This letter proposes a low-complexity deep-learning-based direction-of-arrival (DOA) estimation method for a hybrid massive multiple-input multiple-output (MIMO) system with a uniform circular array at the base station. In the proposed method, we first input the received signal vector into some small deep feedforward networks that are trained offline. Based on the outputs of the networks, we then generate a set of candidate angles. By selecting the optimal one from all candidate angles, we finally obtain the DOA estimation. Simulation results demonstrate that, compared with the conventional maximum likelihood (ML) method, the proposed DOA estimation method can achieve similar or even better performance with much less complexity.

We propose a simple sparse channel estimation and tracking method for orthogonal frequency-division multiplexing (OFDM) systems based on a dynamic parametric channel model, where the channel is parameterized by a small number of distinct paths, each characterized by path delay and path gain, and all parameters are time varying. In the proposed method, we adaptively choose the delay grid and estimate each channel path delay iteratively. To further reduce the complexity, we also propose a tracking algorithm based on the fact that the changes in the channel path number and path delays are small over a few adjacent OFDM symbols. After the physical path delays are estimated, we then estimate the channel path gains by using the polynomial basis expansion model (P-BEM). Simulation results demonstrate the effectiveness of the proposed channel estimation and tracking method in dynamic environments. Compared with the compressive-sensing-based channel estimator using the orthogonal matching pursuit (OMP) algorithm, the new technique proposed here has much lower computational complexity while offering comparable performance.

Satellite communication has the potential to play a key role in many applications of Internet of Things (IoT). In this paper, we consider a satellite-based IoT and investigate the technology that can improve the spectral efficiency. In general, one beam in satellite systems serves one user. To serve multiple users, time division multiplexing (TDM) or frequency division multiplexing (FDM) is usually used. In this paper, we propose a novel forward-link multiplexed scheme, by which the signals of different users can be transmitted simultaneously using the same frequency band. Specifically, at the transmitter, we first map each combination of the users’ constellation points to a higher-order constellation point, which is referred to as constellation coding, and then transmit such higher-order modulation signals. At the user side, after receiving and detecting the transmitted signal, each user obtain its own signal by the corresponding demapping, which is referred to as constellation decoding. The total system capacity over an additive white Gaussian noise (AWGN) channel is analyzed in the paper. Simulation results demonstrate that the proposed scheme can greatly improve the spectral efficiency.

This letter is concerned with the downlink channel estimation in frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) system. With the number of antennas increased, acquiring the downlink channel state information (CSI) becomes complex, thus restricts the performance of communication systems. A deep learning based algorithm is used to estimate the downlink CSI without the feedback. In the proposed method, the uplink cluster is firstly obtained from the receiving signals. Based on the uplink cluster data, the downlink channel is then estimated by a neural network. Simulation results show that the proposed algorithm can achieve higher achievable spectral efficiency.

Typical adversarial-training-based unsupervised domain adaptation (UDA) methods are vulnerable when the source and target datasets are highly complex or exhibit a large discrepancy between their data distributions. Recently, several Lipschitz-constraint-based methods have been explored. The satisfaction of Lipschitz continuity guarantees a remarkable performance on a target domain. However, they lack a mathematical analysis of why a Lipschitz constraint is beneficial to UDA and usually perform poorly on large-scale datasets. In this article, we take the principle of utilizing a Lipschitz constraint further by discussing how it affects the error bound of UDA. A connection between them is built, and an illustration of how Lipschitzness reduces the error bound is presented. A local smooth discrepancy is defined to measure the Lipschitzness of a target distribution in a pointwise way. When constructing a deep end-to-end model, to ensure the effectiveness and stability of UDA, three critical factors are considered in our proposed optimization strategy, i.e., the sample amount of a target domain, dimension, and batchsize of samples. Experimental results demonstrate that our model performs well on several standard benchmarks. Our ablation study shows that the sample amount of a target domain, the dimension, and batchsize of samples, indeed, greatly impact Lipschitz-constraint-based methods' ability to handle large-scale datasets. Code is available at https://github.com/CuthbertCai/SRDA.

Today, stream data applications represent the killer applications for Edge computing: placing computation close to the data source facilitates real-time analysis. Previous efforts have focused on introducing light-weight distributed stream processing (DSP) systems and dividing the computation between Edge servers and the clouds. Unfortunately, given the limited computation power of Edge servers, current efforts may fail in practice to achieve the desired latency of stream data applications. In this vision paper, we argue that by introducing FPGAs in Edge servers and integrating them into DSP systems, we might be able to realize stream data processing in Edge infrastructures. We demonstrate that through the design, implementation, and evaluation of F-Storm, an FPGA-accelerated and general-purpose distributed stream processing system on Edge servers. F-Storm integrates PCIe-based FPGAs into Edge-based stream processing systems and provides accelerators as a service for stream data applications. We evaluate F-Storm using different representative stream data applications. Our experiments show that compared to Storm, F-Storm reduces the latency by 36% and 75% for matrix multiplication and grep application. It also obtains 1.4x and 2.1x improvement for these two applications, respectively. We expect this work to accelerate progress in this domain.