This paper considers a multi-user semantic and data communication (MU-SemDaCom) system, where a base station (BS) simultaneously serves users with different semantic and data tasks through a downlink multi-user multiple-input single-output (MU-MISO) channel. The coexistence of heterogeneous communication tasks, diverse channel conditions, and the requirements for digital compatibility poses significant challenges to the efficient design of MU-SemDaCom systems. To address these issues, we propose a multi-user adaptive source-channel coding (MU-ASCC) framework that adaptively optimizes deep neural network (DNN)-based source coding, digital channel coding, and superposition broadcasting according to the channel conditions. First, we employ a data-regression method to approximate the end-to-end (E2E) semantic and data distortions, for which no closed-form expressions exist due to the complex coupling between DNN-based source coding and channel codes. The obtained logistic formulas decompose the E2E distortion as the addition of the source and channel distortion terms, in which the logistic parameter variations are task-dependent and jointly determined by both the DNN and channel parameters. Then, based on the derived formulas, we formulate a weighted-sum E2E distortion minimization problem that jointly optimizes the source-channel coding rates, power allocation, and beamforming vectors for both the data and semantic users. Finally, an alternating optimization (AO) framework is developed, where the adaptive rate optimization is solved using the subgradient descent method, while the joint power and beamforming is addressed via the uplink-downlink duality (UDD) technique. Simulation results demonstrate that, compared with the conventional separate source-channel coding (SSCC) and deep joint source-channel coding (DJSCC) schemes that are designed for a single task, the proposed MU-ASCC scheme achieves simultaneous improvements in both the data recovery and semantic task performance.

Ultra dense networks (UDNs) with smart-duplex (SD), which allows the base stations (BSs) to flexibly switch between the half-duplex (HD) and full-duplex (FD), are expected to support high-density transmissions. However, to centrally handle a large network is costly, while distributed processing may suffer from the severe performance loss due to the complicated intercell interferences in the UDNs. This article aims to balance the system performance and clustering cost of the SD UDNs by dividing all small cells into several clusters. A Markov decision process (MDP) problem is formulated to maximize the average weighted sum of network throughput and clustering cost for all clusters. To approximately solve this problem, we first adopt an affinity propagation method to determine the number of clusters and the center of each cluster. Then, by treating small cells as agents, the original MDP problem is proved to be equivalent to a multiagent MDP to maximize the average reward of all small cells. Next, a multiagent deep reinforcement learning (DRL) is proposed to jointly implement the dynamic clustering for noncenter small cells, resource allocation, and duplex mode selection. Simulation results show that SD has prominent advantages over both the HD and FD in UDNs, and the proposed multiagent DRL outperforms other clustering schemes under the considered scenarios.

In this article, we consider covert beamforming design for intelligent reflecting surface (IRS)-assisted Internet-of-Things (IoT) networks, where Alice utilizes IRS to covertly transmit a message to Bob without being recognized by Willie. We investigate the joint beamformer design of Alice and IRS to maximize the covert rate of Bob when the knowledge about Willie’s channel state information (WCSI) is perfect and imperfect at Alice, respectively. For the former case, we develop a covert beamformer under the perfect covert constraint by applying semidefinite relaxation. For the latter case, the optimal decision threshold of Willie is derived, and we analyze the false alarm and the missed detection probabilities. Furthermore, we utilize the property of the Kullback–Leibler divergence to develop the robust beamformer based on a relaxation,$S$-Lemma, and alternate iteration approach. Finally, the numerical experiments evaluate the performance of the proposed covert beamformer design and robust beamformer design.

Massive machine-type communications (mMTC) are expected to support a large amount of randomly deployed users for short package transmissions. Noncoherent random access provides an efficient and practical multi-access protocol for mMTC, and also poses new challenges for the receiver design. In this paper, we leverage two well-known methods, i.e., message passing and deep learning, to jointly detect the user activity and the desired data for the noncoherent mMTC. First, by exploiting the exact distribution information of the received signal, a generalized approximate message passing (GAMP)-based algorithm is proposed, which is shown to jointly detect the user activity and the desired data by two modules: inter-user interference elimination and data detection for each user. Inspired by the two-module GAMP-based algorithm, we then propose a model-driven deep learning method, which utilizes the deep neural networks (DNNs) to approximate both the two modules. The loss function for training the DNNs is derived by formulating the two-module detection as an unconstrained optimization problem. Simulation results reveal that the proposed GAMP-based algorithm outperforms the proposed deep learning method when the channel distribution is perfectly known, while it suffers from a significant performance degradation for the case with imperfect channel distribution information.

Massive machine-type communications (mMTC) for the Internet of Things (IoT) are expected to support a large number of devices/users for short packet transmissions with low complexity and low energy consumption. By utilizing the simple while efficient noncoherent energy-based transmission scheme, this work aims to jointly detect the user activity and the desired data for mMTC. First, by exploiting the sparse characteristics of the user activity, approximation message passing (AMP) algorithm is proposed to eliminate the multi-user interference, and a denoiser is designed to minimize the mean-squared error (MSE) of the transmitted signals. Then, maximum${a}$posteriori(MAP) criterion is adopted to approximately detect the user activity and the desired data. By minimizing the symbol error probability of the above two-step algorithm, the power constellation for each user is designed, and it is shown to be asymptotically optimal as the number of the receiver antennas goes to infinity. Finally, simulation results reveal that the proposed noncoherent scheme outperforms the coherent one in the low SNR regime and for short packet transmissions.

Compressive sensing (CS) is a technique frequently adopted in wireless communications. By utilizing CS, a receiver could sense the state of channels with sub-Nyquist analog to digital converters when signals are sparse. Traditional CS methods struggle with non-sparse signals due to their intrinsic sparsity assumption. Therefore, we propose using deep learning (DL) to solve the vector support recovery problem with channels’ high occupancy. The simulation results show that the proposed CS framework powered by DL can perform better than a traditional CS analytical benchmark, both in high and low channel occupation regions. We also observe that the ML can work under a lower sampling rate than traditional CS methods. To process data sampled with high channel numbers, a divide and conquer tactic is implemented.

This article studies the energy efficiency (EE) of the two-way wireless communication system operating in the full-duplex (FD) and half-duplex (HD) modes, respectively. Particularly, both the residual self-interference (RSI) and power consumption for the self-interference cancellation (SIC) in the FD mode are modeled as linear functions over the transmit power. The EE maximization problems for FD, time-division duplex (TDD), and frequency-division duplex (FDD) modes with the sum and individual spectral efficiency (SE) constraints are studied, respectively. With the sum SE constraint, closed-form expressions for the maximum EE of these three modes are derived, and the maximum EE among them are compared. Based on the comparison results, a duplex selection scheme to achieve the highest EE among the three duplex modes is proposed. With the individual SE constraint, the optimal and suboptimal resource allocation are obtained by utilizing fractional programming for the three duplex modes. Somehow surprisingly, numerical results reveal that the FD mode achieves the best EE performance when the target sum SE or the distance between the two transceivers is relatively large.

Massive machine-type communications (mMTC) are expected to support bunches of low cost devices, which are stochastically active. By utilizing the simple while efficient noncoherent energy-based transmission scheme, this work aims to jointly detect the user activity and the data in mMTC. First, by exploiting the sparse characteristics of the user activity, approximation message passing (AMP) algorithm is proposed to suppress the multi-user interference. Then, maximum a posteriori (MAP) criterion is adopted to approximately detect the user activity and the desired data. By minimizing the symbol error probability of the above two-step algorithm, the power constellations at each user are designed. Finally, the scaling behavior of the considered system is analyzed, and it is shown that to guarantee reliable communications, the number of the receiver antennas per user should vanish in the order of O([1/log(N)]) with N being the number of the total users.

This paper considers a massive single-input multiple-output (SIMO) system, where multiple single-antenna transmitters simultaneously communicate with a receiver equipped with a large number of antennas. Different from the conventional noncoherent transceivers which require a certain level of the statistical information on the channel fading, we propose a joint transceiver design method based on machine learning, requiring a limited number of channel realizations. In the proposed method, the multiple transmitters, the channel, and the receiver are represented with a deep neural network (NN), and an autoencoder is adopted to minimize the end-to-end transmission error probability. Simulation results show that the proposed NN-based transceiver achieves lower transmission error probability in typical scenarios, and is more robust against the channel parameters variation compared with the existing methods.

In order to improve the multicast transmissions, a full-duplex (FD) receiver cooperation scheme is proposed in this paper. The transmitter sends one common message to two FD receivers, and each receiver forwards its received signals to its counterpart by the amplify-and-forward (AF) scheme. Due to the imperfect SI cancellation at the receivers and the AF scheme, the residual self-interference and the additive noise (RIAN) will be accumulated over time. First, this paper analyzes the equivalent channel model of the considered system as well as the the statistics of the accumulated RIAN. Then, with the forward decoding scheme, the corresponding achievable rates are derived, and the optimal power allocation is obtained via solving a max-min problem. In particular, one-side cooperation scheme (i.e., only one receiver forwards its received signal to its counterpart) is shown to be optimal to achieve the best system performance.

This article considers the design of the transmitter and receiver in a noncoherent massive single-input and multiple-output (SIMO) system over a multipath channel, representing a typical Internet-of-Things (IoT) scenario that consists of multiple single-antenna transmitters and one receiver with a large number of antennas. In particular, the autoencoders, which consist of multiple independent neural networks (NNs), are adopted at the transmitters and the receiver and are trained jointly, while working separately. To avoid the delicate design for mitigating the intersymbol interference (ISI) caused by multipath channels, the modulation schemes at the transmitters and the demodulation rule at the receiver are learned by the NNs over a limited number of channel samples. Moreover, the relationship between the number of channel samples and the performance of the trained transceiver is analyzed. The simulation results show that the proposed method achieves a lower error probability in comparison with the conventional optimization-based methods under typical channel conditions.

In this study, we investigated the nonorthogonal multiple access (NOMA) for visible light communication (VLC) Internet of Things (IoT) networks and provided a promising system design for 5G and beyond 5G applications. Specifically, we studied the capacity region of a practical uplink NOMA for multiple IoT devices with discrete and continuous inputs, respectively. For discrete inputs, we proposed an entropy approximation method to approach the channel capacity and obtain the discrete inner and outer bounds. For the continuous inputs, we derived the inner and outer bounds in closed forms. Based on these results, we further investigated the optimal receiver beamforming design for the multiple access channel (MAC) of VLC IoT networks to maximize the minimum uplink rate under receiver power constraints. By exploiting the structure of the achievable rate expressions, we showed that the optimal beamformers are the generalized eigenvectors corresponding to the largest generalized eigenvalues. Numerical results show the tightness of the proposed capacity regions and the superiority of the proposed beamformers for VLC IoT networks.

Spectrum sensing is one of the most challenging tasks in cognitive radio. Unfortunately traditional schemes fail to balance between accuracy and complexity, which are the key indicators for the performance of spectrum sensing. In this paper, a new spectrum sensing scheme based on phase difference is proposed. Through analyzing the distributions of phase difference between adjacent samples of noise and noise-perturbed primary signal, we notice that the mean of phase difference varies from noise when primary signal is present. On this basis, a novel sensing scheme using the accumulation of phase difference as test statistics is formulated. Then the analytical performance of our scheme is derived and its complexity is analyzed. Our proposed scheme is simple, accurate and immune to noise uncertainty. Simulation results show that our scheme outperforms conventional energy detection and can achieve a detection probability of 99% at −7dB signal-to-noise ratio using 500 data samples.