The multi-probe anechoic chamber (MPAC) method is widely regarded as the most promising for 5G multiple-input multiple-output (MIMO) dynamic channel over-the-air (OTA) testing. However, the probes used in the testing system is varying to reproduce the channel in a laboratory environment, which leads to a dramatic increase in the complexity of probe selection. In this paper, a concept of context channel information is presented based on the variation of angles. And two corresponding probe selection algorithms are proposed to resolve the above problem. These algorithms map the context channel information to the variation of probe locations. Based on the proposed methods, reconstruction of two typical dynamic channel models are performed. Through different indicators like four-dimensional root-mean-square (4D - RMS) error of spatial correlation, four-dimensional power angular spectrum similarity percentage (4D - PSP) and execution time, the improvement in complexity and accuracy of our proposed methods relative to the snapshot-by-snapshot methods are discussed. The simulation results indicate that the proposed methods can accurately reconstruct the channel environment around the device-under-test (DUT) with much lower computational complexity.
As the primary source of energy consumption in communication networks, the power usage of 5G base station(BS) is a significant concern. The sleep mode (SM) of BS can be utilized to reduce mobile network consumption during periods of low traffic demand. However, current BS sleep technologies primarily rely on network-side participation, with insufficient involvement from user equipment. In this paper, inspired by the concept of user-initiated BS activation mentioned in 3GPP TR38.864 [1], we design a user-driven base station sleep and wake-up mechanism that considers user-generated wake-up signals to activate sleeping BSs. Analysis results indicate that this method can achieve significant energy savings.
Steganographic coding is a fundamental technique in adaptive steganography, and the introduction of polar codes has significantly enhanced its security. However, the performance of steganographic polar codes has primarily been evaluated through experiments, and their construction has remained some-what heuristic. This paper theoretically presents an optimal model for steganographic polar codes based on a analysis of their performance bounds, by examining the relationship between polar code decoding and steganographic coding based on polar codes. Although this model can only be constructed approximately, numerical simulations indicate that the proposed optimal construction offers improved security performance compared to classic codes.
This paper presents a computer-aided search scheme based on simulated annealing methodology for regular quasi-cyclic low-density parity-check (QC-LDPC) codes, ensuring a minimum girth of at least 8. The approach utilizes the simulated annealing algorithm, leveraging the correlation between cycles and trapping sets, and analyzes their positioning in the Tanner graph to iteratively reduce trapping sets. This process ultimately results in the generation of high-girth QC-LDPC codes. A significant advantage of our method is that it greatly reduces the search time compared to existing approaches. Furthermore, our method generates QC-LDPC codes that demonstrate a diminution in the number of 8-cycles, concurrently achieving a reduction in detrimental trapping sets. Simulation results reveal that our method generates QC-LDPC codes with excellent bit error rate performance.
Freshness of information is a critical issue for real-time Internet of Things (IoT) applications. Unmanned aerial vehicles (UAVs), due to their high flexibility and low cost, are widely deployed in time-sensitive IoT scenarios. In this article, we explore the trajectory planning problem in rechargeable-UAV-aided IoT Networks. To accurately measure the freshness of information, we introduce Age of Information (AoI) and Age of Synchronization (AoS). AoI is utilized to plan the UAVs' trajectories for accessing sensors (SNs) and reduce the average AoS of the system. UAVs can recharge at charging stations to maintain energy above a certain threshold. We formulate a Markov decision process with the objective of minimizing the average AoS under the energy constraint. To adjust the dynamic environment, we propose a multi-agent deep reinforcement learning method to jointly optimize the UAVs' flight trajectories and battery recharging. Simulation results demonstrate that the proposed algorithm can significantly reduce the total average AoS under the energy constraint.
