As a potential IoT technology for future networks, Ambient IoT (A-IoT) has entered the standardization process in Third Generation Partnership Project (3GPP). In A-IoT, Forward Error Correction (FEC)-Miller concatenated coding is emerging as a promising coding mechanism due to its advantages in link performance and time synchronization. This concatenated mechanism necessitates the use of Miller soft decoding before FEC decoding. However, it is difficult to design a unified Miller soft decoding approach in A-IoT. The reason is that, the soft decoding approach is closely associated with the receiver detection approaches, such as envelope detectors or amplitude/phase detectors. Owing to the diversity of A-IoT devices/readers, hardware complexities of different receivers vary significantly, thereby resulting in disparate signal detecting and decoding capabilities. To address this challenge, this paper considers three typical A-IoT receivers with different hardware complexities, proposes Miller soft decoding algorithms in Miller-FEC concatenated decoding for each receiver respectively, and derives closed-form expressions of Log-Likelihood Ratio (LLR). Finally, link-level simulations are conducted to validate the effectiveness of the proposed algorithms across different receivers.

Multiple-input-multiple-output (MIMO) optical wireless communication (OWC) is a promising technology capable of providing high data rates. However, interuser interference can negatively impact system performance. Existing studies employ CVX-based beamforming designs to mitigate this issue, but these approaches suffer from high computational complexity. To tackle this challenge, this article studies the low-complexity beamforming design to maximize the sum rate. Specifically, we first utilize the fractional programming (FP) method to decompose the original beamforming problem into a series of convex subproblems and propose a block coordinate descent (BCD)-based beamforming framework. Then, we provide closed-form or semi-closed-form solutions for these subproblems. For the general approximate sum rate maximization, we drive the optimal semi-closed-form beamforming structure based on the Karush-Kuhn–Tucker (KKT) conditions. Furthermore, we propose a closed-form solution for the upper bound maximization by taking advantage of the separability of optical power constraints. Finally, numerical results indicate that the proposed beamforming designs reduce the computational complexity significantly while keeping the sum rate performance.

This paper presents a new multiple-input multiple-output (MIMO) communication system. The system, termed as “pseudo MIMO”, facilitates the transmission of more parallel data streams than the number of receiving radio frequency (RF) chains. The key principle involves connecting multiple antenna elements to each receiving RF chain and employing multiple analog combining patterns for signal reception within the orthogonal frequency division multiplexing (OFDM) sampling period. Through detailing the entire transmission and signal processing procedures by matrix manipulation, the equivalent channel matrix for each OFDM subcarrier is derived in a unified matrix form, which can be further represented by the product of the analog combining matrix and the frequency domain wireless channel matrix. As per the equivalent channel matrix, an optimization problem is formulated and solved to maximize the spectral efficiency by jointly designing the digital precoding and analog combining matrices. Numerical results demonstrate that the performance of a pseudo MIMO system can closely approach that of a fully digital (FD) MIMO system with the same antenna configuration but more RF chains. It is also revealed that a low-precision discrete analog combining scheme is sufficient to achieve nearly optimal performance. Further, the effectiveness of pseudo MIMO technology is validated through prototype testing.

Abstract A novel multistatic integrated sensing and communication (ISAC) system based on cellular network is proposed. It can make use of widespread base stations (BSs) to perform cooperative sensing in wide area. This system is important since the deployment of sensing function can be achieved upon the mobile communication network at low complexity and cost without modifying the architecture of BSs for full duplexing. In this work, the topology of sensing cell is first provided, which can be duplicated to seamlessly cover the cellular network. Each sensing cell consists of a single central BS transmitting signals and multiple neighboring BSs receiving reflected signals from sensing objects. Then an estimating approach is described for obtaining position and velocity of sensing objects that locate in the sensing cell. Joint data processing with an efficient optimization method is also provided. In addition, key issues in the cellular network based multistatic ISAC system are analyzed. Simulation results show that the multistatic ISAC system can reduce interference power by over 10 dBm and significantly improve position and velocity estimation accuracy of objects when compared with the monostatic ISAC system, demonstrating the effectiveness and promise of implementing the proposed system in the mobile network.

Entering the 5G era, the mobile network operators (MNO) are facing greater challenges in providing services cost effectively than any other previous generations. The potential solutions to this are lying on the emerging trend of deep convergence of information technology (IT), communication technology (CT) and data technology (DT). In particular, the O-RAN technology, the representation of such ICDT convergence and proposed by the O-RAN ALLIANCE in 2018, is transforming Radio Access Networks towards a new paradigm featuring openness, cloudification and intelligence. O-RAN has gained huge attention from both industry and academia since its inception. In this paper, we presented the recent endeavors from China Mobile, including our deployment scenarios, various test results from open fronthaul, cloud platform to the intelligent controller. Our rich and comprehensive tests have demonstrated the viability and superiority of current O-RAN technologies. Furthermore, we also provide our deep thinking on the O-RAN future evolution in order to better serve the emerging applications such as Metaverse, cloud extended-reality (XR), extensive enterprise private 5G verticals and so on.