Low earth orbit (LEO) satellite communication system is vital due to its global coverage and low latency. To meet higher data rates, orthogonal frequency division multiplexing (OFDM) technology is recommended for adoption. In this paper, we analyze the nonlinear behavior of high-power amplifier (HPA) at Ka and Q/V frequency bands in wideband OFDM-based satellite communication system. A real satellite PA testing platform is constructed. Experimental results reveal that in wideband OFDM-based systems with high peak-to-average power ratio (PAPR), the conventional power back-off (PBO) method is no longer effective, particularly in mitigating in-band imbalance. Furthermore, we propose a low-complexity digital predistortion (DPD) scheme for satellite communication system. Experimental results demonstrate the robust performance of the proposed DPD.

Large-scale antenna arrays in millimeter wave (mmWave) systems are the cornerstone for next-generation Internet of Things (IoT) infrastructure. However, wideband mmWave systems suffer from frequency-dependent channel responses, known as beam squint. With beam squint effect, the far-field over-the-air (OTA) signal may exhibit frequency selective fading, rendering ineffective digital predistortion (DPD) design based on an OTA feedback structure. Therefore, existing mmWave DPD feedback architectures need to be examined carefully. In this article, we analyze the impact of beam squint on DPD and propose a proper DPD feedback architecture. To the best of our knowledge, this is the first work to address DPD in wideband mmWave systems with beam squint. Our results indicate that, with beam squint, the nonlinearity observed at the far-field OTA side differs from that at the power amplifier (PA) output side. We demonstrate that the far-field OTA received signal is no longer suitable as the feedback signal for DPD estimation. Moreover, we propose an effective DPD scheme for mmWave systems with beam squint. In this scheme, analog beamforming coefficients are fixed for DPD identification. Compared to existing solutions, hardware complexity of the proposed DPD scheme is much reduced. Numerical results validate the effectiveness of the proposed DPD scheme.

Motivated by progress in data-driven supervised learning, semantic communication has witnessed remarkable advancements in improving the efficiency of data transmission under various channel conditions. These advancements typically require a substantial amount of training data for offline training, which is challenging in practical systems. Therefore, in this work, we propose O2SC, a one-shot online-learning framework for semantic communication to achieve adaptive transmission under different channel conditions. Since semantic communication relies on acquired channel state information (CSI), we jointly design the channel estimation and semantic communication processes. Specifically, we introduce a denoising module based on one-shot self-supervised learning, allowing semantic communication systems to adapt to new channel conditions without the need to collect extensive training data. The denoising module is utilized to eliminate noise in the received data samples, using only the data samples themselves. Following this, we further exploit meta-learning to allow the system to quickly adapt to diverse channel conditions, by finding an appropriate initialization for each data sample in a timely way. Simulation results demonstrate that the proposed method achieves performance close to that of supervised learning-based approaches while also providing improved generalizability across different channel conditions.

Phase noise can cause significant performance degradation in multiple-input-multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, especially for high-order data transmission. To mitigate the effect of phase noise on data transmission, pilot-based and blind-based algorithms are widely adopted in the existing works, which suffer from spectral efficiency degradation or formidable computational cost due to the large-scale and time-dependent properties of phase noise. In this paper, we propose an efficient multi-level maximum a posteriori (MMAP)-based blind data detection algorithm to address the phase noise compensation in MIMO-ODFM systems. The proposed algorithm, exploiting the spectral low-dimensional property of phase noise and the approximate message passing (AMP) rule, achieves a near optimal detection performance. The exploitation of low-pass characteristics of phase noise spectrum significantly reduces the computational complexity, and the adoption of AMP principle ensures a linear complexity of the algorithm with respect to the number of antennas and subcarriers. Thus, a good complexity-accuracy trade-off is obtained. Besides, the proposed algorithm is applicable to the scenarios of commonly shared oscillators and independent oscillators. The numerical experiments show that the proposed pilot-free data detection algorithm can achieve superior data transmission performance for channels with strong phase noise at a low complexity.

Hybrid beamforming has been widely considered in millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) system, which can greatly reduce power consumption and hardware cost of data paths. Compared to the fully-connected hybrid beamforming architecture, the sub-connected architecture is more practical for its reduced complexity. However, optimal precoding design for the sub-connected architecture is not straightforward due to the specific block-diagonal structure of analog phase shifter network. Algorithms on fully-digital or fully-connected hybrid beamforming architecture cannot be directly applied to sub-connected architecture. Meanwhile, most existing precoding algorithms in such case can only solve the approximate problem, which results in significant performance loss. In this paper, we study the sum rate maximization problem in the sub-connected architecture. We first relax the objective function and derive a relaxed upper bound of the original problem. Then we propose an algorithm to solve the original problem with a local-optimal solution. Simulation results show that the proposed local-optimal algorithm outperforms the baseline algorithms with better sum rate and energy efficiency performance. Besides, the proposed algorithm also converges quickly and is robust.

In conventional supervised deep learning based channel estimation algorithms, a large number of training samples are required for offline training. However, in practical communication systems, it is difficult to obtain channel samples for every signal-to-noise ratio (SNR). Furthermore, the generalization ability of these deep neural networks (DNN) is typically poor. In this work, we propose a one-shot self-supervised learning framework for channel estimation in multi-input multi-output (MIMO) systems. The required number of samples for offline training is small and our approach can be directly deployed to adapt to variable channels. Our framework consists of a traditional channel estimation module and a denoising module. The denoising module is designed based on the one-shot learning method Self2Self and employs Bernoulli sampling to generate training labels. Besides,we further utilize a blind spot strategy and dropout technique to avoid overfitting. Simulation results show that the performance of the proposed one-shot self-supervised learning method is very close to the supervised learning approach while obtaining improved generalization ability for different channel environments.

The Unmanned aerial vehicle (UAV) assisted Internet of Things (IoT) has attracted substantial attention as it is capable of collecting scattered data to meet the stringent demands of emerging IoT applications. Dispatching UAV to collect data from IoT devices (IoTDs) can significantly improve data freshness, which can be measured by Age of Information (AoI). On the other hand, the quantity of IoTDs increases and existing UAV navigation algorithms for dozens of IoTDs can not be applied to massive IoTDs scenarios directly. In this paper, we investigate the AoI minimization problem in massive IoTDs scenarios. Considering unknown traffic patterns of IoTDs, we reformulate the AoI minimization problem as a Markov decision process (MDP). Then we propose a twin delayed deep deterministic policy gradient (TD3) based UAV navigation algorithm to minimize the average AoI of data collected from IoTDs. Simulation results demonstrate that the proposed algorithm can significantly reduce the average AoI in massive IoTDs scenarios when compared with baseline algorithms.

In the 5th generation (5G) massive machine-type communication (mMTC), random access is the limiting factor of performance because there may be massive user equipments (UEs) up to 1 million unit competing for the access opportunities. Existing random access schemes in the 4th generation (4G) or 5G systems are not able to handle such a large amount of concurrent random access requests. This article proposes a random access scheme based on directional beams, which offers a new spatial degree of freedom to improve the random access performance in 5G mMTC. In this scheme, a cell is divided into Beam Zones in the space domain. UEs in different Beam Zones choose a certain physical random access channel (PRACH) resource with a different probability. When preamble collision occurs, uplink radio resource is allocated to the Beam Zone which has low collision probability. We provide theoretical performance analysis of the proposed scheme using different performance metrics. Comparisons are carried out between the proposed and several existing random access schemes. The proposed scheme can be easily deployed without any modification to the 5G framework. Existing random access optimizing algorithms can also be applied to the proposed scheme.

In the 5th generation (5G) unlicensed band communication, listen-before-talk (LBT) is a key mechanism to allow fair coexistence with other radio access technologies (RATs). With LBT, the user device (UE) needs to transmit a reservation signal (RS) between LBT success and the next slot boundary to avoid competing transmission from other RATs. This scheme, on the other hand, also blocks other UEs from accessing to the base station (BS). The uplink multiple access ability is prominently degraded due to LBT. In this article, an improved LBT scheme is proposed for uplink transmission in the 5G unlicensed band. The RS is enhanced by carrying an indicator of a successful LBT. Other UEs of the same RAT check for enhanced RS in addition to the conventional energy detection. Once the enhanced RS is detected, UE stops LBT, mutes until the next slot boundary, and transmits data in the next slot. We provide theoretical performance analysis of the proposed scheme. Comparisons are carried out between the proposed scheme and other existing schemes. The proposed scheme has better performance than other schemes. In a typical uplink transmission scenario, the proposed scheme improves the average number of UEs with successful LBT by 92% over the existing scheme. In addition, the proposed scheme can be easily deployed complying with the current LBT framework of the 5G unlicensed band.

In this paper, we propose an end-to-end deep learning-based joint transceiver design algorithm for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, which consists of deep neural network (DNN)-aided pilot training, channel feedback, and hybrid analog-digital (HAD) precoding. Specifically, we develop a DNN architecture that maps the received pilots into feedback bits at the receiver, and then further maps the feedback bits into the hybrid precoder at the transmitter. To reduce the signaling overhead and channel state information (CSI) mismatch caused by the transmission delay, a two-timescale DNN composed of a long-term DNN and a short-term DNN is developed. The analog precoders are designed by the long-term DNN based on the CSI statistics and updated once in a frame consisting of a number of time slots. In contrast, the digital precoders are optimized by the short-term DNN at each time slot based on the estimated low-dimensional equivalent CSI matrices. A two-timescale training method is also developed for the proposed DNN with a binary layer. We then analyze the generalization ability and signaling overhead for the proposed DNN based algorithm. Simulation results show that our proposed technique significantly outperforms conventional schemes in terms of bit-error rate performance with reduced signaling overhead and shorter pilot sequences.

In massive multiple-input multiple-output (MIMO) systems, hybrid analog-digital beamforming is an essential technique for exploiting the potential array gain without using a dedicated radio frequency chain for each antenna. However, due to the large number of antennas, the conventional channel estimation and hybrid beamforming algorithms generally require high computational complexity and signaling overhead. In this work, we propose an end-to-end deep-unfolding neural network (NN) joint channel estimation and hybrid beamforming (JCEHB) algorithm to maximize the system sum rate in time-division duplex (TDD) massive MIMO. Specifically, the recursive least-squares (RLS) algorithm and stochastic successive convex approximation (SSCA) algorithm are unfolded for channel estimation and hybrid beamforming, respectively. In order to reduce the signaling overhead, we consider a mixed-timescale hybrid beamforming scheme, where the analog beamforming matrices are optimized based on the channel state information (CSI) statistics offline, while the digital beamforming matrices are designed at each time slot based on the estimated low-dimensional equivalent CSI matrices. We jointly train the analog beamformers together with the trainable parameters of the RLS and SSCA induced deep-unfolding NNs based on the CSI statistics offline. During data transmission, we estimate the low-dimensional equivalent CSI by the RLS induced deep-unfolding NN and update the digital beamformers. In addition, we propose a mixed-timescale deep-unfolding NN where the analog beamformers are optimized online, and extend the framework to frequency-division duplex (FDD) systems where channel feedback is considered. Simulation results show that the proposed algorithm can significantly outperform conventional algorithms with reduced computational complexity and signaling overhead.

Selected mapping (SLM) is an efficient peak-to-average-power-ratio (PAPR) reduction algorithm for orthogonal frequency division multiplexing (OFDM) systems. Conventional SLM requires extra resources for side information transmission. If the side information is transmitted implicitly, significantly high computation overhead is imposed to the receiver at the user equipment (UE) side. In the orthogonal frequency division multiple access (OFDMA) system, the SLM can not be directly applied since the UE does not have access to the signal of other UEs. In this paper, we propose a novel SLM algorithm for the OFDMA system that requires no side information transmission. With the proposed SLM algorithm, each UE can receive its own data without the knowledge of other UEs. The SLM demapping at the UE side is much simplified. Besides, detection of the implicit side information with the proposed algorithm is more robust than existing SLM algorithms. Numerical results validate the theoretical performance of the proposed algorithm.

The intelligent reflecting surface (IRS) technology has been recently proposed as a promising solution to improve the coverage of communication systems. The performance of the IRS-aided communication systems, on the other hand, highly depends on the controllability and consistency of the IRS elements. Previous works mainly discussed the IRS system performance with analog mismatches in the single-user scenario. In this paper, we study the performance of the IRS-aided downlink multi-user multiple-input single-output (MU-MISO) system in the presence of analog mismatches, where the inter-user interference (IUI) may greatly degrade the system performance. The upper bound of general system performance expression is derived. Closed-form expressions can be obtained for some special cases. We show that the system performance is significantly degraded when analog mismatches exist, since the IUI shows up and can not be completely removed.

High data throughput is desired in the wireless communication system design. Rate adaptation is an efficient way to update the data rate in the dynamic wireless environment. Conventional rate adaptation algorithms rely on the feedback of acknowledgment/negative acknowledgment (ACK/NACK) messages or signal to noise ratio (SNR). Existing rate adaptation algorithms can not achieve satisfactory transmission rates in time-varying environments. In this paper, we model the rate selection problem as a multi-armed bandit (MAB) problem and propose an online learning rate adaptation algorithm that learns the channel status from both RSSI and ACK/NACK signals. Compared with existing rate adaptation algorithms, the proposed algorithm can adapt to the time-varying channel better and achieve near-optimal transmission rate performance.

Fog computing is emerging as a promising paradigm to perform distributed, low-latency computation. Efficient computation peer offloading is critical to fully utilize the computational resources in fog networks. In this paper, we consider computation peer offloading problem in a fog network with time-varying stochastic time of arrival tasks and channel conditions. Such time-varying conditions are not available to all fog nodes. In order to minimize the latency of accomplishing arrival tasks, we propose an online algorithm based on combinatorial upper confidence bounds algorithm with two uncertain variables under the non-stationary bandit model. The proposed computation offloading policy is optimized based on historical feedback. The performance of the proposed scheme is validated through numerical simulations.

In wireless sensor networks (WSN s), energy is always precious for sensor nodes. To save energy, censoring is introduced to cut the total number of transmission by only transmitting informative data. This algorithm, however, ignores the energy consumption during the delivery of parameters, which can be significant comparing to the saved power. In this paper, we consider the adaptive censoring from the energy perspective. A distributed censoring algorithm with energy constraint is developed that allows sensor nodes to make autonomous decisions on whether to transmit the incoming data. We show that with the proposed algorithm, the overall energy consumption of the WSN s is reduced, while the performance loss in terms of the estimation error is negligible. Simulation results validate its effectiveness.

In this paper, we look into the issue of intra-cell uplink (UL) pilot orthogonalization and schemes for mitigating the inter-cell pilot contamination with a realistic massive multi-input multi-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system model. First, we show how to align the power-delay profiles (PDP) of different users served by one BS so that the pilots sent within one common OFDM symbol are orthogonal. From the derived aligning rule, we see much more users can be sounded in the same OFDM symbol as their channels are sparse in time. Second, in the case of massive MIMO, we show how PDP alignment can help to alleviate the pilot contamination due to inter-cell interference. We demonstrate that, by utilizing the fact that different paths in time are associated with different angles of arrival (AoA), the pilot contamination can be significantly reduced through aligning the PDPs of the users served by different BSs appropriately. Computer simulations further convince us PDP aligning can serve as the new baseline design philosophy for the UL pilots in massive MIMO.

The emerging market of Internet of things has created great demand for low‐cost, low‐power wireless technologies. Existing IEEE 802.15.4 standard is designed for low‐rate wireless personal area networks (LR‐WPANs). However, current standard does not fully utilise the benefit of the code redundancy. In this study, the authors propose new coding schemes for LR‐WPANs with improved coding gain. They first propose a block code based on extended Bose–Chaudhuri–Hocquenghem (BCH) code that increases the minimum Hamming distance compared with the existing code used in LR‐WPANs. The computational complexity of the encoder and decoder remains about the same. In addition, by applying the extended BCH code directly to LR‐WPANs, the data rate of the system can be increased without sacrificing coding performance. They further propose a tail‐biting convolutional (TBC) code with optimum generator polynomials for LR‐WPANs. The proposed TBC code enjoys significant performance improvement while preserving the effective code rate as well as a low decoding complexity. Simulation results validate the effectiveness of the proposed coding schemes.

In digital predistortion (DPD) implementation, the computational complexity of coefficients estimation of the DPDmodel is a key performance metric. Conventional coefficients estimation algorithms, such as least squares (LS), recursive least squares (RLS), and least mean squares (LMS) cannot achieve a fast convergence with little computation. In this paper, we propose an RLS algorithm with reduced complexity by introducing orthonormal polynomial basis functions. The proposed algorithmis as simple as LMS algorithmyet as efficient as RLS algorithm. Simulation results validate our analysis.