In massive MIMO systems, the high complexity of signal detection comes mainly from computing a Gram matrix and its inversion. In this correspondence, we propose a low-complexity MIMO detection method based on the Neumann series (NS), which does not require explicitly computing the Gram matrix and its inversion. The proposed method leverages the statistical information of the Gram matrix to compute an initialization matrix, which guarantees the convergence of NS expansion. The signal detection is conducted implicitly by transforming matrix inversion and product operations into matrix-vector multiplication operations. By doing so, it reduces the computational complexity of MIMO signal detection from $\mathcal {O}(BU^{2}+U^{3})$ to $\mathcal {O}(BUN)$ , where $B$ , $U$ , and $N$ are the numbers of antennas at the base station, at the user equipment, and NS terms, respectively. Simulation results show that, compared with existing approaches, the proposed method can significantly reduce the signal detection complexity while achieving similar performance.

The M-ary distributed decision fusion is studied for multihop amplify-and-forward wireless sensor networks (WSNs), the implementation framework is given, and the performance analysis is developed. In particular, we first propose an M-ary distributed decision fusion configuration, wherein the multihop relay network is involved, and the relay node only forward information from their neighbors. Furthermore, the optimal decision rule with the explicit and exact form is derived, and the implementation structure is depicted. Our results show that, the hamming distance (HD), which is well-known in information theory, is finally involved in the optimal decision metric. Then, we derive the suboptimal decision fusion algorithms for three scenarios. First, we achieve an interesting rule in the form of a maximum ratio combining (MRC), wherein the local channel is considered to be ideal. When the idea of equal gain combining (EGC) is directly followed, we propose the minimum HD summation (MHDS) rule. We also give a selective combining (SC) statistic, wherein only received observation with the optimal quality is selected for decision fusion, and we correspondingly term this criterion as the minimum HD (MHD) rule. Second, we achieve a Chair–Varshney rule when it is assumed that the crossover probability of relay binary symmetric channel (BSC) is small enough. The simple majority-based statistic is developed when the homogeneous multihop relay WSNs is considered. A statistic with the similar form of SC is also developed. Third, when each relay BSC’s crossover probability is relatively large, we also propose the suboptimum fusion statistic in an analog form to the MRC and EGC, respectively. Our results show that the MHDS rule can be achieved via optimum decision rule in the first or third scenarios. The performance assessment is determined both Monte Carlo simulation and analysis.

Many approaches to build a wiretap channel (WTC) by multi‐input multi‐output system have been introduced. Different from those approaches, the authors propose a feedback method combined with the low‐density parity‐check (LDPC) codes for building the WTC I (WTC‐I) to achieve unconditional security under single‐input single‐output system by the soft decision decoding. The novel approach establishes the WTC‐I on both the binary symmetric channel and binary input additive white Gaussian noise channel. In order to keep the eavesdropper be fully ignorant about the secret information, randomness is added to the feedback signals from the destination by taking advantage of feedback. In addition, the message to be sent is encoded by the LDPC codes such that it can be correctly decoded by a legitimate receiver. Furthermore, the secret information transmission capacity can be improved by the soft decision decoding.

Accurate rainfall observation data with high temporal and spatial resolution are essential for national disaster prevention and mitigation as well as climate response decisions. This paper introduces a field experiment using an E-band millimeter-wave link to obtain rainfall rate information in Nanjing city, which is situated in the east of China. The link is 3 km long and operates at 71 and 81 GHz. We first distinguish between the wet and the dry periods, and then determine the classification threshold for calculating attenuation baseline in real time. We correct the influence of the wet antenna attenuation and finally calculate the rainfall rate through the power law relationship between the rainfall rate and the rain-induced attenuation. The experimental results show that the correlation between the rainfall rate retrieved from the 71 GHz link and the rainfall rate measured by the raindrop spectrometer is up to 0.9. The correlation at 81 GHz is up to 0.91. The mean relative errors are all below 5%. By comparing with the rainfall rate measured by the laser raindrop spectrometer set up at the experimental site, we verified the reliability and accuracy of monitoring rainfall using the E-band millimeter-wave link.

Data heterogeneity may significantly deteriorate the performance of federated learning since the client’s data distribution is divergent. To mitigate this issue, an effective method is to partition these clients into suitable clusters. However, existing clustered federated learning is only based on the gradient descent method, which leads to poor convergence performance. To accelerate the convergence rate, this paper proposes clustered federated learning based on momentum gradient descent (CFL-MGD) by integrating momentum and cluster techniques. In CFL-MGD, scattered clients are partitioned into the same cluster when they have the same learning tasks. Meanwhile, each client in the same cluster utilizes their own private data to update local model parameters through the momentum gradient descent. Moreover, we present gradient averaging and model averaging for global aggregation, respectively. To understand the proposed algorithm, we also prove that CFL-MGD converges at an exponential rate for smooth and strongly convex loss functions. Finally, we validate the effectiveness of CFL-MGD on CIFAR-10 and MNIST datasets.

In this paper, the noncoherent detection scheme for the receiver in wireless sensor nodes is discussed. That is, an implementation-friendly and energy-efficient symbol-by-symbol detection scheme for IEEE 802.15.4 offset-quadrature phase shift keying (O-QPSK) receivers is investigated under both pure additive white Gaussian noise (AWGN) channel and fading channel. Specifically, the residual carrier frequency offset (CFO) of the chip sample is estimated and compensated with the aid of the preamble; then, the standard noncoherent detection scheme with perfectly known CFO is directly configured. The corresponding simulation results show that only 4 preamble symbols is sufficient for accurate CFO estimation. Compared with the conventional noncoherent detector, the average running time per data packet of our enhanced detector is only 0.17 times of the former; meanwhile, at the packet error rate of 1 × 10⁻³, our enhanced detector can obtain 2.2 dB gains in the (32, 4) direct sequence spread spectrum system. A more reasonable trade-off between complexity and reliability is thus achieved for energy-saving and maximum service life in wireless sensor networks (WSNs).