Accurate channel state information (CSI) acquisition is essential for modern wireless systems, which becomes increasingly difficult under large antenna arrays, strict pilot overhead constraints, and diverse deployment environments. Existing artificial intelligence-based solutions often lack robustness and fail to generalize across scenarios. To address this limitation, this paper introduces a predictive-foundation-model-based channel estimation framework that enables accurate, low-overhead, and generalizable CSI acquisition. The proposed framework employs a predictive foundation model trained on large-scale cross-domain data to extract universal channel representations and provide predictive priors with strong cross-scenario transferability. A pilot processing network based on a vision transformer architecture is further designed to capture spatial, temporal, and frequency correlations from pilot observations. An efficient fusion mechanism integrates predictive priors with real-time measurements, enabling reliable CSI reconstruction even under sparse or noisy conditions. Extensive evaluations across diverse configurations demonstrate that the proposed estimator significantly outperforms both classical and data-driven baselines in accuracy, robustness, and generalization capability.

Reconfigurable intelligent surfaces (RISs) enhance communication performance by adjusting the propagation directions of incident signals. However, joint beamforming design requires the acquisition of channel state information, often leading to significant pilot overhead in RIS-assisted systems, particularly when the number of reflective elements is large. In this study, we analyze the characteristics of the cascaded channel and propose a method that combines amplitude correlation with existing structured sparsity. Leveraging these characteristics, we first derive an on-grid channel estimation method, demonstrating the effectiveness of incorporating additional characteristics in cascaded channel estimation. We then extend the proposed algorithm to off-grid channel estimation by refining the coarsely estimated channel using alternating optimization and gradient descent. Furthermore, we adapt the algorithm to enhance estimation accuracy with the support of digital twin (DT) technology, utilizing a few pilots to refine the channel generated by DT. Simulation results show up to a 5 dB improvement in normalized mean squared error compared to state-of-the-art channel estimation algorithms that employ structured sparsity. Additionally, with DT assistance, the proposed algorithm achieves nearly a two-fold performance improvement over traditional algorithms that do not incorporate amplitude correlation and structured sparsity.

The superimposed pilot transmission scheme offers substantial potential for improving spectral efficiency in MIMO-OFDM systems, but it presents significant challenges for receiver design due to pilot contamination and data interference. To address these issues, we propose an advanced iterative receiver based on joint channel estimation, signal detection, and decoding, which refines the receiver outputs through iterative feedback. The proposed receiver incorporates two adaptive channel estimation strategies to improve robustness against discrepancies between the time-varying channel conditions encountered during training and those experienced during testing. First, a variational message passing (VMP) method and its low-complexity variant (VMP-L) are introduced to perform inference without relying on time-domain correlation. Second, a deep learning (DL) based estimator is developed, featuring a convolutional neural network with a despreading module and an attention mechanism to extract and fuse relevant channel features. Extensive simulations under multi-stream and high-mobility scenarios demonstrate that the proposed receiver consistently outperforms conventional orthogonal pilot baselines in both throughput and block error rate. Moreover, over-the-air experiments validate the practical effectiveness of the proposed design. Among the methods, the DL based estimator achieves a favorable trade-off between performance and complexity, highlighting its suitability for real-world deployment in dynamic wireless environments.

The fast time-varying channels in high-speed scenarios impact signal transmission between transceivers and pose challenges to both the accuracy and bandwidth utilization of communication systems. Semantic communication, known for its ability to significantly reduce transmission bandwidth and enhance communication reliability, is especially effective in extreme environments. However, current semantic communication systems lack a comprehensive physical layer design, which limits their ability to achieve optimal performance in rapidly changing conditions. In this paper, we propose an adaptive semantic speech recognition and cloning transmission system with a superimposed pilot (SwitchAC-SIP) tailored for high-speed scenarios to ensure high-quality speech transmission. The system converts speech signals into textual content and speaker timbre features at the transmitter, while a speech cloning model reconstructs the speech at the receiver with a timbre closely resembling the original speaker based on these features, thereby eliminating the need to retrain the speech generation model for different users, ensuring both transmission quality and efficiency. To address the impact of high-speed environments on channel estimation performance, we introduce a superimposed pilot (SIP) in the physical layer. This method superimposes pilots and data across the entire time-frequency grid with a specific power ratio, significantly mitigating the detrimental effects of high-speed conditions on semantic communication systems. Furthermore, to enhance system flexibility in dynamic scenarios, we design a channel-adaptive network that dynamically allocates bandwidth ratios for text and audio semantics based on real-time channel conditions. This adaptive approach prioritizes the protection of critical semantic features according to user requirements. Simulation results demonstrate the substantial improvements in transmission efficiency and accuracy achieved by the proposed system.

Reconfigurable intelligent surfaces (RIS) have emerged as a transformative technology for enhancing wireless coverage and transmission rates while reducing hardware costs and power consumption. This work addresses the limitations of separately optimizing RIS deployment and beamforming by proposing a unified joint deployment and beamforming framework tailored for multi-user multi-input single-output systems. By formulating RIS control as a Markov decision process, we develop a deep reinforcement learning framework that integrates a graph neural network to exploit the inherent topology of wireless communication networks. To reduce the action space and improve learning efficiency, the framework leverages discrete Fourier transform codebooks. Simulation results demonstrate that the proposed approach achieves up to a twofold improvement in weighted sum rate compared to fixed RIS deployment strategies, all while eliminating the need for explicit cascaded channel estimation and accurate channel model.

The superimposed pilot (SIP) transmission scheme shows great potential for improving spectral efficiency in MIMO-OFDM systems. However, it also introduces complex challenges for receiver design, particularly due to pilot contamination and data interference. To address these issues, the joint channel estimation, signal detection, and decoding (JCDD) framework has emerged as a promising solution, utilizing iterative refinement to enhance receiver performance. Despite this, existing JCDD methods either focus heavily on theoretical analysis, often neglecting practical application scenarios, or experience performance limitations due to inherent design flaws. In this paper, we propose an advanced iterative JCDD receiver that effectively mitigates the negative effects of pilot contamination and data interference. Our approach improves traditional linear minimum mean-square error (LMMSE) channel estimation by incorporating state-of-the-art techniques—specifically variational message passing (VMP) and deep learning (DL)—allowing for better adaptation to varying channel conditions. Extensive empirical evaluations demonstrate that our proposed SIP receiver not only surpasses the conventional orthogonal pilot (OP) scheme but also exhibits outstanding adaptability in mismatched channel environments, thanks to the VMP and DL-based improvements.

The space-air–ground integrated network (SAGIN) integrates satellites, unmanned aerial vehicles (UAVs), and terrestrial remote clouds to provide seamless network access and high-volume computing services for remote Internet of Things (IoT) devices, thus alleviating geographic and resource constraints. Existing methods typically focus on the network dynamics while overlooking the comprehensive consideration of device dynamics, namely, the time-varying task performance weights, task sizes, and task processing demands. Moreover, the centralized learning-based offloading schemes often lead to substantial signaling overhead. To bridge these gaps, this article proposes a distributed dynamic task offloading mechanism with game-theoretic multiagent stochastic learning (MASL). Technically, a stochastic game is formulated with each device as a player minimizing its weighted sum cost of latency and energy. We prove the existence of Nash equilibrium (NE) for our proposed game and propose a multiagent entropy-enhanced stochastic learning (MESL) algorithm in a fully distributed manner with no information exchange among IoT devices. By introducing the entropy of decision probability for each device, MESL increases decision dimensions, accelerates convergence, and facilitates optimal strategy achievement. Experimental results show that the MESL algorithm significantly reduces the overall cost and greatly enhances the convergence speed in dynamic SAGIN environments compared to existing algorithms.

The deployment of multiple reconfigurable intelligent surfaces (RIS) is a promising strategy to enhance wireless system performance. However, joint beamforming in multi-RIS assisted systems faces significant challenges due to the increased number of optimization variables, non-convex objective functions, and constraints. In this study, we propose an algorithm based on weighted minimum mean square error optimization and the successive convex approximation algorithm, maximizing the weighted sum rate in a double-RIS assisted downlink multi-user multiple-input single-output system. We also present a general framework for model-driven deep learning that addresses the limitations of existing methods, which often lack flexibility to different channels and suffer from a large training burden due to the high-dimensional action space of deep reinforcement learning (DRL). Initially, we configure the step size in the proposed algorithm as trainable, accelerating convergence. Then, a recurrent neural network generates the step size for iterations, allowing dynamic iteration extension in varying environmental conditions. We enhance the neural network’s self-adaptability by introducing a model-driven DRL algorithm, integrating expert knowledge into the DRL actor network’s design. Simulation results demonstrate up to 30% performance improvement over traditional algorithms, achieved by our model-driven framework. The proposed model-driven DRL shows higher capacity for dynamic extension and rapid adaptation to new environments.

The acquisition of channel state information (CSI) is essential in MIMO-OFDM communication systems. Data-aided enhanced receivers, by incorporating domain knowledge, effectively mitigate performance degradation caused by imperfect CSI, particularly in dynamic wireless environments. However, existing methodologies face notable challenges: they either refine channel estimates within MIMO subsystems separately, which proves ineffective due to deviations from assumptions regarding the time-varying nature of channels, or fully exploit the time-frequency characteristics but incur significantly high computational overhead due to dimensional concatenation. To address these issues, this study introduces a novel data-aided method aimed at reducing complexity, particularly suited for fast-fading scenarios in fifth-generation (5G) and beyond networks. We derive a general form of a data-aided linear minimum mean-square error (LMMSE)-based algorithm, optimized for iterative joint channel estimation and signal detection. Additionally, we propose a computationally efficient alternative to this algorithm, which achieves comparable performance with significantly reduced complexity. Empirical evaluations reveal that our proposed algorithms outperform several state-of-the-art approaches across various MIMO-OFDM configurations, pilot sequence lengths, and in the presence of time variability. Comparative analysis with basis expansion model-based iterative receivers highlights the superiority of our algorithms in achieving an effective trade-off between accuracy and computational complexity.

Deep learning has been widely applied to channel estimation (CE), yielding significant performance improvements. However, existing research primarily focuses on static channel scenarios, leading to substantial performance degradation in dynamic environments. Furthermore, the use of fixed pilot patterns fails to adequately capture channel dynamics, resulting in unnecessary pilot overhead. In this study, we propose a Vision Transformer-based joint pilot design (PD) and CE network (PD-CEViT) for orthogonal frequency division multiplexing (OFDM) systems. The PD module leverages maximum Doppler shift and delay spread information to determine pilot positions, effectively capturing channel variations in dynamic scenarios. To further improve CE accuracy and robustness across diverse environments, the coarse CE from the PD module is passed to a CE module that utilizes a Vision Transformer (ViT), forming the joint PD-CEViT structure. Additionally, we introduce a pilot number switch network, named SwitchPD-CEViT, which dynamically adjusts between different PD-CEViT configurations based on the current channel conditions. This strategy balances network performance and pilot overhead, accommodating varying pilot requirements across different scenarios. Simulation results demonstrate that our proposed structure more effectively tracks channel variations compared to fixed pilot patterns. Even under challenging conditions with large Doppler shifts and delay spreads, our method significantly outperforms traditional and deep learning approaches in terms of mean square error (MSE) performance. Moreover, the integration of channel information further enhances estimation performance and robustness. Meanwhile, SwitchPD-CEViT achieves superior CE performance with reduced pilot overhead by efficiently managing pilot utilization.

Massive multiple-input multiple-output (MIMO) technology has significantly enhanced spectral and power efficiency in cellular communications and is expected to further evolve towards extra-large-scale MIMO. However, centralized processing for massive MIMO faces practical obstacles, including excessive computational complexity and a substantial volume of baseband data to be exchanged. To address these challenges, decentralized baseband processing has emerged as a promising solution. This approach involves partitioning the antenna array into clusters with dedicated computing hardware for parallel processing. In this paper, we investigate the gradient-based Markov chain Monte Carlo (MCMC) method—an advanced MIMO detection technique known for its near-optimal performance in centralized implementation—within the context of a decentralized baseband processing architecture. This decentralized design mitigates the computation burden at a single processing unit by utilizing computational resources in a distributed and parallel manner. Additionally, we integrate the mini-batch stochastic gradient descent method into the proposed decentralized detector, achieving remarkable performance with high efficiency. Simulation results demonstrate substantial performance gains of the proposed method over existing decentralized detectors across various scenarios. Moreover, complexity analysis reveals the advantages of the proposed decentralized strategy in terms of computation delay and interconnection bandwidth when compared to conventional centralized detectors.

Along with the prosperity of generative artificial intelligence (AI), its potential for solving conventional challenges in wireless communications has also surfaced. Inspired by this trend, we investigate the application of the advanced diffusion models (DMs), a representative class of generative AI models, to high dimensional wireless channel estimation. By capturing the structure of multiple-input multiple-output (MIMO) wireless channels via a deep generative prior encoded by DMs, we develop a novel posterior inference method for channel reconstruction. We further adapt the proposed method to recover channel information from low-resolution quantized measurements. Additionally, to enhance the over-the-air viability, we integrate the DM with the unsupervised Stein’s unbiased risk estimator to enable learning from noisy observations and circumvent the requirements for ground truth channel data that is hardly available in practice. Results reveal that the proposed estimator achieves high-fidelity channel recovery while reducing estimation latency by a factor of 10 compared to state-of-the-art schemes, facilitating real-time implementation. Moreover, our method outperforms existing estimators while reducing the pilot overhead by half, showcasing its scalability to ultra-massive antenna arrays.

Reconfigurable intelligent surfaces (RIS) can improve signal propagation environments by adjusting the phase of the incident signal. However, optimizing the phase shifts jointly with the beamforming vector at the access point is challenging due to the non-convex objective function and constraints. In this study, we propose an algorithm based on weighted minimum mean square error optimization and power iteration to maximize the weighted sum rate (WSR) of a RIS-assisted downlink multi-user multiple-input single-output system. To further improve performance, a model-driven deep learning (DL) approach is designed, where trainable variables and graph neural networks are introduced to accelerate the convergence of the proposed algorithm. We also extend the proposed method to include beamforming with imperfect channel state information and derive a two-timescale stochastic optimization algorithm. Simulation results show that the proposed algorithm outperforms state-of-the-art algorithms in terms of complexity and WSR. Specifically, the model-driven DL approach has a runtime that is approximately 3% of the state-of-the-art algorithm to achieve the same performance. Additionally, the proposed algorithm with 2-bit phase shifters outperforms the compared algorithm with continuous phase shift.

Accurately detecting symbols transmitted over multiple-input multiple-output (MIMO) wireless channels is crucial in realizing the benefits of MIMO techniques. However, optimal MIMO detection is associated with a complexity that grows exponentially with the MIMO dimensions and quickly becomes impractical. Recently, stochastic sampling-based Bayesian inference techniques, such as Markov chain Monte Carlo (MCMC), have been combined with the gradient descent (GD) method to provide a promising framework for MIMO detection. In this work, we propose to efficiently approach optimal detection by exploring the discrete search space via MCMC random walk accelerated by Nesterov’s gradient method. Nesterov’s GD guides MCMC to make efficient searches without the computationally expensive matrix inversion and line search. Our proposed method operates using multiple GDs per random walk, achieving sufficient descent towards important regions of the search space before adding random perturbations, guaranteeing high sampling efficiency. To provide augmented exploration, extra samples are derived through the trajectory of Nesterov’s GD by simple operations, effectively supplementing the sample list for statistical inference and boosting the overall MIMO detection performance. Furthermore, we design an early stopping tactic to terminate unnecessary further searches, remarkably reducing the complexity. Simulation results and complexity analysis reveal that the proposed method achieves exceptional performance in both uncoded and coded MIMO systems, adapts to realistic channel models, and scales well to large MIMO dimensions.

Deep learning (DL) has been widely utilized for channel estimation and has resulted in significant performance improvements. However, most existing research only performs training and testing in relatively static scenarios, leading to a serious deterioration in dynamic scenarios. In this paper, we propose a robust channel estimator for orthogonal frequency-division multiplexing (OFDM) systems in dynamic scenarios called channel estimator Vision Transformer (CE-ViT) based on attention mechanism. We perform a patch embedding operation to process data in both the time and frequency domains, addressing the limitations of the attention mechanism in extracting 2D correlations. Additionally, we introduce tokens that reflect channel characteristics into the network to enhance the robustness. Experimental results show that CE-ViT outperforms the state-of-the-art DL-based methods. Moreover, the addition of tokens significantly improves the performance of CE-ViT in dynamic channel conditions.

Video surveillance in intelligent transportation systems is advancing rapidly, with video analytics technology being used to enhance the security of the Internet of Vehicles (IoV) system. However, the sheer volume of video data from cameras and the computational intensity of video analysis pose significant challenges to the IoV network. To address this, mobile edge computing (MEC) has been introduced to offload video tasks from cameras to mobile edge servers/groups formed by vehicles. However, the resource-constrained nature of edge servers and vehicle groups necessitates the design of effective offloading strategies. Additionally, ensuring the security of user data during transmission and computation is a pressing issue. Moreover, the heterogeneous devices in the IoV system may be reluctant to participate in the collaborative processing of video tasks due to mistrust and lack of incentives. To tackle these challenges, we propose a cooperative computing offloading and resource allocation framework that integrates blockchain and MEC to provide secure and low-latency computing offloading services for the IoV system. We also design an efficient incentive mechanism to promote the collaborative processing of video tasks. Our framework formulates computing offloading and resource allocation as a joint optimization problem to maximize the system revenue, and we propose an algorithm based on the alternating direction method of multipliers (ADMM) to solve the distributed optimization problem with fast convergence and low complexity. Simulation results demonstrate that compared to the typical baselines, our scheme can achieve the maximum system revenue and effectively reduce the system delay.

Optimal detection of symbols transmitted over multiple-input multiple-output (MIMO) wireless channels is known to entail exponentially increasing complexity with MIMO dimensions, making it impractical for large-scale systems. Recently, Markov chain Monte Carlo (MCMC) has been combined with the gradient descent (GD) method to create a promising machine learning solution to this issue. This paper proposes a novel algorithm for approaching optimal detection via MCMC random walk accelerated by Nesterov's gradient method, efficiently exploring the discrete search space for MIMO detection. Our proposed method utilizes multiple GDs per random walk and guarantees high sampling efficiency while mitigating the complexity associated with matrix inversions. Simulation results and complexity analysis reveal that the proposed method achieves near-optimal performance and scales effectively to large MIMO dimensions.

With the widespread adoption of resource-intensive mobile applications, mobile edge computing (MEC) has emerged as a solution to enhance the computational power of mobile user equipments (UEs) and minimize their computational delay by offloading tasks to edge servers (ESs). This paper delves into the computing offloading challenge for multiple UEs in dynamic Internet of Things (IoT) networks with partial information-sharing. In such settings, the transmission bandwidth for each UE varies over time, and they can only access the historical data of their peers. Since UEs are self-interested in offloading computational tasks to ESs that possess limited computational resources, we model the UEs’ offloading decision-making in this dynamic, privacy-bound scenario as a game. Subsequently, this game is further formulated as a multi-agent Partially Observable Markov Decision Process (POMDP). To address the POMDP and attain a near-optimal Nash equilibrium (NE) of the structured game, we introduce an algorithm grounded in multi-agent reinforcement learning, integrating Differentiable Neural Computer and Advantage Actor-Critic framework (abbreviated as DNA). Through this method, each UE autonomously decides the optimal computing offloading strategy based on its game history, without obtaining the detailed offloading policies of other UEs. Experimental outcomes reveal that DNA surpasses the state-of-the-art benchmark methods by at least 8.3% in computing offloading utilities and 3.98% in convergence rate, highlighting its effectiveness in a dynamic IoT environment with partial information-sharing between UEs.

The use of a hybrid analog-digital architecture that connects one RF chain to multiple antennas through phase shifters is an energy-efficient solution for multiuser multiple-input multiple-output (MU-MIMO) systems. However, designing the hybrid precoder is challenging due to its nonconvex objective functions and constraints. Existing algorithms struggle with high computational complexity or poor performance, which often result from slow or no convergence. This study proposes a solution that leverages model-driven deep learning (DL) to maximize the spectral efficiency of MU-MIMO systems through hybrid precoding. The optimization problem is first transformed into a weighted minimum mean square error optimization. Then, it is combined with manifold optimization and DL to improve performance and simplify the process. The algorithm is designed to be robust in changing environments and utilizes DL to address imperfect channel state information. Simulation results show that the proposed method outperforms existing algorithms, is robust in changing system parameters, and can even outperforms fully digital precoding with the same number of antennas.

Energy harvesting (EH)-enabled cellular Internet of Things (IoT) is a promising solution to handle the charging and accessing of massive IoT nodes. However, limited by the high-frequency band of future 5G, the radius of the small base station (SBS) is reduced, hence greatly increasing the cost of the network operators (NOs). In this article, we consider the joint cell association, cell sleeping (CS), and incentive decision problem for EH-aided cellular IoT with full-duplex (FD) self-backhauls. We formulate a Stackelberg game to investigate the coordination between the utilities of NO and energy transmitters (ETs), where both the features of FD self-backhauls and CS are introduced to reduce the expense of NO. We then propose an alternative direction algorithm to solve the equilibrium of the game efficiently, where the relationship of the formulated constraints and variables are utilized to transform the original problem into two subproblems. We propose a two-level Lagrangian relaxation to solve the first subproblem, while the other is proved to be convex and solved by an efficient iteration. Simulation results demonstrate the benefits of our algorithm in utility improvement and expense reduction. Moveover, it shows that our algorithm can obtain high efficiency by adjusting the tradeoff between the number of active SBS and transmitting power of ETs according to the network deployment.

Algorithm unfolding, which provides a systematic connection between conventional model-based algorithms and modern data-based deep learning, has exhibited great empirical success for efficiently balancing the performance and complexity of multiple-input and multiple-output (MIMO) detectors. However, existing unfolding-based MIMO detectors have difficulties adapting to the high discrepancy in channel and noise conditions. In this study, we present a novel unfolding-based framework for MIMO detectors, which can automatically determine internal parameters of an unfolding-based MIMO detector to adapt to the varying conditions. A key part of our approach is to develop a hypernetwork that can effectively learn to generate the internal parameters in the sophisticated expectation propagation-based MIMO detector. In particular, we design long short-term memory-based hypernetwork to ensure the flexibility of the layers of the unfolded algorithm. The proposed framework is also extended to a coded MIMO turbo receiver to adapt to the different feedback beliefs from the decoder. Numerical results demonstrate that the proposed MIMO detectors have excellent adaptation capability to different channel environments and noise levels. Compared with the existing unfolded algorithm that is an optimal reference, the proposed framework avoids frequent retraining and presents the nearly optimal performance in uncoded and coded MIMO systems. An over-the-air platform is presented as well to demonstrate the significant robustness of the proposed receivers in practical deployment.

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM), a fundamental transmission scheme, promises high throughput and robustness against multipath fading. However, these benefits rely on the efficient detection strategy at the receiver and come at the expense of the extra bandwidth consumed by the cyclic prefix (CP). We use the iterative orthogonal approximate message passing (OAMP) algorithm in this paper as the prototype of the detector because of its remarkable potential for interference suppression. However, OAMP is computationally expensive for the matrix inversion per iteration. We replace the matrix inversion with the conjugate gradient (CG) method to reduce the complexity of OAMP. We further unfold the CG-based OAMP algorithm into a network and tune the critical parameters through deep learning (DL) to enhance detection performance. Simulation results and complexity analysis show that the proposed scheme has significant gain over other iterative detection methods and exhibits comparable performance to the state-of-the-art DL-based detector at a reduced computational cost. Furthermore, we design a highly efficient CP-free MIMO-OFDM receiver architecture to remove the CP overhead. This architecture first eliminates the intersymbol interference by buffering the previously recovered data and then detects the signal using the proposed detector. Numerical experiments demonstrate that the designed receiver offers a higher spectral efficiency than traditional receivers. Finally, over-the-air tests verify the effectiveness and robustness of the proposed scheme in realistic environments.

Orthogonal frequency division multiplexing (OFDM) has been widely applied in many wireless communi- cation systems. The artificial intelligence (AI)-aided OFDM receivers are currently brought to the forefront to replace and improve the traditional OFDM receivers. In this paper, we first compare two AI-aided OFDM receivers, namely, data-driven fully connected deep neural network and model-driven ComNet, through extensive simulation and real-time video transmission using a 5G rapid prototyping system for an over-the-air (OTA) test. We find a performance gap between the simulation and the OTA test caused by the discrepancy between the channel model for offline training and the real environment. We develop a novel online training system, which is called SwitchNet receiver, to address this issue. This receiver has a flexible and extendable architecture and can adapt to real channels by training only several parameters online. From the OTA test, the AI-aided OFDM receivers, especially the SwitchNet receiver, are robust to OTA environments and promising for future communication systems. At the end of this paper, we discuss potential challenges and future research inspired by our initial study in this paper.

Deep neural networks (NNs) have exhibited considerable potential for efficiently balancing the performance and complexity of multiple-input and multiple-output (MIMO) detectors. However, existing NN-based MIMO detectors are difficult to be deployed in practical systems because of their slow convergence speed and low robustness in new environments. To address these issues systematically, we propose a receiver framework that enables efficient online training by leveraging the following simple observation: although NN parameters should adapt to channels, not all of them are channel-sensitive. In particular, we use a deep unfolded NN structure that represents iterative algorithms in signal detection and channel decoding modules as multi layer deep feed forward networks. An expectation propagation (EP) module, called EPNet, is established for signal detection by unfolding the EP algorithm and rendering the damping factors trainable. An unfolded turbo decoding module, called TurboNet, is used for channel decoding. This component decodes the turbo code, where trainable NN units are integrated into the traditional max-log-maximuma posterioridecoding procedure. We demonstrate that TurboNet is robust for channels and requires only one off-line training. Therefore, only a few damping factors in EPNet must be re-optimized online. An online training mechanism based on meta learning is then developed. Here, the optimizer, which is implemented by long short-term memory NNs, is trained to update damping factors efficiently by using a small training set such that they can quickly adapt to new environments. Simulation results indicate that the proposed receiver significantly outperforms traditional receivers and that the online learning mechanism can quickly adapt to new environments. Furthermore, an over-the-air platform is presented to demonstrate the significant robustness of the proposed receiver in practical deployment.

This paper presents a novel model-driven deep learning (DL) architecture, called TurboNet, for turbo decoding that integrates DL into the traditional max-log-maximuma posteriori(MAP) algorithm. The TurboNet inherits the superiority of the max-log-MAP algorithm and DL tools and thus presents excellent error-correction capability with low training cost. To design the TurboNet, the original iterative structure is unfolded as deep neural network (DNN) decoding units, where trainable weights are introduced to the max-log-MAP algorithm and optimized through supervised learning. To efficiently train the TurboNet, a loss function is carefully designed to prevent tricky gradient vanishing issue. To further reduce the computational complexity and training cost of the TurboNet, we can prune it into TurboNet+. Compared with the existing black-box DL approaches, the TurboNet+ has considerable advantage in computational complexity and is conducive to significantly reducing the decoding overhead. Furthermore, we also present a simple training strategy to address the overfitting issue, which enable efficient training of the proposed TurboNet+. Simulation results demonstrate TurboNet+’s superiority in error-correction ability, signal-to-noise ratio generalization, and computational overhead. In addition, an experimental system is established for an over-the-air (OTA) test with the help of a 5G rapid prototyping system and demonstrates TurboNet’s strong learning ability and great robustness to various scenarios.

Channel estimation and signal detection are very challenging for an orthogonal frequency division multiplexing (OFDM) system without cyclic prefix (CP). In this article, deep learning based on orthogonal approximate message passing (DL-OAMP) is used to address these problems. The DL-OAMP receiver includes a channel estimation neural network (CE-Net) and a signal detection neural network based on OAM-P, called OAMP-Net. The CE-Net is initialized by the least square channel estimation algorithm and refined by minimum mean-squared error (MMSE) neural network. The OAMP-Net is established by unfolding the iterative OAMP algorithm and adding some trainable parameters to improve the detection performance. The DL-OAMP receiver is with low complexity and can estimate time-varying channels with only a single training. Simulation results demonstrate that the bit-error rate (BER) of the proposed scheme is lower than those of competitive algorithms for high-order modulation.

In this paper, we propose a game theoretic approach for joint offloading and resource allocation optimization (JORAO) problem in mobile edge computing (MEC) system. This study not only investigates offloading strategy, but also considers cloud and wireless resource allocation. Specially, the concern of the JORAO problem is to minimize the energy consumption and monetary cost from mobile terminals' perspective. However, the JORAO problem is non-convex and NP hard. Therefore, it is formulated as a JORAO game. The existence of Nash equilibrium (NE) is proved for it. To obtain NE, we also concentrate on cloud and wireless resource allocation algorithm (CWRAA), which is the sub- algorithm of the JORAO game. For the CWRAA, on one hand, we take consideration of OFDM sub-channels allocation and uplink power allocation in radio access networks (RAN). On the other hand, the computation resource allocation in MEC is studied. Simulation results show that the distributed JORAO game algorithm can nearly minimize the total cost of all mobile terminals (MTs) with low complexity. In addition, the energy consumption and completion time are less when the size of data becomes larger compared with existing algorithms.

Energy efficiency (EE) of wireless communications has attracted growing attention in recent years. In this paper, we focus on the EE optimization in cognitive radio networks (CRN) from a perspective of designing MAC frame structure, namely scheduling the sensing-then-transmission time slots. Considering secondary users adopting channel handoff to avoid collisions with primary users, the EE of CRN is modeled, which is a function of the sensing time and the transmission time. Under the constraint of protecting primary users sufficiently, an EE optimization problem is formulated, and the energy- efficient MAC frame is thus obtained by solving the problem. Simulation results confirm theoretical analysis, i.e., the EE of CRN depends on the sensing capability and interference constraint. Moreover, the proposed optimization method for cognitive MAC frame can enhance the EE of CRN effectively.

In this study, the authors focus on the tradeoff between spectrum sensing and energy efficiency of cognitive radio networks (CRN). Considering two interference‐avoidance schemes, that is, channel handoff and stop‐and‐wait, the authors, respectively, model the energy efficiency (EE) of CRN as the mean of the throughput‐to‐power ratio. Research shows that stop‐and‐wait scheme is a special case of channel handoff from an EE perspective. Based on the proposed EE model, the authors build up an EE optimisation problem under the constraint of the sensing quality, and formulate the sensing‐energy efficiency tradeoff (SET) for CRN. The similarity and difference between the SET and the sensing‐throughput tradeoff are also investigated. Simulation analysis confirms that there is indeed an optimal sensing time to make the EE maximum, and it is larger than that for maximum throughput. Another interesting result is that the EE and the throughput of CRN can be enhanced together by optimising MAC frame structure in combination with sensing bandwidth adjustment and power control. Our work provides some insights for green CRN in view of MAC frame optimisation.

Sensing-throughput tradeoff is involved in opportunistic spectrum access (OSA). At the physical layer it's expressed as the tradeoff between false alarm and misdetection, while at the MAC layer it's between maximizing the throughput of secondary users and reducing collisions with primary users. To balance both tradeoffs together drives the optimization of MAC frame structure for OSA. Since channel handoff results in a time overhead for MAC frame, it's first researched by modeling OSA dynamics in the paper. Three handoff cases and their probabilities are deduced, which lead to three application scenarios of MAC frame. Thus the throughput model of secondary network involving channel handoff is proposed. By balancing the misdetection and false alarm probabilities and maximizing the throughput of secondary network subject to the sensing quality and collision avoidance constraints, the optimal sensing time and frame duration are deduced as closed forms. The characteristics of the optimal MAC frame structure are researched as well. Theoretical and simulated results disclose the impacts of channel handoff and spectrum sensing on MAC frame structure and the achievable throughput of secondary network, which provide an insight for OSA design and improvement.