Polarization shift keying (PolarSK) has been demonstrated as a spectrally efficient single-radio-frequency (RF) multiple-input multiple-output (MIMO) technique. We herein further reveal a pivotal characteristic of PolarSK: The inherent robustness to hardware impairments, which provides a theoretical guarantee for practical implementation. Specifically, we propose the first performance degradation evaluation framework for the PolarSK system under transmitter hardware impairments, which incorporates typical hardware impairments including in-phase/quadrature (I/Q) imbalance, nonlinearity, and phase noise. We derive the analytical average bit error probability (ABEP) expression of PolarSK under hardware impairments, along with the asymptotic ABEP floor. The theoretical derivations on both the approximate ABEP and the asymptotic floor are verified in 3GPP channels. Compared to state-of-the-art modulation systems in single-RF MIMO, the PolarSK system exhibits superior robustness against ABEP degradation under non-ideal hardware conditions, achieving up to 8 times lower ABEP.

The evolution of 6G wireless networks requires robust and adaptive communication systems that can handle dynamic indoor environments. Accurate prediction of the terahertz (THz) channel is a key enabler of this adaptability, enabling proactive decisions such as beamforming and handover. Because traffic levels (i.e., user densities) fluctuate, the underlying channel statistics change over time, resulting in concept drift that can deteriorate the performance and generalization ability of deep learning (DL) models if they are tested against mismatched conditions (i.e., on a traffic level other than the one used for training). This paper introduces a novel framework for generalizable THz channel prediction, enabled by fusing environmental sensing with AI-assisted wireless communication to mitigate concept drift and maintain generalization. First, we investigate the use of DL-based channel prediction models tailored to specific traffic levels—light, moderate, and dense—and evaluate their performance under mismatched conditions. Results show up to 51% performance deterioration when models are exposed to mismatched traffic scenarios. To mitigate this issue, a traffic-aware channel prediction framework is proposed, comprising three stages: people counting using sensing technologies; quantization of the user count into traffic levels; and dynamic selection of the corresponding DL model. Simulation results demonstrate that integrating accurate sensing technologies, particularly vision-based systems, significantly reduces prediction deterioration to as low as 4%. The proposed framework’s adaptability ensures reliable channel prediction by aligning model selection with real-time traffic conditions, which highlights the potential of fusing environmental sensing with AI-assisted wireless communication to enhance the robustness of future 6G networks.

The maximum indoor wireless network performance is determined by building design, making the evaluation of building wireless performance (BWP) essential during the design phase. In this study, we aim to propose a novel BWP evaluation framework that, for the first time, analyzes the influence of user equipment (UE) aperture antenna. The framework provides an analytical examination of how UE aperture antenna impacts BWP metrics, specifically interference gain (IG) and power gain (PG).We begin by refining the path gain model to incorporate the effects of aperture antenna, which allows us to derive analytical expressions for IG and PG. To validate the accuracy of the model, Monte Carlo simulations are first conducted. Subsequently, the study concludes by identifying the underlying causes of the discrepancy between two models, one accounting for aperture antenna effects and the other not. Taking the 1 GHz band as an example, numerical results demonstrate that the existing model underestimates IG by more than 50% due to the neglect of aperture antenna. At 28 GHz, the underestimation becomes more pronounced, with discrepancies increasing by a factor of 3 to 10. In contrast, the PG shows only minor variations between the two analytical models with peak deviations reaching up to 50%. These deviations, resulting from the omission of aperture considerations, may critically compromise key metrics of BWP. Therefore, our proposed model, which factors in aperture effects, is essential for accurately assessing BWP during the building design phase.

The concept of integrating reconfigurable intelligent surfaces (RISs) into visible light communications (VLC) has already been realized through metasurfaces such as adjustable-angle reflectors or liquid crystal tiles with variable refractive indices. However, current research neglects the power consumption and control signaling overhead required to drive these RIS tiles, potentially resulting in the overheads of RIS outweighing its benefits. Considering that some RISs will be blocked in indoor mobile environments and the blockage patterns will change over time, which results in concept drift for management strategy, this paper proposes a dynamic RIS tile exclusion strategy adaptive to indoor mobility. Such an RIS controller deactivates less efficient RIS tiles based on channel conditions, which reduces signaling and power consumption costs, thereby, enhancing the overall system efficiency. Since this decision problem is highly non-convex and non-linear, and involves RIS-assisted time-varying visible light channels that cannot be characterized by general mathematical models, this paper proposes a maximum entropy deep reinforcement learning-based method to optimize the strategy. In the validation scenarios, compared to the tile exclusion strategy at fixed time intervals, the proposed adaptive strategy improves the RIS utilization efficiency by over 100%. Moreover, it only takes less than$53.55~\mu $s to execute the trained strategy on a low-cost micro-controller unit. The generalization ability of the learned strategy is also proven as no transfer learning or adaption is needed when facing a new environment.

This paper explores secret key generation in 5G and beyond LiFi networks using visible light in the downlink and infrared in the uplink. Unlike the existing works, we focus on a realistic indoor environment with multi-user mobility. Given inaccuracies in high-frequency channel models, we introduce the first deep learning model that combines the channel probing and quantization phases to generate initial secret keys with a minimal key disagreement rate (KDR) of 16% between the uplink and downlink, leading to a key generation rate (KGR) of 79 bits/s after information reconciliation. We show that LiFi channel statistics suffer from concept drifts with user density changes in the room. This increases the KDR by 28% - 44% and the generated keys fail to pass the NIST randomness tests. As a countermeasure, we introduce a voting ensemble model that mitigates concept drifts, maintaining a stable 16% KDR, 79 bits/s KGR, and passing NIST tests, despite the varying user densities.

Accurate channel prediction using deep learning (DL) algorithms can address the challenges of terahertz (THz) propagation, such as atmospheric absorption and object scattering, by enabling proactive handover and beamforming. However, indoor environments are inherently dynamic, with factors like occupancy level variations causing the channel characteristics to change over time. This phenomenon, known as concept drift, can severely degrade the DL model performance used in channel prediction. This paper investigates the impact of indoor occupancy level variations on the generalization ability of state-of-the-art DL models for THz channel prediction. We identify three distinct occupancy levels (low, medium, and high) within the THz indoor channel. Our results demonstrate that the state-of-the-art DL models exhibit limited generalization capabilities, with performance deterioration in prediction accuracy ranging from 4−62%. We propose a robust two-stage framework to mitigate concept drift in THz channel prediction. The first stage predicts the indoor occupancy level from the THz wireless signal, which is a multi-class classification problem. Due to the reoccurring concept of occupancy levels, the second stage contains a pool of models in a sleeping mode based on a hybrid convolutional neural network (CNN) long-short-term memory (LSTM) architecture. One of these DL expert models is activated for channel prediction based on the occupancy level predicted from the previous stage. Our framework demonstrates superior generalization by limiting the performance deterioration from 62% due to concept drift to ≤ 9%. This represents an 85% reduction in performance deterioration compared to the existing state-of-the-art DL models.

This paper explores the vulnerability of wireless secret key generation (WSKG) to eavesdropping in a dynamic indoor light-fidelity (LiFi) network. It analyzes the channel impulse response (CIR) similarities of two moving user equipments (UEs) across scenarios with two, four, and eight UEs. We observe that as the number of UEs increases, the similarity in CIR also rises, due to the proximal movement patterns among UEs. Specifically, the similarity rate peaks at 70% when eight UEs enter the room; it then drops to 24% during the wandering phase and rises again to 80% as UEs exit the room. Consequently, an eavesdropper among the eight UEs is able to generate 27% of a legitimate UE’s secret key, it significantly reduces the key’s complexity, decreasing the number of possible keys that need to be tested to break the encryption and making it easier to predict the remainder of the key. To mitigate this issue, we introduce a novel approach that utilizes a generative adversarial network (GAN) to artificially manipulate the CIR, thereby reducing the effectiveness of eavesdropping by adding noise into the observed CIR. This method effectively reduces the CIR similarity to a negligible 1%, thus ensuring the integrity of WSKG against eavesdropping threats.

Reconfigurable intelligent surfaces (RIS) experience considerable control overhead, particularly problematic in mobile multi-user programmable wireless environments (PWEs). This paper presents a strategy that dynamically excludes shadowed RIS tiles from supporting non-line-of-sight mmWave communications, reducing overheads and enhancing RIS usage efficiency. Spatio-temporal variations caused by crowd mobility necessitate the dynamic adaption of this strategy. The primary challenge lies in identifying optimal occasions for updating RIS tile exclusion decisions, which must strike a balance between improving achievable channel gain (performance) and power consumption as well as controlling overhead (cost) associated with decision updates. Given the absence of a general mmWave channel model, this paper applies deep reinforcement learning (DRL) for managing the RIS exclusion strategy. DRL caters to the susceptibility of mmWave channels to concept drift, where spatio-temporal variations in crowd mobility alter the probability distribution of RIS channel gains and outages. To counter concept drift and generate a universal RIS exclusion strategy for any indoor mmWave environment, we propose an adaptive exclusion update mechanism powered by DRL. This mechanism utilizes hierarchical decision decomposition, reward signal embedding, and a fusion of concept drift and temporal features due to crowd mobility, enabling efficient adaptation to environmental changes. Extensive cross-validation confirms the agent’s impressive generalization ability, directly applicable to varied environments. This adaptive mechanism, despite containing only 2, 300 learnable parameters, achieves more than a two-fold increase in efficiency relative to static exclusion timing methods. Furthermore, decision execution, based on a low-cost RIS controller, only takes a few tens of nanoseconds, showcasing practicality and efficiency.

Recent efforts have promoted programmable wireless environments (PWEs) to enhance the reception quality in high-frequency bands via reconfigurable intelligent surfaces (RISs). However, relevant research efforts are limited to setups with stationary users. This paper shows that crowd mobility in indoor PWEs induces spatio-temporal shadows on the surfaces, resulting in spatio-temporal sparsity in channel gains due to signal blockages. This overlooked aspect impacts the operation strategy of PWEs as the shadowed RIS tiles would contribute to the overheads while offering almost no improvement to the reception quality. Hence, this paper proposes an optimal strategy that excludes the shadowed tiles, which maximizes the utilization efficiency of RISs while minimizing the overheads. Since signal blockage is tied with the details of user mobility, a general model does not exist to identify such shadowed tiles for exclusion. Hence, we follow a data-driven approach that capitalizes on a realistic indoor mobility model and ray-tracing to generate the channel data. However, conventional ray-tracing presents high complexity that hinders data generation. So, we propose an approach to identify the shadow regions with a nine-order of magnitude reduction in complexity to efficiently generate the channel data. Furthermore, we present two exclusion strategies that offer guaranteed and best-effort quality-of-service support, and each can identify the tiles to be excluded via a search method with a complexity of$\mathcal {O}(N)$for$N$tiles. The results indicate that the proposed strategies reduce the overheads by$45-50\%$while maintaining optimal service quality in various environments, operation frequencies, and user and access point density.

The mmWave WiGig frequency band can support high throughput and low latency emerging applications. In this context, accurate prediction of channel gain enables seamless connectivity with user mobility via proactive handover and beamforming. Machine learning techniques have been widely adopted in literature for mmWave channel prediction. However, the existing techniques assume that the indoor mmWave channel follows a stationary stochastic process. This paper demonstrates that indoor WiGig mmWave channels are non-stationary where the channel’s cumulative distribution function (CDF) changes with the user’s spatio-temporal mobility. Specifically, we show significant differences in the empirical CDF of the channel gain based on the user’s mobility stage, namely, room entering, wandering, and exiting. Thus, the dynamic WiGig mmWave indoor channel suffers from concept drift that impedes the generalization ability of deep learning-based channel prediction models. Our results demonstrate that a state-of-the-art deep learning channel prediction model based on a hybrid convolutional neural network (CNN) long-short-term memory (LSTM) recurrent neural network suffers from a deterioration in the prediction accuracy by 11–68% depending on the user’s mobility stage and the model’s training. To mitigate the negative effect of concept drift and improve the generalization ability of the channel prediction model, we develop a robust deep learning model based on an ensemble strategy. Our results show that the weight average ensemble-based model maintains a stable prediction that keeps the performance deterioration below 4%.

This paper investigates wireless secret key generation in 5G+ indoor LiFi networks operating in the visible light band in the downlink and the infrared band in the uplink. Unlike the existing research, this paper aims to characterize the impact of indoor user mobility on the ability to generate identical secret keys between the user and the base station. Hence, we first generate mobility traces that reflect realistic human behavior in an indoor setup to capture the sensitivity of the optical channels to random blockage and transceiver-misorientation events associated with user mobility. Next, we generate the corresponding channel impulse response and adopt guard band quantization and information reconciliation to extract identical uplink and downlink keys. Then, we examine a set of spatial and temporal features related to the channel probing rate, key disagreement rate, and key generation rate, along with their statistical distributions. Further, we study the randomness of the key following the NIST randomness tests. Our study demonstrated that the handover strategy significantly impacts the randomness of the key and its generation rate.

Next-generation wireless networks rely on unused high spectrum bands, such as mmWave and visible light, to support high-speed communications. However, such bands suffer from frequent outages of line-of-sight (LoS) signals, which deteriorates the communication's quality. Thus, recent efforts have promoted programmable wireless environments (PWEs), which employs reconfigurable intelligent surfaces (RIS) that can redirect non-LoS (NLoS) signals towards the user to enhance reception quality. Unfortunately, these efforts are limited so far to setups with stationary users. We show in this paper that crowd mobility induces spatio-temporal sparseness across the wireless channels in an indoor layout. As a result, shadowed regions will exist on the walls of the room due to signal blockage. Hence, random allocations of passive RIS tiles across the room will not result in the utmost gains and will unnecessarily complicate the communication system. Therefore, we propose an efficient data driven approach that capitalizes on a realistic indoor mobility model to allocate RIS tiles across the room in a way that maximizes their exploitation efficiency. Our results indicate that efficient design of PWEs can reduce the number of required passive RIS tiles by 70% while maintaining high service quality.

Optical wireless networks, especially those relying on visible light communications, suffer from severe deterioration in signal's quality when the line-of-sight (LOS) link is absent due to user's mobility. In order to enable efficient resource management within such networks, reliable prediction of LOS link outage is essential. Towards this objective, this article proposes a data-driven approach based on deep machine learning techniques to predict the outage events in an LOS link. First, we present a framework to generate sufficient data representing the channel gain in mobile optical wireless networks that consist of visible light communications in the downlink and infrared communications in the uplink. Using the developed dataset, we propose a channel predictor that forecasts the burst outages or signal recoveries in the upcoming frames using a deep recurrent neural network that implements long-short-term-memory (LSTM) units. To achieve this goal, we propose a low-complexity approach to reduce the data sparsity due to the user's mobility by abstracting and densifying the channel state sequence. For a one second prediction interval, the proposed prediction framework achieves an event hit rate of 91.55% for abrupt outages with an average event timing error of 79 ms, and 83.19% for recoveries from outages with 145 ms timing error. This timing error is on the same order of magnitude with the coherence time of the optical wireless channel. Therefore, this predictor is very useful in developing efficient resource management strategies in such optical networks.

This article investigates a heterogeneous network (HetNet) consisting of an overlapped coverage of radio frequency (RF) and optical wireless communication (OWC) to support human connectivity to the Internet of Things (IoT) in the fifth generation and beyond (5G+) mobile networks. Such a HetNet-based IoT benefits from the high throughput of the OWC and the high reliability of the RF communications. To ensure a reliable link with Quality-of-Service (QoS) guarantee in terms of network delay and throughput, vertical handovers are triggered within the HetNet. A cross-layer data-driven approach is adopted to reach optimal handover decisions and tackle the challenges associated with the mobility and reliability of IoT. As mobile time-varying wireless channel gains in optical links are not publicly available, we first present a realistic model for the mobile optical channel that reflects the nature of human mobility, and hence, a useful model that features the spatial-temporal patterns of indoor mobile channels is proposed. Using the created data set, we then present a data-driven algorithm that predicts abrupt outages in Line-of-Sight (LOS) optical links and evaluates the optical channel quality through deep learning. Given the resulting LOS link outage prediction in OWC, a reinforcement-learning-based approach is proposed to implement optimal vertical handover decisions with the QoS guarantee. The proposed handover decision algorithm learns to make a tradeoff between the outage risk and the cost of excessive handovers. The numerical results demonstrate considerable improvement in overall latency and handover rate under indoor mobility for bidirectional links.

Link instability induced by users' mobility is one of the challenges of optical wireless communications (OWC) inherited from the propagation nature of light. Hence, good understanding of the optical channel characteristics in dynamic environments plays a vital role in developing robust resource management strategies in OWC networks. Unfortunately, it is quite difficult to collect accurate indoor optical channel data in dynamic environments. In addition, indoor trajectory dataset is not publicly available. To overcome such limitations, this paper proposes a mobile terminal-centric analytical framework that captures the propagation channel characteristics in a mobile OWC network whose downlink is based on visible light and uplink is based on infrared light. We abstract the nature of human behavior by integrating both macro and micro mobility patterns. These patterns are then used to realize the spatio-temporal characteristics of optical wireless channels under long-term environment-confined mobility. The statistics derived from the developed framework indicate that the mobile line-of-sight (LOS) channel gain follows space-time-dependent multiple-peak Nakagami distributions, whereas the non-line-of-sight (NLOS) channel gain adheres to various space-time-dependent single peak distributions under different indoor layouts. The overall distribution of NLOS bandwidth follows space-time-dependent multiple-peak log-logistic distributions in downlinks and space-time-dependent generalized log-logistic distributions in uplinks. Our investigation demonstrates that the indoor layout and the user's environment-confined mobility pattern significantly impact the LOS dynamics but present limited impact on NLOS components. Motivated by the need for better channel models for mobile OWC, the proposed framework fills up an important gap in literature and help the research community to understand better the indoor optical wireless channel characteristics.