Massive multiple input multiple output (MIMO) systems can increase capacity and reliability greatly. However,  extremely high hardware costs and computational complexity lead to the demand for reasonable antenna selection.  Aiming at the problem that the traditional antenna selection algorithm based on maximizing sum capacity has large  complexity and worse bit error rate (BER) performance, a two-step selection algorithm is proposed, which selects  a part of the antennas based on the norm-based antenna selection (NBS) firstly, and then selects the antenna based  on maximizing capacity via convex optimization. The simulation results show that the improved algorithm has better  BER performance than the traditional algorithms. At the same time, it reduces computational complexity greatly.

Ultra-dense networks (UDNs) is a promising solution to meet the exponential increase in mobile data traffic. But  the ultra-dense deployment of cells inevitably brings complicated inter-cell interference ( ICI) and existing  interference coordination scheme cannot be directly applied. To minimize the aggregate interference of each small  cells, this paper formulates the problem as a distributed noncooperation game-based interference coordination  scheme in ultra-dense networks considering the real demand rate of each small cell user equipment (SUE) and  proves it to be a potential game. An improved no-regret learning algorithm is introduced to coverage to the Nash  equilibrium (NE) of the formulated game. Simulation results show that the proposed scheme has better performance  compared with existing schemes.

For large-scale radio frequency identification ( RFID) indoor positioning system, the positioning scale is relatively large, with less labeled data and more unlabeled data, and it is easily affected by multipath and white noise. An RFID positioning algorithm based on semi-supervised actor-critic co-training (SACC) was proposed to solve this problem. In this research, the positioning is regarded as Markov decision-making process. Firstly, the actor-critic was combined with random actions and selects the unlabeled best received signal arrival intensity (RSSI) data by co-training of the semi-supervised. Secondly, the actor and the critic were updated by employing Kronecker-factored approximation calculate (K-FAC) natural gradient. Finally, the target position was obtained by co-locating with labeled RSSI data and the selected unlabeled RSSI data. The proposed method reduced the cost of indoor positioning significantly by decreasing the number of labeled data. Meanwhile, with the increase of the positioning targets, the actor could quickly select unlabeled RSSI data and updates the location model. Experiment shows that, compared with other RFID indoor positioning algorithms, such as twin delayed deep deterministic policy gradient (TD3), deep deterministic policy gradient (DDPG), and actor-critic using Kronecker-factored trust region ( ACKTR), the proposed method decreased the average positioning error respectively by 50.226%, 41.916%, and 25.004%. Meanwhile, the positioning stability was improved by 23.430%, 28.518%, and 38.631%.

Accurate estimationand real-time compensation for phase offset and Doppler shift are essential for coherent multi- input multi-output (MIMO) systems. Here, a spatial multiplexing MIMO scheme with non-coherent frequency-shift  keying (FSK) detection is proposed. It is immune to random phase interference and Doppler shift while increasing  capacity. It is valuable that the proposed spatial multiplexing MIMO based on energy detection (ED) is equivalent  to a linear system, and there is no mutual interference caused by the product of simultaneous signals in square-law  processing. The equivalent MIMO channel model is derived as a real matrix, which remains maximal multiplexing  capacity and reduces the channel estimation complexity. Simulation results show that the proposed scheme has  outstanding performance over Rician flat fading channel, and experimental system obtains four times the capacity  through 4 antennas on both transmitter and receiver.