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In this paper, we consider an extremely large-scale massive multiple-input-multiple-output (XL-MIMO) system. As the scale of antenna arrays increases, the range of near-field communications also expands. In this case, the signals no longer exhibit planar wave characteristics but spherical wave characteristics in the near-field channel, which makes the channel state information (CSI) highly complex. Additionally, the increase of the antenna arrays scale also makes the size of the CSI matrix significantly increase. Therefore, CSI feedback in the near-field channel becomes highly challenging. To solve this issue, we propose a deep-learning (DL)-based ExtendNLNet that can compress the CSI, and further reduce the overhead of CSI feedback. In addition, we have introduced the Non-Local block to obtain a larger area of CSI features. Simulation results show that the proposed ExtendNLNet can significantly improve the CSI recovery quality compared to other DL-based methods.

In this paper, we consider an reconfigurable intelligent surface (RIS)-aided frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) downlink system.In the FDD systems, the downlink channel state information (CSI) should be sent to the base station through the feedback link. However, the overhead of CSI feedback occupies substantial uplink bandwidth resources in RIS-aided communication systems. In this work, we propose a deep learning (DL)-based scheme to reduce the overhead of CSI feedback by compressing the cascaded CSI. In the practical RIS-aided communication systems, the cascaded channel at the adjacent slots inevitably has time correlation. We use long short-term memory to learn time correlation, which can help the neural network to improve the recovery quality of the compressed CSI. Moreover, the attention mechanism is introduced to further improve the CSI recovery quality. Simulation results demonstrate that our proposed DLbased scheme can significantly outperform other DL-based methods in terms of the CSI recovery quality

In this paper, we consider an active reconfigurable intelligent surface (RIS)-aided unmanned aerial vehicle(UAV)-enabled simultaneous wireless information and power transfer(SWIPT) system with multiple ground users. Compared with the conventional passive RIS, the active RIS deploying the internally integrated amplifiers can offset part of the multiplicative fading. In this system, we deal with an optimization problem of minimizing the total energy cost of the UAV. Specifically, we alternately optimize the trajectories, the hovering time, and the reflection vectors at the active RIS by using the successive convex approximation (SCA) method. Simulation results show that the active RIS performs better in energy saving than the conventional passive RIS., Comment: Keywords:Reconfigurable intelligent surface (RIS), active RIS, unmanned aerial vehicle (UAV), simultaneous wireless information and power transfer (SWIPT), successive convex approximation (SCA)

Given a set $V$ of $v$ servers along with $b$ files (data), each file is replicated (placed) on exactly $k$ servers and thus a file can be represented by a set of $k$ servers. Then we produce a data placement consisting of $b$ subsets of $V$ called blocks, each of size $k$. Each server has some probability to fail and we want to find a placement that minimizes the variance of the number of available files. It was conjectured that there always exists an optimal data placement (with variance better than any other placement for any value of the probability of failure). An optimal data placement for triple replication with $b$ blocks (of size three) on a $v$-set was proved to exist by Wei et al. if $v$ and $b$ are not excluded by two conditions. This article concentrates on the parameters $v, b$ satisfying the two conditions and characterizes the combinatorial properties of the corresponding optimal data placements. Nearly well-balanced triple systems (NWBTSs) are defined to produce optimal data placements. Many constructions for NWBTSs are developed, mainly by constructing candelabra systems with various desirable partitions. The main result of this article is that there always exist optimal data placements for triple replication with $b$ blocks on a $v$-set possibly except when $v\equiv 4$ (mod 24) or $v=50,74$, and $\frac{\lambda v(v-1)}{6}-\frac{v}{6} < b < \frac{\lambda v(v-1)}{6}+\frac{v}{6}$ for an odd integer $\lambda$.