the ICA-equalized signals. The proposed precoding-aided CFO estimation approach performs by maximizing a cost function formulated via the cross-correlations between the reference signal and the received signal. We further propose a low-complexity closed-form CFO estimation approach, by transforming the formulated cost function into a new expression. To maximize bit error rate (BER) performance, particle swarm optimization (PSO) is employed to perform the joint optimization of precoding constant and the number of OFDM blocks for CFO estimation, while avoiding exhaustive search. The proposed semi-blind precoding-aided structure provides a trade-off between performance, complexity and spectral efficiency for MIMO OFDM systems in URLLC.

Abstracts:This paper considers an integrated sensing and communication (ISAC) system with monostatic radar functionality using a zero-padding orthogonal frequency division multiplexing (ZP-OFDM) downlink transmission. We focus on ISAC’s sensing aspect, employing an energy-detection (ED) method. The ZP-OFDM transmission is motivated by the fact that sensing can be performed during the silent periods of the transmitter, thereby avoiding self-interference (SI) cancellation processing of the in-band full duplex operation, which is needed for the cyclic prefix (CP)-OFDM. Additionally, we also show that ZP-OFDM can reject nearby clutter interference. We derive the probability of detection (PD) for the ZP and CP-OFDM systems, allowing useful performance analyses. In particular, we show that the PD expressions lead to an upper bound for the ZP-OFDM transmission, which is useful for selecting the best ZP size for a given system configuration. We also provide an expression that allows range comparison between ZP and CP-OFDM, where we consider a general case of imperfect SI cancellation for the CP-OFDM system. The results show that when the ZP size is 25% of the fast Fourier transform size, the range loss of the ZP system range is only 17% larger than the CP transmission.

Abstracts:This research paper delves into leveraging Machine Learning (ML) for precise localization in GPS-challenged environments like urban canyons, addressing the complexities of time-varying signal propagation types, where transient obstructions, such as vehicles, can modify the channel state information (CSI) over time. It presents a novel approach termed semantic localization, which recognizes signal propagation conditions as semantic elements, incorporating them into the localization framework to enhance both accuracy and resilience. To tackle the issue of diverse CSIs at each location and the extensive need for labeled data, the paper proposes a multi-task deep domain adaptation (DA) strategy. This approach trains neural networks using a limited set of labeled data complemented by a vast array of unlabeled samples, coupled with innovative scenario adaptive learning techniques for optimal representation learning and knowledge transfer. Employing Bayesian theory for the efficient management of task importance weights minimizes the necessity for laborious parameter tuning. By making certain assumptions, the study introduces a deep homoscedastic DA method for enhanced joint task efficacy. Through detailed simulations using a 3D ray tracing dataset, the paper evidences that the integration of environmental semantics and the advanced DA localization techniques markedly elevates the precision of localization in various demanding settings.

Abstracts:The increasing demands for spectral resources in emerging massive machine-type communication applications necessitate the implementation of massive grant-free transmission in the millimeter-wave (mmWave) band. This paper proposes two efficient receive analog beamforming design algorithms for mmWave massive grant-free transmission under hybrid beamforming architectures, intending to optimize spectral efficiency and access probability, respectively. Specifically, we first express the spectral efficiency of mmWave massive grant-free transmission systems and then derive an analytically tractable approximation using the random matrix theory. Following this, an alternating optimization method is employed to design the receive beamforming matrix efficiently. Additionally, we provide the formulation of access probability for mmWave massive grant-free transmission, whose explicit expression is approximately derived through the Gaussian approximation. Building upon this, we utilize a convex hull relaxation-based optimization method to optimize the beamforming matrix. The effectiveness of our proposed beamforming design algorithms in improving spectral efficiency and access probability is validated through extensive simulation experiments.

Abstracts:In the context of fully distributed systems, we consider a scenario densely populated with wireless nodes which are equipped with simultaneous wireless information and power transfer (SWIPT) capabilities and can act as relays between a source and a destination. In this scenario, the probability of finding a node able to provide a useful contribution to the quality of the link tends to be high and can be exploited adopting a suitable relay selection scheme. Assuming nodes distributed following a Poisson point process and communicating only with source and destination to ensure the scalability of the system, we consider two well-known relay selection schemes, which are typically used as performance benchmarks, and derive an analytical framework to investigate the performance of the source-destination link in terms of outage probability. In our analysis we include, through the use of a simple but effective model, the non-linear effects of the energy harvesting circuitry of the devices and introduce a simple signaling mechanism between source, SWIPT nodes and destination to identify the most suitable relay among the various SWIPT devices. The impact of multiple access during the phase of relay selection is also modeled and investigated.

Abstracts:Cell-free massive multiple-input multiple-output (CF mMIMO) network has gained significant attention due to its ability to provide a ubiquitous connectivity over a wide geographical area without having any cell boundary. It serves the user equipments (UEs) by employing multiple access points (APs) connected to a central processing unit (CPU). In this paper, we focus on designing beam combiners for the APs under limited availability of the pilots. We argue that the received signal-to-interference plus noise (SINR) can be maximized if received signals at each AP are beam combined in a way that it aligns with the desired signal and nullifies the interference arising due to the pilot reuse. We use this strategy to improve three popular beam combiners for the CF mMIMO network based on the maximum ratio (MR), generalized MR (GMR), and reduced-complexity minimum mean square error (RCMMSE) criteria, respectively. The proposed distributed combiners handle the issue of pilot contamination and reduce the amount of signal exchanges between APs and CPU. We have analytically shown that the proposed combiners improve the spectral efficiency of their respective counterparts without increasing their overall complexities. We have corroborated this using simulations for both uncorrelated and correlated channels.

Abstracts:Reconfigurable Intelligent Surfaces (RIS) are one of the emerging technologies aimed at meeting the expectations of next-generations wireless systems. In this field, the use of multi-port network models for the characterization and optimization of RIS has emerged in recent years. These models take into account aspects traditionally not considered in communication theory, such as mutual coupling of RIS elements and the presence of structural scattering. In this work, we refer to this model and focus on the problem of maximizing the average achievable rate in a multi-user uplink scenario by leveraging statistical Channel State Information (CSI). This approach significantly reduces the computational burden and communication overhead in CSI estimation compared to schemes requiring instantaneous CSI estimation. These benefits are achieved with performance that, in many cases, is reasonably close to that of the perfect CSI scenario. This is one of the outcomes achievable with the proposed optimization scheme. Moreover, it is shown how in multi-user scenarios, namely in the presence of interference, the use of inadequate models to characterize RIS can lead to very poor performances. For example, models that do not consider structural scattering may fail to account for interference caused by RIS.

Abstracts:In traditional multiple-input multiple-output (MIMO) communication systems, the antenna spacing is often no smaller than half a wavelength. However, by exploiting the coupling between more closely-spaced antennas, a superdirective array may achieve a much higher beamforming gain than traditional MIMO. In this paper, we present a novel utilization of neural networks in the context of superdirective arrays. Specifically, a new model called MultiTransUNet-GAN is proposed, which aims to forecast the excitation coefficients to achieve “superdirectivity” or “super-gain” in the compact uniform linear or planar antenna arrays. In this model, we integrate a multi-level guided attention and a multi-scale skip connection. Furthermore, generative adversarial networks are integrated into our model. To improve the prediction accuracy and convergence speed of our model, we introduce the warm up aided cosine learning rate (LR) schedule during the model training, and the objective function is improved by incorporating the normalized mean squared error (NMSE) between the generated value and the actual value. Simulations demonstrate that the array directivity and array gain achieved by our model exhibit a strong agreement with the theoretical values. Overall, it shows the advantage of enhanced precision over the existing models, and a reduced requirement for measurement and the computation of the excitation coefficients.

Abstracts:A reconfigurable intelligent surface (RIS) assisted semi-grant-free (SGF) non-orthogonal multiple access (NOMA) system is investigated. Unlike existing works that only focus on short-term resource allocation, we study a long-term power-saving optimization problem under queue stability constraints and utilize Lyapunov stability theory to deal with delay-aware resource allocation. We first transform the long-term problem into a series of per-time-slot problems by exploiting the Lyapunov theory. Then, the objective function is minimized by alternatingly optimizing the power allocation, channel assignment, and RIS reflection coefficients. In particular, the channel assignment subproblem is solved by invoking a many-to-one matching algorithm. The power allocation sub-problem is addressed by the developed fractional programming algorithm. The reflection coefficients design sub-problem is solved by a penalty-based method, which tackles the rank one constraint and optimizes reflection coefficients. The numerical results validate the effectiveness and show that it can achieve queue stability by setting the Lyapunov parameters. It also shows that the proposed RIS-assisted SGF NOMA system outperforms without RIS and random RIS phase-shift baselines.