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Abstract In this paper, we investigate the uplink transmission for user-centric cell-free massive multiple-input multiple-output (MIMO) systems. The largest-large-scale-fading-based access point (AP) selection method is adopted to achieve a user-centric operation. Under this user-centric framework, we propose a novel inter-cluster interference-based (IC-IB) pilot assignment scheme to alleviate pilot contamination. Considering the local characteristics of channel estimates and statistics, we propose a location-aided distributed uplink combining scheme to balance the relationship among the spectral efficiency (SE), user equipment (UE) fairness and complexity, in which local partial minimum mean-squared error (LP-MMSE) combining is adopted for some APs, while maximum-ratio (MR) combining is adopted for the remaining APs. A corresponding AP selection scheme based on a novel proposed metric representing inter-user interference is proposed. We also propose a new fairness coefficient taking SE performance into account to indicate the UE fairness. Moreover, the performance of the proposed scheme is investigated under fractional power control and max–min fairness (MMF) power control. Simulation results demonstrate that the channel estimation accuracy of our proposed IC-IB pilot assignment scheme outperforms that of the conventional pilot assignment schemes. It is also shown that compared with the benchmark LP-MMSE combining, the proposed location-aided combining trades 13.45 $$\%$$ % average SE loss for 26.61 $$\%$$ % UE fairness improvement and 28.58 $$\%$$ % complexity reduction when $$\gamma = 0.6$$ γ = 0.6 . And by adjusting the threshold $$\gamma$$ γ , a good trade-off between the average SE, UE fairness and computational complexity can be provided by the proposed scheme. Furthermore, the proposed scheme with fractional power control can better demonstrate the advantages of trade-off performance than MMF power control and full power transmission.

In smart city, traffic congestion and parking problems will be solved assisted by precise vehicle location. To address the vehicle localization problem in smart city, a novel extrinsic information aided fingerprint localization algorithm is proposed in this paper. Firstly, on the basis of massive multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM), the angle-delay domain channel matrix (ADDCM) is extracted. Then, an amplitude ratio based non-line of sight (NLoS) identification method is provided to estimate link states. For the case of LoS existence, in order to save storage space, a tuple fingerprint is proposed to record angle of LoS path. In other case, when all APs service in NLoS scenarios, to improve the localization robustness in dynamic environments, the correlated ADDCM (CADDCM) is taken as location fingerprint which can extract the constant information related to fixed scattering clusters. A greedy-based fingerprint matching scheme is used to search the nearest reference point (RP). Furthermore, the extrinsic information provided by neighbor vehicles, such as estimated location and measured distance, is utilized to improve the localization stability. Simulation results show that the extrinsic information aided algorithm could improves the localization accuracy and robustness in dynamic environments.

As a promising candidate for the future generations of mobile communication networks, the cell-free (CF) massive multiple-input multiple-output (mMIMO) networks have been shown to provide high spectral efficiency (SE) and more uniform signal coverage than cellular mMIMO networks, enabling smart cities more diverse application services. The fronthaul link in such networks, defined as the transmission link between the central processing unit and the access point, requires a high capacity, but is often constrained. Hence, the optimization of the user rate under the limited capacity of fronthaul is the key problem to be solved in a CF networks. In this paper, we consider the downlink transmission of CF mMIMO orthogonal frequency division multiplexing (OFDM) systems with constrained transmit power and fronthaul capacity. Based on the min-max and sum-max principles, quadratic transform-min max (QT-MM) and quadratic transform-sum max (QT-SM) optimization algorithms are proposed, respectively. Simulation results show that compared with the weighted minimum mean square error (WMMSE) algorithm, both of the proposed algorithms can effectively prevent very low user rates when the fronthaul capacity is limited. When the fronthaul link capacity constraint is high, the QT-SM algorithm has better rate performance than the distributed WMMSE-d algorithm and the centralized WMMSE-c algorithm.

Orthogonal time–frequency space (OTFS) modulation has been advocated as a promising waveform for achieving integrated sensing and communication (ISAC) due to its superiority in high-mobility adaptability and spectral efficiency. In OTFS modulation-based ISAC systems, accurate channel acquisition is critical for both communication reception and sensing parameter estimation. However, the existence of the fractional Doppler frequency shift spreads the effective channels of the OTFS signal significantly, making efficient channel acquisition very challenging. In this paper, we first derive the sparse structure of the channel in the delay Doppler (DD) domain according to the input and output relationship of OTFS signals. On this basis, a new structured Bayesian learning approach is proposed for accurate channel estimation, which includes a novel structured prior model for the delay-Doppler channel and a successive majorization–minimization (SMM) algorithm for efficient posterior channel estimate computation. Simulation results show that the proposed approach significantly outperforms the reference schemes, especially in the low signal-to-noise ratio (SNR) region.

The intelligent reflecting surface (IRS) is considered a core technology of next-generation mobile communication. It has significant advantages in enhancing network coverage, spectrum efficiency, energy efficiency, and deployment cost. Compared with the conventional massive multiple-input-multiple-output (MIMO) system, cell-free massive MIMO overcomes the limitation imposed by inter-cell interference in traditional cellular mobile networks and realizes coherent transmission centered on users. In this article, we consider a new application scenario for the IRS - an aerial IRS (AIRS)-aided cell-free massive MIMO system where multiple APs serve several users through an AIRS. The users are in a “shadow area” where we cannot provide good quality of service (QoS) due to the remote location and the shelter of tall buildings. Our goal is to optimize the power allocation and beamforming of each AP, the placement and reflection phase shift parameters of the AIRS to maximize the user's achievable rate. Firstly, we consider the optimization for fixed placement of the AIRS, where we propose a joint optimization strategy to maximize the achievable rate of the user. Then, we propose a fast optimal location search algorithm base on the path loss model to determine the optimal location of the AIRS and decrease the computational complexity. The simulation results show that the proposed methods can improve the performance for the achievable rate of the system. To the best of our knowledge, this article is the first to study the application scenario for the combination of IRS technology and a cell-free massive MIMO system.

Cell-free massive multiple-input multiple-output (mMIMO) is regarded as a promising technology in the future wireless communication. This paper aims to minimize the downlink power consumption of cell-free mMIMO system under the downlink rate constraints of users and the power constraints of per-antenna over spatially correlated Rayleigh fading channels. Firstly, this paper studies the performance of two different downlink transmission modes: non-coherent joint transmission and coherent joint transmission. Then, for the two transmission modes, the corresponding downlink power optimization model is established and an efficient power optimization algorithm is proposed based on the Lagrange multiplier (LM) method. Under the power optimization model, this paper analyzes the impact of different factors on the downlink power consumption of cell-free mMIMO system. Simulation results show that coherent joint transmission performs better than non-coherent joint transmission on spectral efficiency (SE) and energy efficiency (EE). In addition, under the same power control strategy, the downlink power consumption of cell-free mMIMO is much lower than co-located mMIMO. Moreover, the total transmission power decreases when more antennas are utilized at each access point (AP) or spatial correlation of channels becomes weak.

Abstract Massive multiple-input multiple-output (MIMO) is a powerful supporting technology to meet the energy/spectral efficiency and reliability requirement of Internet of Things (IoT) network. However, the gain of massive MIMO relies on the availability of channel state information (CSI). In this paper, we investigate the channel estimation problem for frequency division duplex (FDD) massive MIMO system. By analyzing the sparse property of the downlink massive MIMO channel in the angular domain, a structured prior-based sparse Bayesian learning (SP-SBL) approach is proposed to estimate the downlink channels between base station (BS) and users reliably. The scheme can be implemented without the knowledge of channel statistics and angular information of users. The simulation results show that the proposed scheme outperforms the reference schemes significantly in terms of normalized mean square error (NMSE) for a variety of scenarios with different lengths of pilot sequence, transmit signal-to-noise ratios (SNRs), and angular spreads.

Although the hybrid analog–digital processing reduces the hardware complexity to realize massive multiple-input multiple-output system, it increases the difficulty of channel estimation significantly since the base station cannot obtain enough training samples with the limited number of radio frequency chains. This paper aims to address this problem using the emerging machine-learning techniques. By exploiting the sparsity property of the angular domain channel, a novel clustered sparse Bayesian learning approach is proposed to estimate the channel reliably. The scheme does not require the knowledge of channel statistics and angular information of users. A data-aided channel estimation scheme based on variational optimization is proposed to further improve the estimation performance by using the training data and unknown information data jointly. The numerical simulations show that the proposed schemes outperform the reference schemes significantly in terms of the normalized mean square error and the achievable sum rate.

Abstract In this paper, we consider the beam-domain hybrid time-switching (TS) and power-splitting (PS) simultaneous wireless information and power transfer (SWIPT) protocol design in full-duplex (FD) massive multiple-input multiple-output (MIMO) system, where the FD base station (BS) simultaneously serves a set of downlink half-duplex (HD) users (cellular users) and a set of fixed uplink HD users (fixed sensor nodes) which are uniformly distributed in its coverage area. In order to reduce the computational complexity, we investigate the beam-domain representation of massive MIMO channels based on the basis expansion model, and then the beam-domain SWIPT protocol which lies in intelligently scheduling the users and sensors based on the beam-domain distributions of their associated channels to mitigate SI and enhance transmission efficiency is designed for full-duplex massive MIMO system. The whole beam-domain hybrid TS and PS SWIPT protocol is divided into two phases based on the ideal of TS. The first phase is used for cellular users uplink training and sensor nodes energy harvesting as well as downlink training, wherein the cellular users transmit uplink pilots for uplink channel estimation at the BS, while the BS simultaneously transmits energy signals to the sensor nodes. Based on the idea of PS, the sensor nodes utilize the received energy signals for energy harvesting and downlink channel estimation. In the second phase, the BS forms the transmit beamformers for information transmission to the users as well as the receive beamformers for the sensors transmit their data to the BS simultaneously. By optimizing the TS ratio and transmit powers at the BS in two phases, the system achievable sum rate performance is maximized. Simulation results show the superiority of the proposed protocol on spectral efficiency compared with the existing massive MIMO SWIPT protocol.

In this paper, we propose a hybrid one-way full-duplex (OWFD)/two-way half-duplex (TWHD) relaying scheme to improve the performance of the relay system with asymmetric channel gains and traffic requirements. In the proposed scheme, the transmit time interval is decomposed to three subintervals, and in each subinterval the relay operates in OWFD or TWHD mode. The duration of each subinterval is optimized to minimize the system outage probability. We show that the subinterval time optimization can be formulated as a linear programming problem. By exploiting the structure of the problem, the closed-form solution of the problem was derived. We also present a power control scheme to mitigate the effect of residual self-interference (RSI) when the relay is operated in the OWFD mode. Simulation results show that the hybrid scheme achieves better outage performance under the asymmetric environment when compared with the traditional schemes. Moreover, the scheme is robust to the RSI caused by the full-duplex operation.

This paper investigates the 3-D massive multiple-input multiple-output (MIMO) for air-to-ground transmission, where an air platform (AP) is equipped with a 2-D rectangular antenna array and communicates with a number of user equipments (UEs) on the ground. By exploiting the slow timevarying parameters, such as channel correlation and angles of departure (AoDs) of UEs, we first propose a location-assisted two-layer precoding scheme for downlink transmission. The first-layer precoding aims to decompose the original massive MIMO system into several low-dimension MIMO systems, with each operating on the orthogonal subspace. Through proper UE clustering, we show that the first-layer precoding matrix can be approximated using a constant-envelope matrix, which results in significant reduction on hardware complexity of AP. The second-layer precoding is designed to eliminate the multi-UE interference within each low-dimension MIMO system. Since the AoD information is usually not perfectly known at AP, we then investigate the effect of AoD uncertainty on the performance of the proposed precoding scheme. In particular, we propose a new analytic method to fast estimate approximately the power loss due to AoD error. Numerical simulations are presented to evaluate the performance of location-assisted precoding under different system parameters, including Rician factor, altitude of AP, and AoD uncertainty. The results show that the location-assisted precoding outperforms match filter precoding and basis expansion-based precoding in the air-to-ground transmission scenarios significantly.

This paper investigates a multi-pair two-way massive MIMO amplify-and-forward full-duplex relay (FDR) system over Ricean fading channels, where multiple pairs of full-duplex users exchange information within pair through a full-duplex relay with a very large number of antennas (M for transmission and M for reception). First, the zero-forcing reception/zero-forcing transmission and maximum-ratio combining/maximum-ratio transmission processing matrices with imperfect channel state information at the relay are presented. Then, the corresponding asymptotic expressions (in M) of the end-to-end signal-to-interference-plus-noise ratio are derived. Finally, the asymptotic spectral efficiency (SE) and energy efficiency (EE) at the general power scaling schemes when the number of the relay antennas tends to infinity is analyzed. Theoretical analyses and simulation results show that, when M → ∞, the effects of channel estimation error, the self-loop of each user as well as the relay, inter-user interferences, the noise at users and the relay can be eliminated, respectively, if the power scaling scheme is properly selected. Besides, the considered multi-pair two-way FDR outperforms the multi-pair two-way half-duplex relaying on SE and EE performances when M is large. Moreover, a low complexity power control scheme is proposed to optimize the SE and we analyze the impact of the number of user pairs on the SE.

Electroencephalography (EEG) or Magnetoencephalography (MEG) source imaging aims to estimate the underlying activated brain sources to explain the observed EEG/MEG recordings. Solving the inverse problem of EEG/MEG Source Imaging (ESI) is challenging due to its ill-posed nature. To achieve a unique solution, it is essential to apply sophisticated regularization constraints to restrict the solution space. Traditionally, the design of regularization terms is based on assumptions about the spatiotemporal structure of the underlying source dynamics. In this paper, we propose a novel paradigm for ESI via an Explainable Deep Learning framework, termed as XDL-ESI, which connects the iterative optimization algorithm with deep learning architecture by unfolding the iterative updates with neural network modules. The proposed framework has the advantages of (1) establishing a data-driven approach to model the source solution structure instead of using hand-crafted regularization terms; (2) improving the robustness of source solutions by introducing a topological loss that leverages the geometric spatial information applying varying penalties on distinct localization errors; (3) improving the reconstruction efficiency and interpretability as it inherits the advantages from both the iterative optimization algorithms (interpretability) and deep learning approaches (function approximation). The proposed XDL-ESI framework provides an efficient, accurate, and interpretable paradigm to solve the ESI inverse problem with satisfactory performance in both simulated data and real clinical data. Specially, this approach is further validated using simultaneous EEG and intracranial EEG (iEEG).

Abstract Despite orientationally variant tears of the meniscus, suture repair is the current clinical gold treatment. However, inaccessible tears in company with re-tears susceptibility remain unresolved. To extend meniscal repair tools from the perspective of adhesion and regeneration, we design a dual functional biologic-released bioadhesive (S-PIL10) comprised of methacrylated silk fibroin crosslinked with phenylboronic acid-ionic liquid loading with growth factor TGF-β1, which integrates chemo-mechanical restoration with inner meniscal regeneration. Supramolecular interactions of β-sheets and hydrogen bonds richened by phenylboronic acid-ionic liquid (PIL) result in enhanced wet adhesion, swelling resistance, and anti-fatigue capabilities, compared to neat silk fibroin gel. Besides, elimination of reactive oxygen species (ROS) by S-PIL10 further fortifies localized meniscus tear repair by affecting inflammatory microenvironment with dynamic borate ester bonds, and S-PIL10 continuously releases TGF-β1 for cell recruitment and bridging of defect edge. In vivo rabbit models functionally evidence the seamless and dense reconstruction of torn meniscus, verifying that the concept of meniscus adhesive is feasible and providing a promising revolutionary strategy for preclinical research to repair meniscus tears.