A cell-free massive multiple-input multiple-output (MIMO) network-assisted full-duplex (NAFD) system has been proposed to satisfy the exploding demand of higher data transmission speed and more efficient communication. However, a growing number of users in a cell-free system inevitably leads to pilot contamination. In this paper, we analyze the ergodic spectral efficiency of cell-free massive MIMO NAFD system in the presence of pilot contamination. The cell-free massive MIMO NAFD system model has been investigated and both uplink and downlink channel state information (CSI) is estimated under spatially correlated channels. Under pilot contamination, the closed-form sum-rate expressions of the uplink with maximum ratio combination (MRC) receiver while downlink with maximum ratio transmission (MRT), and uplink with zero-forcing (ZF) receiver while downlink with ZF precoding schemes are derived based on large-scale random matrix theory. Numerical results show that under several environmental settings, the theoretical results match well with the simulated results and cell-free massive MIMO NAFD system has a better performance than time-division duplex (TDD) system. Moreover, simulation results show that the achievable sum-rate of using ZF/ZF could be more spectrally efficient compared to MRC/MRT because of its interference suppression capability.

The Global Navigation Satellite System (GNSS) traditional dual‐frequency cycle slip detection and repair methods such as the TurboEdit algorithm are restricted by the availability and accuracy of pseudorange observations. Doppler observations represent the instantaneous variation of the carrier phase during the integration time, which are immune to cycle slips. They are less affected by multipath effects compared with the pseudorange observations. Hence, a real‐time cycle slip detection and repair approach for the BeiDou Navigation Satellite System (BDS) dual‐frequency signals is proposed by combining the carrier phase and Doppler observations. The proposed method adopts the phase‐Doppler combination and the geometry‐free combination to detect cycle slips, and uses the simple rounding method or the dual‐frequency least‐square ambiguity decorrelation adjustment (LAMBDA) method to repair cycle slips. The validity of the proposed method is verified by the static and kinematic observation data under 1 s sampling interval and steady ionosphere. The results show that the new method can successfully detect all the small, insensitive, and large cycle slips. Moreover, the fixing rates of the simple rounding method under static and kinematic conditions are above 94% and 83%, respectively, while those of the dual‐frequency LAMBDA method are both 100%.

In a massive machine-type communication (mMTC) scenario, there are large number of devices that may establish links to receivers, which causes a great burden to the system for signaling overhead, thereby reducing the communication efficiency. One potential approach involves refraining from assigning signaling overhead to users, with active users opportunistically transmitting data messages within time slots. The receiver only needs to recover these independent data messages without identifying the source of these messages, named unsourced random access (URA). In this paper, suitable URA schemes are proposed for cellular massive multiple-input multiple-output (MIMO) and cell-free massive MIMO systems. In the cellular scenario, the system completes the transmission and estimation of the number of active users and the corresponding channel large-scale fading coefficients (LSFCs) in stage one. Utilizing the channel information obtained in stage one, the receiver in stage two applies the sparse-correction-based bilinear generalized approximate message passing (SCB-BiG-AMP) algorithm proposed in this paper to restore the original data sequences sent by active users. In a cell-free scenario, active users won't transmit LSFC information similar to that in stage one of cellular scenario for the properties of massive distributed antennas. Instead, the central limit theorem (CLT) is used to estimate the average channel LSFC of all active users, which is substituted into the SCB-BiG-AMP algorithm as the equivalent channel variance. Then, the original data sequences sent by active users can be restored. According to the simulation results, the proposed URA communication scheme can achieve good bit error performance. Moreover, as a result of the small uplink user-AP distance, the system performance in the cell-free scenario is much better than that in the cellular scenario.

The demand for higher transmission efficiency and denser user access has been put forth by the next generation of wireless communication systems. To cater to the future communication development, this article focuses on massive random access schemes under the user-centric cell-free massive multiple-input-multiple-output (MIMO) architecture. For uplink transmission, a data frame structure is designed to enable active user detection (AUD), channel estimation (CE), and data transmission. The association between access points (APs) and user equipment (UEs) is presented to facilitate an user-centric cell-free scalable architecture. In this article, a maximum likelihood (ML)-based method is proposed for AUD to obtain the set of active UEs. By setting appropriate thresholds and combining the UE-AP association, accurate active detection results can be obtained. CE can be accomplished with lower computational complexity by utilizing the detected active UE set in AUD module. Specifically, the generalized approximate message passing-based sparse Bayesian learning with Dirichlet process (GAMP-DP-SBL) is adopted as the CE algorithm, leveraging the spatial aggregation and dispersion characteristics of APs to enhance the estimation accuracy. Building upon GAMP-DP-SBL algorithm, a clustered algorithm (GAMP-CDP-SBL) is proposed to reduce the scale of the sensing matrix and improve the accuracy of CE for associated active UEs. Moreover, to enhance system scalability, decentralized AUD and CE algorithms are proposed in this article. Simulation results under various parameter settings and different scenarios exhibit the superior performance of the proposed scheme.

In the research of next-generation wireless communication technologies, orthogonal time frequency space (OTFS) modulation is emerging as a promising technique for high-speed mobile environments due to its superior efficiency and robustness in doubly-selective channels. Additionally, the cell-free architecture, which eliminates the issues associated with cell boundaries, offers broader coverage for radio access networks. Integrating cell-free architecture with OTFS modulation enables support for massive random access in high-mobility machine-type communication scenarios. This paper explores a massive random access scheme based on OTFS modulation within a cell-free architecture. A transceiver model for uplink OTFS signals involving multiple access points (APs) is developed, where channel estimation with fractional channel parameters is approximated as a two-dimensional block sparse matrix recovery problem. Building on existing superimposed and embedded preamble schemes, a hybrid preamble strategy intended for massive random access is proposed. This scheme leverages superimposed and embedded preambles to respectively achieve rough and accurate active user equipment (UE) detection (AUD), as well as precise channel estimation. Moreover, this study introduces a generalized approximate message passing and pattern-coupled sparse Bayesian learning with Laplacian prior (GAMP-PCSBL-La) algorithm, which effectively captures block sparse features after discrete cosine transform (DCT), delivering precise estimation results with reduced computational complexity. Simulation results demonstrate that the proposed scheme is effective and provides superior performance compared to other existing schemes.

Time-frequency doubly-selective characteristics are often exhibited by wireless transmission channels, due to hardware imperfections and the relative mobility of devices. This paper investigates a massive unsourced random access (URA) scheme for the millimeter-wave (mmWave) band in the presence of a doubly-selective channel. By utilizing the sparse path and beam aggregation properties of mmWave communications, the equivalent channel can be modeled as a multi-dimensional block-sparse matrix. This modeling is achieved in the code-time-frequency (CTF)-angle domain through segment-based compressed coding. The joint channel estimation and segment data recovery is then achieved using a pattern-coupled sparse Bayesian learning (PCSBL) with generalized approximate message passing (GAMP) algorithm. A clustering method based on similar channel features is proposed to concatenate different data segments. Numerical simulation results validate the effectiveness of the proposed scheme, demonstrating a 6 dB increase in energy efficiency compared to tree-based coded compressed sensing (CCS) scheme.