This paper investigates the transmission scheme of the orthogonal time frequency space (OTFS) system with multiple antennas. Previous works have studied the multi-antenna transmitter diversity scheme with the ideal pulse shaping in OTFS systems assuming the wireless channels of two consecutive frames duration are invariant. However, the ideal pulse shaping can not be realized in practice and the channel varies rapidly in two consecutive frames in the high-speed mobility scenarios. To this end, we redesign the multi-antenna transmitter diversity scheme to ensure its practicability in the rectangular-pulse-based OTFS systems. At first, the information symbols of each antenna are divided into two half frames along the Doppler domain. Then, the guard symbols are carefully arranged to eliminate the interference between two half frames, and to ensure the equivalent channels of two half frames being identical. By considering the positions of the guard symbols, the transmission codeword is designed and a low complexity linear detection is proposed. Finally, simulation results validate that the bit error rate performance of the proposed scheme has the superiority over the existing works.

In order to provide ultra low-latency and high energy-efficient communication for intelligences, the sixth generation (6G) wireless communication networks need to break out of the dilemma of the depleting gain of the separated optimization paradigm. In this context, this paper provides a comprehensive tutorial that overview how joint source-channel coding (JSCC) can be employed for improving overall system performance. For the purpose, we first introduce the communication requirements and performance metrics for 6G. Then, we provide an overview of the source-channel separation theorem and why it may not hold in practical applications. In addition, we focus on two new JSCC schemes called the double low-density parity-check (LDPC) codes and the double polar codes, respectively, giving their detailed coding and decoding processes and corresponding performance simulations. In a nutshell, this paper constitutes a tutorial on the JSCC scheme tailored to the needs of future 6G communications.

Massive multiple-input multiple-output (MIMO) technology enables higher data rate transmission in the future mobile communications. However, exploiting a large number of antenna elements at base station (BS) makes effective implementation of massive MIMO challenging, due to the size and weight limits of the masssive MIMO that are located on each BS. Therefore, in order to miniaturize the massive MIMO, it is crucial to reduce the number of antenna elements via effective methods such as sparse array synthesis. In this paper, a multiple-pattern synthesis is considered towards convex optimization (CO). The joint convex optimization (JCO) based synthesis is proposed to construct a codebook for beamforming. Then, a criterion containing multiple constraints is developed, in which the sparse array is required to fullfill all constraints. Finally, extensive evaluations are performed under realistic simulation settings. The results show that with the same number of antenna elements, sparse array using the proposed JCO-based synthesis outperforms not only the uniform array, but also the sparse array with the existing CO-based synthesis method. Furthermore, with a half of the number of antenna elements that on the uniform array, the performance of the JCO-based sparse array approaches to that of the uniform array.

Unsourced multiple access (UMA) is a multi-access technology for massive, low-power, uncoordinated, and unsourced Machine Type Communication (MTC) networks. It ensures transmission reliability under the premise of high energy efficiency. Based on the analysis of the 6G MTC key performance indicators (KPIs) and scenario characteristics, this paper summarizes its requirements for radio access networks. Following this, the existing multiple access models are analyzed under these standards to determine UMA's advantages for 6G MTC according to its design characteristics. The critical technology of UMA is the design of its multiple-access coding scheme. Therefore, the existing UMA coding schemes from different coding paradigms are further summarized and compared. In particular, this paper comprehensively considers the energy efficiency and computational complexity of these schemes, studies the changes of the above two indexes with the increase of access scale, and considers the trade-off between the two. It is revealed by the above analysis that some guiding rules of UMA coding design. Finally, the open problems and potentials in this field are given for future research.

As Information, Communications, and Data Technology (ICDT) are deeply integrated, the research of 6G gradually rises. Meanwhile, federated learning (FL) as a distributed artificial intelligence (AI) framework is generally believed to be the most promising solution to achieve “Native AI” in 6G. While the adoption of energy as a metric in AI and wireless networks is emerging, most studies still focused on obtaining high levels of accuracy, with little consideration on new features of future networks and their possible impact on energy consumption. To address this issue, this article focuses on green concerns in FL over 6G. We first analyze and summarize major energy consumption challenges caused by technical characteristics of FL and the dynamical heterogeneity of 6G networks, and model the energy consumption in FL over 6G from aspects of computation and communication. We classify and summarize the basic ways to reduce energy, and present several feasible green designs for FL-based 6G network architecture from three perspectives. According to the simulation results, we provide a useful guideline to researchers that different schemes should be used to achieve the minimum energy consumption at a reasonable cost of learning accuracy for different network scenarios and service requirements in FL-based 6G network.

In 5G networks, optimization of antenna beam weights of base stations has become the key application of AI for network optimization. For 6G, higher frequency bands and much denser cells are expected, and the importance of automatic and accurate beamforming assisted by AI will become more prominent. In existing network, servers are “patched” to network equipment to act as a centralized brain for model training and inference leading to high transmission overhead, large inference latency and potential risks of data security. Decentralized architectures have been proposed to achieve flexible parameter configuration and fast local response, but it is inefficient in collecting and sharing global information among base stations. In this paper, we propose a novel solution based on a collaborative cloud edge architecture for multi-cell joint beamforming optimization. We analyze the performance and costs of the proposed solution with two other architectural solutions by simulation. Compared with the centralized solution, our solution improves prediction accuracy by 24.66%, and reduces storage cost by 83.82%. Compared with the decentralized solution, our solution improves prediction accuracy by 68.26%, and improves coverage performance by 0.4dB. At last, the future research work is prospected.

Sixth Generation (6G) wireless communication network has been expected to provide global coverage, enhanced spectral efficiency, and AI(Artificial Intelligence)-native intelligence, etc. To meet these requirements, the computational concept of Decision-Making of cognition intelligence, its implementation framework adapting to foreseen innovations on networks and services, and its empirical evaluations are key techniques to guarantee the generation-agnostic intelligence evolution of wireless communication networks. In this paper, we propose an Intelligent Decision Making (IDM) framework, acting as the role of network brain, based on Reinforcement Learning modelling philosophy to empower autonomous intelligence evolution capability to 6G network. Besides, usage scenarios and simulation demonstrate the generality and efficiency of IDM. We hope that some of the ideas of IDM will assist the research of 6G network in a new or different light.

The convergence of information, communication, and data technologies (ICDT) has been identified as one of the developing trends of the sixth generation(6G) network. Service-based architecture (SBA) as one of the promising information technology, has been preliminarily introduced into the fifth generation (5G) core network (CN) and successfully commercialized, which verifies its feasibility and effectiveness. However, SBA mainly focuses on the control plane of CN at present and the SBA-CN user plane is being studied by the industry. In addition to further evolving the SBA-CN, SBA radio access network (RAN) should also be considered to enable an end-toend SBA, so as to meet more comprehensive and extreme requirements of future applications, as well as support fast rollout requirements of RAN devices.