In the next generation wireless communication system, transmission rates should continue to rise to support emerging scenarios, e.g., the immersive communications. From the perspective of communication system evolution, multiple-input multiple-output (MIMO) technology remains pivotal for enhancing transmission rates. However, current MIMO systems rely on inserting pilot signals to achieve accurate channel estimation. As the increase of transmit stream, the pilots consume a significant portion of transmission resources, severely reducing the spectral efficiency. In this correspondence, we propose a belief information based mechanism. By introducing a plug-and-play belief information module, existing single-antenna channel estimation networks could be seamlessly adapted to multi-antenna channel estimation and fully exploit the spatial correlation among multiple antennas. Experimental results demonstrate that the proposed method can either improve 1 ~ 2 dB channel estimation performance or reduce 1/3 ~ 1/2 pilot overhead, particularly in bad channel conditions.

Network slicing enables industrial Internet of Things (IIoT) networks with multiservice and differentiated resource requirements to meet increasing demands through efficient use and management of network resources. Typically, the network slice orchestrator relies on demand forecasts for each slice to make informed decisions and maximize resource utilization. The new generation of Industry 4.0 has introduced digital twins to map physical systems to digital models for accurate decision-making. In our approach, we first use graph-attention networks to build a digital twin environment for network slices, enabling real-time traffic analysis, monitoring, and demand forecasting. Based on these predictions, we formulate the resource allocation problem as a federated multi-agent reinforcement learning problem and employ a deep deterministic policy gradient to determine the resource allocation policy while preserving the privacy of the slices. Our results demonstrate that the proposed approaches can improve the accuracy of demand prediction for network slices and reduce the communication overhead of dynamic network slicing.

Deep Reinforcement Learning (DRL) is regarded as a promising tool for optical network optimization. However, the flexibility and efficiency of current DRL-based solutions for optical network optimization require further improvement. Currently, generative models have showcased their significant performance advantages across various domains. In this paper, we introduce OpticGAI, the AI-generated policy design paradigm for optical networks. In detail, it is implemented as a novel DRL framework that utilizes generative models to learn the optimal policy network. Furthermore, we assess the performance of OpticGAI on two NP-hard optical network problems, Routing and Wavelength Assignment (RWA) and dynamic Routing, Modulation, and Spectrum Allocation (RMSA), to show the feasibility of the AI-generated policy paradigm. Simulation results have shown that OpticGAI achieves the highest reward and the lowest blocking rate of both RWA and RMSA problems. OpticGAI poses a promising direction for future research on generative AI-enhanced flexible optical network optimization.

Unmanned Ariel Vehicle (UAV) services with 5G connectivity is an emerging field with numerous applications. Operator-controlled UAV flights and manual static flight configurations are major limitations for the wide adoption of scalability of UAV services. Several services depend on excellent UAV connectivity with a cellular network and maintaining it is challenging in predetermined flight paths. This paper addresses these limitations by proposing a Deep Reinforcement Learning (DRL) framework for UAV path planning with assured connectivity (DUPAC). During UAV flight, DUPAC determines the best route from a defined source to the destination in terms of distance and signal quality. The viability and performance of DUPAC are evaluated under simulated real-world urban scenarios using the Unity framework. The results confirm that DUPAC achieves an autonomous UAV flight path similar to base method with only 2% increment while maintaining an average 9% better connection quality throughout the flight.

The emergence of 5G technology marks a significant milestone in developing telecommunication networks, enabling exciting new applications such as augmented reality and self-driving vehicles. However, these improvements bring an increased management complexity and a special concern in dealing with failures, as the applications 5G intends to support heavily rely on high network performance and low latency. Thus, automatic self-healing solutions have become effective in dealing with this requirement, allowing a learning-based system to automatically detect anomalies and perform Root Cause Analysis (RCA). However, there are inherent challenges to the implementation of such intelligent systems. First, there is a lack of suitable data for anomaly detection and RCA, as labelled data for failure scenarios is uncommon. Secondly, current intelligent solutions are tailored to LTE networks and do not fully capture the spatio-temporal characteristics present in the data. Considering this, we utilize a calibrated simulator, Simu5G, and generate open-source data for normal and failure scenarios. Using this data, we propose Simba, a state-of-the-art approach for anomaly detection and root cause analysis in 5G Radio Access Networks (RANs). We leverage Graph Neural Networks to capture spatial relationships while a Transformer model is used to learn the temporal dependencies of the data. We implement a prototype of Simba and evaluate it over multiple failures. The outcomes are compared against existing solutions to confirm the superiority of Simba.

Any speech recognition system must learn to recognize the intended words regardless of the various ways in which those words may be pronounced. A substantial amount of the variability in speech is systematic, arising from phonological processes occurring in predictable environments. One such process is place assimilation, where phonemes adopt the articulation place of adjacent phonemes. For instance, the word pair clean pan is frequently pronounced as clea[m] pan, with the wordfinal coronal /n/ in clean assimilating to the subsequent labial [p] in pan. This is a simple yet common phonological process across the world's languages (Hura, Lindblom, and Diehl 1992). In English, it occurs for coronal segments (e.g., /t/, /d/, /n/) that are followed by noncoronals, such as labials (e.g., [p], [b], [m]) or velars (e.g., [k], [g], [N]). Human listeners are able to infer the underlying /n/ when exposed to assimilated inputs like clea[m] pan, allowing them to perceive the intended word clean. This phenomenon is referred to as compensation for assimilation and happens automatically-- that is, humans compensate without conscious awareness of the assimilation itself. Psycholinguistic research has used controlled stimuli to investigate the mechanism behind this process.

This article provides a methodology and open-source implementation of Reinforcement Learning algorithms for finding optimal routes in a packet-optical network scenario. The algorithm uses measurements provided by the physical layer (pre-FEC bit error rate and propagation delay) and the link layer (link load) to configure a set of latency-based rewards and penalties based on such measurements. Then, the algorithm executes Q-learning based on this set of rewards for finding the optimal routing strategies. It is further shown that the algorithm dynamically adapts to changing network conditions by re-calculating optimal policies upon either link load changes or link degradation as measured by pre-FEC BER.

SoftBank Group founder Masayoshi Son has declared he's ready to swing for the fences when he makes his next big tech bet, suggesting the Japanese conglomerate is on the cusp of making a major investment in artificial intelligence. The billionaire has warned that his next big endeavor could be a big hit or a bad flop -- but that SoftBank has no choice but to try. That echoes SoftBank Chief Financial Officer Yoshimitsu Goto's recent comments about the investment firm needing to take more risk, particularly as AI development accelerates. "We need to look for our next big move, without fear of whether it'll be a hit or miss," Son told SoftBank shareholders gathered for the wireless operator's annual meeting Thursday. He added that the company lost billions of dollars through its bet on WeWork.

We present a Reinforcement Learning (RL) approach to the problem of controlling the Discontinuous Reception (DRX) policy from a Base Transceiver Station (BTS) in a cellular network. We do so by means of optimally timing the transmission of fast Layer-2 signaling messages (a.k.a. Medium Access Layer (MAC) Control Elements (CEs) as specified in 5G New Radio). Unlike more conventional approaches to DRX optimization, which rely on fine-tuning the values of DRX timers, we assess the gains that can be obtained solely by means of this MAC CE signalling. For the simulation part, we concentrate on traffic types typically encountered in Extended Reality (XR) applications, where the need for battery drain minimization and overheating mitigation are particularly pressing. Both 3GPP 5G New Radio (5G NR) compliant and non-compliant ("beyond 5G") MAC CEs are considered. Our simulation results show that our proposed technique strikes an improved trade-off between latency and energy savings as compared to conventional timer-based approaches that are characteristic of most current implementations. Specifically, our RL-based policy can nearly halve the active time for a single User Equipment (UE) with respect to a na\"ive MAC CE transmission policy, and still achieve near 20% active time reduction for 9 simultaneously served UEs.

At MWC earlier this year, Google announced Gemini's integration with Messages, giving you a way to access the chatbot from within the texting app. The feature was limited to newer Pixel and Samsung Galaxy phones at launch, but now Google has updated its Help page to say that all you need to access it is an "Android device with 6GB of RAM or higher." At the moment, Google Messages only supports Gemini in the English language in 164 countries where it's available. The only exception is Canada, where it also supports French. Google says it's "working hard" to make it available in more languages and more territories in the future.