This paper considers a practical cell-free massive multiple-input multiple-output (CF-mMIMO) architecture within an open radio access network, where edge distributed units (EDUs) and user-centric distributed units (UCDUs) collaboratively handle physical-layer functions (e.g., channel estimation, precoding), while the open radio units (ORUs) are responsible for radio-frequency transmission and reception with the user equipment (UE). Based on large-dimensional random matrix theory, we derive a deterministic equivalent (DE) expression for the ergodic sum SE under imperfect statistical channel state information (S-CSI). Thanks to this DE-assisted result, two optimization problems: 1) sum power minimization, and 2) ergodic sum spectral efficiency (SE) maximization, are addressed through regularized parameter tuning in local partial regularized zero-forcing (LP-RZF), and power control with large-scale fading (LSF)-based EDU-ORU deployment and ORU-UE association. Numerical results validate the tightness of the DE-based ergodic sum SE expression and demonstrate the effectiveness of the LP-RZF scheme compared to the benchmark schemes. Meanwhile, the reduced computational complexity is achieved with acceptable performance loss.

We consider a cell-free massive multiple-input and multiple-output (CF-mMIMO) system, where we derive a deterministic equivalent (DE)-form of the ergodic sum spectral efficiency (SE) based on local partial regularized zero-forcing (LP-RZF) precoding with statistical channel state information (S-CSI) by leveraging large-dimensional random matrix theory. Thanks to this derivation, the previously challenging issue of precoding design based on S-CSI is now resolved, particularly in scenarios where CSI is limited to local information at each access point (AP). Moreover, as the central processing unit (CPU) now only needs to transmit an optimized regularization parameter to the APs, the computational overhead can be reduced, which naturally enhances the system scalability. Driven by these advantages, we then introduce a joint user association, power allocation, and precoding design (i.e., regularization parameter optimization) scheme aimed at maximizing the ergodic sum SE and minimizing the sum power consumption. This is achieved through two optimization problems: one for the ergodic sum SE maximization using weighted minimum mean square error (WMMSE)-based processing and another for the sum power consumption minimization employing a block coordinate descent (BCD)-based algorithm. Numerical results demonstrate the superior performance of the proposed PRO-LPRZF scheme.

Cell-free massive multiple-input multiple-output (MIMO) systems, leveraging tight cooperation among wireless access points, exhibit remarkable signal enhancement and interference suppression capabilities, demonstrating significant performance advantages over traditional cellular networks. This paper investigates the performance and deployment optimization of a user-centric scalable cell-free massive MIMO system with imperfect channel information over correlated Rayleigh fading channels. Based on the large-dimensional random matrix theory, this paper presents the deterministic equivalent of the ergodic sum rate for this system when applying the local partial minimum mean square error (LP-MMSE) precoding method, along with its derivative with respect to the channel correlation matrix. Furthermore, utilizing the derivative of the ergodic sum rate, this paper designs a successive convex approximation based deployment optimization method to improve system deployment. Simulation experiments demonstrate that under various parameter settings and large-scale antenna configurations, the deterministic equivalent of the ergodic sum rate accurately approximates the Monte Carlo ergodic sum rate of the system. Furthermore, the deployment optimization algorithm effectively enhances the ergodic sum rate of this system by optimizing the positions of access points.

Cell-free (CF) massive multiple-input multipleoutput (MIMO) system based on local operations is recognized as a viable next-generation network architecture with high performance. Leveraging large-dimensional random matrix theory, we have derived a deterministic equivalent performance expression for the performance of a CF MIMO system employing local regularized zero-forcing (L-RZF) precoding, and based on this performance expression, we have designed a high-performance regularization parameter optimization method, termed statistical channel state information aided L-RZF (SCSI-L-RZF). Under SCSI-L-RZF method, the CPU only needs to optimize the regularization parameters for each AP by utilizing statistical channel state information (CSI), while each AP only needs to design precoding by combining the regularization parameter and local CSI to achieve good performance gains. Simulation results demonstrate that our SCSI-L-RZF scheme outperforms the current mainstream maximum ratio transmission (MRT) precoding schemes, L-RZF and local team minimum mean square error (LT-MMSE), further narrowing the performance gap between local and global precoding.

To meet the quality demands of industries for future communication networks, it is crucial to explore wireless communication technologies with low latency and high reliability. In wireless networks, multi-connectivity (MC) technologies can improve performance in terms of data rate, reliability, and latency. To realize the vision of ultra-reliable and low latency communications (URLLC), the design and performance analysis of transmission schemes for short packet communication are required. In this paper, we propose MC transmission schemes based on packet splitting (SP) for the multiple-input multipleoutput (MIMO) eigenbeam-space division multiplexing (E-SDM) system architecture over quasi-static fading channels in the finite blocklength regime, accompanied by theoretical performance evaluations. Analytical expressions for the decoding error probability across distinct eigenbeams are derived to enable comparative analysis of the transmission performance. Simulation results validate the theoretical derivations, demonstrating that the flexible design of transmission mechanisms leveraging eigenbeam characteristics can further unlock the reliable performance of short packet communication.

The explosive growth of data results in more scarce spectrum resources. It is important to optimize the system performance under limited resources. In this paper, we investigate the weighted throughput (WPT) maximization for cell-free (CF) multiuser (MU) MIMO multicarrier (MC) systems through resource allocation (RA) in finite blocklength regime (FBL) while ensuring the quality of service (QoS) of each user under the constraints of total power consumption. Since the channels vary in different subcarriers and inter-user interference strengths, the WPT can be maximized by scheduling the best users in each time-frequency (TF) resource and advanced beamforming design (BF). With this motivation, we propose a joint user scheduling (US) and BF algorithm to address an mixed integer nonlinear programming (MINLP) problem. Numerical results demonstrate that the proposed RA scheme outperforms the comparison schemes. And the CF system in our scenario is capable of achieving higher spectral efficiency (SE) than the centralized antenna systems (CAS).

Satellite communication is an important technology for the coverage of open country, and plays a vital role of link channel in remote Internet of Things (IoT). However, various kinds of IoT terminals may suffer from the limited battery capacity and computing capability. Therefore, this article proposes a new multiuser IoT system design, in which the satellite link is used to provide communication service for access points (AP), and allow terminals to download and upload data through APs. Then, the AP will exploit simultaneous wireless information and power transfer (SWIPT) and mobile edge computing (MEC) technologies to alleviate the deficiencies mentioned above. Moreover, the AP will equip with the full duplex (FD) and multi-input multi-output (MIMO) technologies to further improve the spectrum efficiency. Besides, a hybrid energy storage is assumed in the AP, i.e., the energy may come from power grid or renewable energy. Taking into account all issues above, we optimize the uplink achievable rate through jointly optimizing the CPU frequency, computation tasks, terminal transmitting power and the ratio of MEC task. In order to solve this optimization problem, we first decouple it into two sub-problems. For the first one, we can obtain the closed-form solution of CPU frequency. For the second one, we continue to decompose it into two non-convex problems, and then the iterative active-set method is used to solve these two problems. Numerical simulations demonstrate the effectiveness of the proposed design.