Building the next-generation wireless systems that could support services such as the metaverse, digital twins (DTs), and holographic teleportation is challenging to achieve exclusively through incremental advances to conventional wireless technologies like metasurfaces or holographic antennas. While the 6G concept of artificial intelligence (AI)-native networks promises to overcome some of the limitations of existing wireless technologies, current developments of AI-native wireless systems rely mostly on conventional AI tools such as auto-encoders and off-the-shelf artificial neural networks. However, those tools struggle to manage and cope with the complex, nontrivial scenarios faced in real-world wireless environments and the growing quality-of-experience (QoE) requirements of the aforementioned, emerging wireless use cases. In contrast, in this article, we propose to fundamentally revisit the concept of AI-native wireless systems, equipping them with the common sense necessary to transform them into artificial general intelligence (AGI)-native systems. Our envisioned AGI-native wireless systems acquire common sense by exploiting different cognitive abilities such as reasoning and analogy. These abilities in our proposed AGI-native wireless system are mainly founded on three fundamental components: a perception module, a world model, and an action-planning component. Collectively, these three fundamental components enable the four pillars of common sense that include dealing with unforeseen scenarios through horizontal generalizability, capturing intuitive physics, performing analogical reasoning, and filling in the blanks. Toward developing these components, we start by showing how the perception module can be built through abstracting real-world elements into generalizable representations. These representations are then used to create a world model, founded on principles of causality and hyperdimensional (HD) computing. Specifically, we propose a concrete definition of a world model, viewing it as an HD causal vector space that aligns with the intuitive physics of the real world—a cornerstone of common sense. In addition,we discuss how this proposed world model can enable analogical reasoning and manipulation of the abstract representations. Then, we show how the world model can drive an action-planning feature of the AGI-native network. In particular, we propose an intent-driven and objective-driven planning method that can maneuver the AGI-native network to plan its actions. These planning methods are based on brain-inspired frameworks such as integrated information theory and hierarchical abstractions that play a crucial role in enabling human-like decision-making. Next, we explain how an AGI-native network can be further exploited to enable three use cases related to human users and autonomous agent applications: 1) analogical reasoning for the next-generation DTs; 2) synchronized and resilient experiences for cognitive avatars; and 3) brain-level metaverse experiences exemplified by holographic teleportation. Finally, we conclude with a set of recommendations to ignite the quest for AGI-native systems. Ultimately, we envision this article as a roadmap for the next generation of wireless systems beyond 6G.

Long-range (LoRa) wireless networks have been widely proposed as efficient wireless access networks for battery-constrained Internet of Things (IoT) devices. However, applying the LoRa-based IoT network in search-and-rescue (SAR) operations will have limited coverage caused by high signal attenuation due to terrestrial blockages, especially in highly remote areas. To overcome this challenge, using unmanned aerial vehicles (UAVs) as a flying LoRa gateway to transfer messages from ground LoRa nodes to the ground rescue station can be a promising solution. In this paper, an artificial intelligence-empowered SAR operation framework using a UAV-assisted LoRa network in different unknown search environments is designed and implemented. The problem of the flying LoRa (FL) gateway control policy is modeled as a partially observable Markov decision process to move the UAV towards the LoRa transmitter carried by a lost person in the known remote search area. A deep reinforcement learning (RL)-based policy is designed to determine the adaptive FL gateway trajectory in a given search environment. Then, as a general solution, a deep meta-RL framework is used for SAR in any new and unknown environments. The proposed deep meta-RL framework integrates the information of the prior FL gateway experience in the previous SAR environments to the new environment and then rapidly adapts the UAV control policy model for SAR operation in a new and unknown environment. To analyze the performance of the proposed framework in real-world scenarios, the proposed SAR system is experimentally tested in three environments: a university campus, a wide plain, and a slotted canyon at Mongasht mountain ranges, Iran. Experimental results show that if the deep meta-RL-based control policy is applied instead of the deep RL-based one, the number of SAR time slots decreases from 141 to 50. Moreover, in the slotted canyon environment, the UAV energy consumption under the deep meta-RL policy is respectively 57% and 23% less than the deep RL and Actor-Critic RL policies.

In this paper a novel joint sensing, communication, and artificial intelligence (AI) framework is proposed so as to optimize extended reality (XR) experiences over terahertz (THz) wireless systems. Within this framework, active reconfigurable intelligent surfaces (RISs) are incorporated as as pivotal elements, serving as enhanced base stations in the THz band to enhance Line-of-Sight (LoS) communication. The proposed framework consists of three main components.First, a tensor decomposition framework is proposed to extract unique sensing parameters for XR users and their environment by exploiting the THz channel sparsity. Essentially, THz band’s quasi-opticality is exploited and the sensing parameters are extracted from the uplink communication signal, thereby allowing for the use of thesame waveform, spectrum, and hardware for both communication and sensing functionalities. Then, the Cramer-Rao lower bound is derived to assess the accuracy of the estimated sensing parameters.Second, a non-autoregressive multi-resolution generative artificial intelligence (AI) framework integrated with an adversarial transformer is proposed to predict missing and future sensing information. The proposed framework offers robust and comprehensive historical sensing information and anticipatory forecasts of future environmental changes, which aregeneralizable to fluctuations in both known and unforeseen user behaviors and environmental conditions.Third, a multi-agent deep recurrent hysteretic Q-neural network is developed to control the handover policy of RIS subarrays, leveraging the informative nature of sensing information to minimize handover cost, maximize the individual quality of personal experiences (QoPEs), and improve the robustness and resilience of THz links. Simulation results show a high generalizability of the proposed unsupervised generative AI framework to fluctuations in user behavior and velocity, leading to a 61% improvement in instantaneous reliability compared to schemes with known channel state information.

Accommodating digital twins (DTs) in the metaverse is essential to achieving digital reality. This need for integrating DTs into the metaverse while operating them at the network edge has increased the demand for a decentralized edge-enabled metaverse. Hence, to consolidate the fusion between real and digital entities, it is necessary to harmonize the interoperability between DTs and the metaverse at the edge. In this paper, a novel decentralized metaverse framework that incorporates DT operations at the wireless edge is presented. In particular, a system of autonomous physical twins (PTs) operating in a massively-sensed zone is replicated as cyber twins (CTs) at the mobile edge computing (MEC) servers. To render the CTs' digital environment, this zone is partitioned and teleported as distributed sub-metaverses to the MEC servers. To guarantee seamless synchronization of the sub-metaverses and their associated CTs with the dynamics of the real world and PTs, respectively, this joint synchronization problem is posed as an optimization problem whose goal is to minimize the average sub-synchronization time between the real and digital worlds, while meeting the DT synchronization intensity requirements. To solve this problem, a novel iterative algorithm for joint sub-metaverse and DT association at the MEC servers is proposed. This algorithm exploits the rigorous framework of optimal transport theory so as to efficiently distribute the sub-metaverses and DTs, while considering the computing and communication resource allocations. Simulation results show that the proposed solution can orchestrate the interplay between DTs and sub-metaverses to achieve a 25.75% reduction in the sub-synchronization time in comparison to the signal-to-noise ratio-based association scheme.

Terahertz (THz) communication will be a key enabler for next-generation wireless systems. While THz frequency bands provide abundant bandwidth and extremely high data rates, their effective operation is inhibited by short communication ranges and narrow beams, thus, leading to major challenges pertaining to user mobility, beam alignment, and handover. In particular, there is a strong need for novel beam tracking methods that consider the tradeoff between enhancing the received signal strength via increasing beam directivity, and increasing the coverage probability by widening the beam. In this paper, a multi-objective optimization problem is formulated with the goal of jointly maximizing the expected rate and minimizing the outage probability subject to transmit power and overhead constraints. Subsequently, a novel parameterized beamformer with dynamic beamwidth adaptation is proposed. In addition to the precoder, an event-based beam tracking approach is introduced that efficiently prevents outages caused by beam misalignment and dynamic blockage while maintaining a low pilot overhead. Simulation results show that the proposed beamforming scheme improves average rate performance and reduces the amount of outages caused by the brittle THz misalignment process and the particularly severe path loss in the THz band. Moreover, the proposed event-triggered THz channel estimation approach enables connectivity with minimal overhead and reliable communication at THz bands.

Next-generation wireless systems are rapidly evolving from communication-only systems to multi-modal systems with integrated sensing and communications. In this paper a novel joint sensing and communication framework is proposed for enabling wireless extended reality (XR) at terahertz (THz) bands. To gather rich sensing information and a higher line-of-sight (LoS) availability, THz-operated reconfigurable intelligent surfaces (RISs) acting as base stations are deployed. The sensing parameters are extracted by leveraging THz’s quasi-opticality and opportunistically utilizing uplink communication waveforms. This enables the use of the same waveform, spectrum, and hardware for both sensing and communication purposes. The environmental sensing parameters are then derived by exploiting the sparsity of THz channels via tensor decomposition. Hence, a high-resolution indoor mapping is derived so as to characterize the spatial availability of communications and the mobility of users. Simulation results show that in the proposed framework, the resolution and data rate of the overall system are positively correlated, thus allowing a joint optimization between these metrics with no tradeoffs. Results also show that the proposed framework improves the system reliability in static and mobile systems. In particular, the highest reliability gains of 10% are achieved in a walking speed mobile environment compared to communication only systems with beam tracking.

Wireless virtual reality (VR), a key 3GPP use case of emerging cellular systems, imposes new visual and haptic requirements directly linked to the Quality of Experience (QoE) of VR users. These QoE requirements can only be met by wireless connectivity that offers high-rate and high-reliability low-latency communications (HR2LLC), unlike the low rates commonly associated with ultrareliable low-latency communication. The high rates for VR over short distances can only be supported by an enormous bandwidth, available in the terahertz (THz)-frequency bands. To explore the potential of THz for meeting HR2LLC requirements, a quantification of the risk for an unreliable VR performance is conducted through a novel and rigorous characterization of the tail of the end-to-end (E2E) delay. Then, a thorough analysis of the Tail-Value-at-Risk (TVaR) is performed to concretely characterize the behavior of extreme wireless events crucial to the real-time VR experience. In particular, the probability distribution function of the THz transmission delay is derived and then used to infer the system reliability scenarios with guaranteed Line of Sight (LoS) as a function of THz network parameters. Numerical results show that abundant bandwidth and low molecular absorption are necessary to improve the reliability. However, their effect remains secondary compared to the availability of LoS, which significantly affects the THz HR2LLC performance. In particular, for scenarios with guaranteed LoS, a reliability of 99.999% (with an E2E delay threshold of 20 ms) for a bandwidth of 15 GHz along with data rates of 18.3 Gbps can be achieved by the THz network, compared to a reliability of 96% for twice the bandwidth, when blockages are considered.

In this paper, a lifelong learning problem is studied for an Internet of Things (IoT) system. In the considered model, each IoT device aims to balance its information freshness and energy consumption tradeoff by controlling its computational resource allocation at each time slot under dynamic environments. An unmanned aerial vehicle (UAV) is deployed as a flying base station so as to enable the IoT devices to adapt to novel environments. To this end, a new lifelong reinforcement learning algorithm, used by the UAV, is proposed in order to adapt the operation of the devices at each visit by the UAV. By using the experience from previously visited devices and environments, the UAV can help devices adapt faster to future states of their environment. To do so, a knowledge base shared by all devices is maintained at the UAV. Simulation results show that the proposed algorithm can converge 25% to 50% faster than a policy gradient baseline algorithm that optimizes each device’s decision making problem in isolation.

Guaranteeing fresh and reliable information for augmented reality (AR) services is a key challenge to enable a real-time experience and sustain a high quality of physical experience (QoPE) for the users. In this paper, a terahertz (THz) cellular network is used to exchange rate-hungry AR content. For this network, guaranteeing an instantaneous low peak age of information (PAoI) is necessary to overcome the uncertainty stemming from the THz channel. In particular, a novel economic concept, namely, the risk of ruin is proposed to examine the probability of occurrence of rare, but extremely high PAoI that can jeopardize the operation of the AR service. To assess the severity of these hazards, the cumulative distribution function (CDF) of the PAoI is derived for two different scheduling policies. This CDF is then used to find the probability of maximum severity of ruin PAoI. Furthermore, to provide long term insights about the AR content's age, the average PAoI of the overall system is also derived. Simulation results show that an increase in the number of users will positively impact the PAoI in both the expected and worst-case scenarios. Meanwhile, an increase in the bandwidth reduces the average PAoI but leads to a decline in the severity of ruin performance. The results also show that a system with preemptive last come first served (LCFS) queues of limited size buffers have a better ruin performance (12% increase in the probability of guaranteeing a less severe PAoI while increasing the number of users), whereas first come first served (FCFS) queues of limited buffers lead to a better average PAoI performance (45% lower PAoI as we increase the bandwidth).

In this paper, the problem of associating reconfigurable intelligent surfaces (RISs) to virtual reality (VR) users is studied for a wireless VR network. In particular, this problem is considered within a cellular network that employs terahertz (THz) operated RISs acting as base stations. To provide a seamless VR experience, high data rates and reliable low latency need to be continuously guaranteed. To address these challenges, a novel risk-based framework based on the entropic value-at-risk is proposed for rate optimization and reliability performance. Furthermore, a Lyapunov optimization technique is used to reformulate the problem as a linear weighted function, while ensuring that higher order statistics of the queue length are maintained under a threshold. To address this problem, given the stochastic nature of the channel, a policy-based reinforcement learning (RL) algorithm is proposed. Since the state space is extremely large, the policy is learned through a deep-RL algorithm. In particular, a recurrent neural network (RNN) RL framework is proposed to capture the dynamic channel behavior and improve the speed of conventional RL policy-search algorithms. Simulation results demonstrate that the maximal queue length resulting from the proposed approach is only within 1% of the optimal solution. The results show a high accuracy and fast convergence for the RNN with a validation accuracy of 91.92%.