Abstract: A transmit beamforming method for integrated multi-antenna systems with joint near-field sensing and far-field communication, which includes: (1) establishing a communication signal transmission model and a communication channel model, and analyzing an upper limit of communication spectrum efficiency; (2) acquiring an optimal communication beam matrix based on the communication channel model; (3) establishing a near-field sensing signal model; (4) generating an optimal radar beam matrix based on an antenna array response vector of a radar detection target; (5) introducing a compromise factor to construct an initial optimization problem of an integrated beamforming matrix in power constraint based on the established near-field sensing signal model; and (6) transforming and solving the constructed initial optimization problem to obtain the integrated beamforming matrix.

Abstract: A dynamic power control method and system for resisting multi-user parameter biased aggregation in federated learning are provided; the method includes: (1) establishing a federated learning system model for resisting parameter biased aggregation; (2) constructing a 5 corresponding objective function based on a training purpose of the federated learning system model; (3) introducing, according to the established federated learning system model, a power control factor for resisting user biased gradient aggregation, and determining a corresponding over-the-air computation communication model; (4) processing a signal by a receiver using an incoherent energy detection method without cooperation between the receiver and a transmitter; and (5) determining a federated learning security mechanism method based on resistance against parameter biased aggregation, and completing an updating training process of the federated learning system model.

Abstract: A non-uniform beam spatial modulation method and system applicable to multi-antenna communication and sensing integration; the method includes: finding an ISAC beam that satisfies both sensing performance and communication performance, including: constructing a communication transmitting signal; constructing a communication receiving signal; constructing an upper bound of the integrated communication and sensing spectral efficiency of the non-uniform beam spatial modulation; constructing an objective function of the sensing performance; and finding a candidate beam matrix and a beam activation probability distribution where the spectrum frequency is maximum and the sensing performance is best; and finding a spectral efficiency of non-uniform beam spatial modulation by means of the ISAC beam. Under the condition of meeting the same sensing performance requirement, the spectral efficiency of the present invention is higher.

Abstract: A communication compression method based on model weight distribution in federated learning, and belongs to the technical field of wireless communication. Based on the existing federated average idea in federated learning, counts the distribution of model weight information to be transmitted between nodes during each communication, then performs scalar quantization and compression through Lloyd-Max quantizer according to their distribution characteristics, then encodes with Huffman coding method, and finally sends the codes to the target node, thereby the minimum mean square quantization error is realized and the number of bits required for communication is reduced.