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To deal with the resource competition among satellites in the multi-satellite space-ground computing network, a space-ground heterogeneous resource cooperative game mechanism was designed in terms of the computing and spectrum domains.Each satellite published a computing task which was independent of other tasks and relied on UE to generate raw data.By competing the resources of user terminals and UE, the task offloading and processing was achieved.To provide real-time data services, a distributed scheme was proposed based on multi-agent reinforcement learning to coordinate the computing and spectrum resource competition among satellites, thereby minimizing the system latency.Simulation results indicated that, compared with the existing schemes, the proposed algorithm achieves a lower system latency by fully utilizing the computing and spectrum resources and coordinating the resource competition.

In this paper, we consider a decode-and-forward (DF) buffer-aided (BA) multi-relay system using double differential (DD) encoding and decoding for the transmission and reception of data packets. The proposed system does not require carrier frequency offset (CFO) information and channel state information (CSI) at any communicating node. A priority-based max link selection protocol has been adopted to select links based on the buffer status and channel quality. The Markov-chain approach is used for developing the state transition matrix, using which the steady state probability of the system is obtained. The outage probability and average bit error rate (ABER) expressions are derived using the steady-state probability. The proposed setup performance is then compared with the conventional coherent BA system. It is established that the considered setup is affected by a signal-to-noise ratio (SNR) penalty of $\approx 3$ dB, which is considerably lower than the well-known 6 dB SNR penalty existing for DD modulation compared to coherent modulation. Also, the proposed setup outcompetes the coherent max-link approach in the presence of a CFO.

Ultra-dense low earth orbit (LEO) satellite communication networks can overcome the scarcity of spectrum resources and the limited coverage of traditional terrestrial networks, and thus have the potential to provide high data rate services and global massive connectivity for terrestrial users.However, due to the high mobility of the satellites, LEO satellite networks put more stringent requirements on antenna technologies in terms of accurate beam steering and high antenna gain.Reconfigurable holographic surface (RHS), as a new type of metamaterial antenna, is investigated to assist LEO satellite communications.The RHS can electronically control the metamaterial units by leveraging the holographic principle to generate desired directional beams.Based on the hardware structure and holographic working principle of RHS, an RHS-assisted multi-satellite communication scheme was proposed, which considered both the LEO satellite tracking scheme and the data transmission scheme.A holographic beamforming optimization algorithm was also designed to maximize the sum rate.Simulation results verify the effectiveness of the proposed scheme and demonstrat that the RHS provids a more cost-effective way to support satellite communications than the conventional phased array antennas.

Integrated sensing and communication (ISAC) is an essential technology in the upcoming 6G network, and its performance can be effectively enhanced by the reconfigurable intelligent surface (RIS). Among the various RIS-enabled ISAC techniques, RIS-enabled sensing-assisted communication has attracted growing attention because it can effectively improve communication performance by focusing the energy on the sensed locations of the users and is easy to be integrated into existing communication systems. However, existing RIS-enabled sensing-assisted communication systems rely on antenna arrays for the acquisition of angular information to localize the users, making the system more complex and expensive. To handle this problem, in this paper, we propose an RIS-enabled multi-user sensing-assisted communication framework where a single antenna access point (AP) first senses the locations of the single-antenna users with the help of the RIS, and then the energy of the communication signal is focused on the sensed locations of the users to provide them with a higher sum-rate by adjusting the phase shifts of the RIS. However, the selection of the RIS phase shifts heavily influences the resource block and power allocation at the AP, which makes the joint design of the RIS phase shifts and the resource allocation very challenging. In order to address this challenge, we formulate an RIS-enabled multi-user communication optimization problem and design a two-stage optimization algorithm based on the genetic and Lagrangian duality methods to jointly optimize the RIS phase shifts and the resource allocation. Simulation and experimental results show that compared with the scheme without RIS, the proposed RIS-enabled multi-user communication system can achieve a higher sum-rate and lower localization error.

With the development of blockchain technology,more and more blockchain projects have appeared.Due to the closure property of the blockchain,different blockchains have become islands of value.The information interaction and value transfer problems between different blockchains need to be solved urgently.The cross-chain technology is to solve the cross-chain operation problems of assets and data between different chains.There are many attempts and developments on cross-chain technology in the past few years,and the major models of cross-chain include Hash lock,notary mechanism,side chain and relay technology,etc.The concept of each cross-chain technology was introduced,and the advantages and disadvantages of each cross-chain technology were summarized and analyzed.

With the development of the Internet of things,multiple real-time applications will emerge in future 6G communication network.Driven by demands on low-latency services of multiple real-time applications,mobile edge computing (MEC) will become an important technology to improve the user experience and reduce the network cost.However,the computing capacity of a single MEC server is limited,which induces that it is difficult to meet the low-latency requirement of data processing for the computation-intensive applications.A heterogeneous multi-layer mobile edge computing (HetMEC) network architecture was proposed to jointly utilize the computing and transmission resources of both the cloud computing center and multi-layer MEC servers.By reasonably dividing and offloading computing tasks,reliable and efficient computing services were provided for edge applications.Simulation results show that the proposed algorithm could effectively reduce the processing latency,improve the network processing rate and robustness.

Driven by both safety concerns and commercial interests, popular content distribution (PCD), as one of the key services offered by vehicular networks, has recently received considerable attention. In this paper, we address the PCD problem in highway scenarios, in which a popular file is distributed to a group of onboard units (OBUs) driving through an area of interest (AoI). Due to high speeds of vehicles and deep fading of vehicle-to-roadside (V2R) channels, the OBUs may not finish downloading the entire file. Consequently, a peer-to-peer (p2p) network should be constructed among the OBUs for completing the file delivery process. Here, we apply the cognitive radio technique for vehicle-to-vehicle communications and propose a cooperative approach based on coalition formation games, which jointly considers the spectrum sensing and channel access performance. Simulation results show that our approach presents a considerable performance improvement compared with the non-cooperative case.

The research progress and contributions about the fundamental theories and key technologies of collaborative heterogeneous underlaying cellular networks were introduced. The fundamental theories include the architecture, capacity region, connectivity problem of the collaborative heterogeneous underlaying networks, while the key technologies consist of interference management, wireless resource management, and exploration and utilization of vacant radio-frequency spectrum. The current study shows that D2D communication in collaborative heterogeneous underlaying cellular networks will become the key technology in the next-generation cellular communication systems because it can further improve the spectrum efficiency and system capacity in wireless communication networks.

Device-to-device（D2D）communication enables seamless operation of combining cellular and mobile Ad Hoc networks, which significantly improves the system spectrum efficiency and network capacity. The concept and application challenges of D2D local area network in theory and practice were introduced. The importance of cross layer resource management in solving interference avoidance of D2D communication was discussed, and an example of wireless resource management was also provided. The data results show that the performance of the proposed scheme is close to that of the exhaustive search optimal scheme, while has a relatively lower complexity. At present, D2D communication exhibits vast potential of achieving high system capacity and fully exploring wireless resources.

We introduce the superimposed training strategy into the multiple-input multiple-output (MIMO) amplify-and-forward (AF) one-way relay network (OWRN) to perform the individual channel estimation at the destination. Through the superposition of a group of additional training vectors at the relay subject to power allocation, the separated estimates of the source-relay and relay-destination channels can be obtained directly at the destination, and the accordance with the two-hop AF strategy can be guaranteed at the same time. The closed-form Bayesian Cramér-Rao lower bound (CRLB) is derived for the estimation of two sets of flat-fading MIMO channel under random channel parameters and further exploited to design the optimal training vectors. A specific suboptimal channel estimation algorithm is applied in the MIMO AF OWRN using the optimal training sequences, and the normalized mean square error performance for the estimation is provided to verify the Bayesian CRLB results.

We propose double-differential (DD) modulation for the amplify-and-forward protocol over Nakagami-m fading channels with carrier offsets. We propose an emulated maximum ratio combining (EMRC) decoder, which could be used by the double-differential receiver in the absence of exact channel knowledge. Approximate bit error rate (BER) analysis is performed for the proposed double-differential modulation-based cooperative communication system. The proposed double-differential system is immune to random carrier offsets, whereas the conventional single-differential modulation-based cooperative system breaks down. In addition, the proposed scheme is able to perform better than the same rate training-based cooperative system which utilizes training data for finding estimates of carrier offsets and channel gains.

We report a simple differential modulation scheme for quasi-orthogonal space-time block codes. A new class of quasi-orthogonal coding structures that can provide partial transmit diversity is presented for various numbers of transmit antennas. Differential encoding and decoding can be simplified for differential Alamouti-like codes by grouping the signals in the transmitted matrix and decoupling the detection of data symbols, respectively. The new scheme can achieve constant amplitude of transmitted signals, and avoid signal constellation expansion; in addition it has a linear signal detector with very low complexity. Simulation results show that these partial-diversity codes can provide very useful results at low SNR for current communication systems. Extension to more than four transmit antennas is also considered.