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The emergence of new wireless technologies, such as movable antennas and reconfigurable intelligent surfaces, enable channel morphing. In this new paradigm, variations in channel statistics may be decided and thus known prior to communications, deviating from the conventional ergodic models in which the underlying processes that govern channel statistics are assumed to be temporally independent. To provide a fundamental understanding beyond the typical physical-layer studies, we study the capacity region of movable antenna-aided broadcast packet erasure channels, and provide new inner and outer bounds. The proposed linear network-coding protocol opportunistically benefits from the prior knowledge of future statistical changes, and achieves the outer-bounds for a wide range of parameters. Network capacity can be further maximized in a cross-layer fashion by determining optimal channel statistics controlled by the movable antenna position.

Future wireless access networks aim to simultaneously support a large number of devices with heterogeneous service requirements, including data rates, error rates, and latencies. While achievable rate and capacity results exist for Gaussian broadcast channels in the asymptotic blocklength regime, the characterization of second-order achievable rate regions for heterogeneous blocklength constraints is not available. Therefore, we investigate a two-user Gaussian broadcast channel (GBC) with heterogeneous blocklength constraints, specified according to users’ channel output signal-to-noise ratios (SNRs). We assume that the user with higher output SNR has a shorter blocklength constraint. We show that with sufficiently large output SNR, the stronger user can perform the early decoding (ED) technique to decode and subtract the interference via successive interference cancellation (SIC). To achieve this goal, we derive an explicit lower bound on the necessary number of received symbols for a successful ED, using an independent and identically distributed Gaussian input. The second-order rate region is also derived. Numerical results show that ED can outperform the hybrid non-orthogonal multiple access scheme when the stronger channel is sufficiently better than the weaker one. Under the considered setting, about 7-dB SNR gain can be achieved compared to treating interference as noise. These results show that ED with SIC is a promising technique for future wireless networks.

To support manifold vertical applications in any deployment environment, the fifth generation (5G) radio access network (RAN) may exploit artificial intelligence (AI) and machine learning (ML) for intelligent RAN configuration. With the open RAN (O-RAN) architecture supporting the near-real-time (Near-RT) RAN intelligent controller (RIC), various AI/ML algorithms can be designed in the form of “xAPPs” to optimize the performance for different vertical applications. To this end, a low-latency near-RT RIC platform is of crucial importance. The lack of an effective design for the autonomous intelligent xAPP generation for all vertical applications also obstructs the zero-touch operations of the O-RAN. In this article, the new designs to enhance the existing standards and platform of near-RT RIC, and the new design flow for autonomous intelligent xAPP generation are presented. Experimental results demonstrate the ability to support various vertical applications.

In 6G systems, non-terrestrial networks (NTNs) are poised to address the limitations of terrestrial systems, particularly in unserved or underserved areas, by providing infrastructure with mobility that enhances reliability, availability, and responsiveness. Among various types of mobile infrastructures, low earth orbit (LEO) satellite communication (SATCOM) has the potential to offer extended coverage that supports numerous devices simultaneously with low latency. Consequently, LEO SATCOM attracts significant attention from academia, government, and industry. The dynamic formation problem must be solved to form a swarm connecting to the ground station with the most appropriate satellites to achieve LEO SATCOM systems with higher throughput. Existing solutions use computationally demanding methods to solve the NP-hard problem and cannot be employed for SATCOM systems with short coherence time. Furthermore, precise channel state information (CSI) between the ground station and all candidate satellites is required for formation designs, resulting in significant signaling overheads. To overcome these drawbacks, we propose a learning-based dynamic formation method for real-time dynamic formation capability. Specifically, motivated by the channel features of LEO SATCOM, we develop a CSI estimation method to provide coarse CSI (i.e., imperfect CSI) solely based on available geometrical information of LEO SATCOM and without any signaling overhead. Then, our approach can utilize the obtained coarse CSI as inputs and provide valuable guidelines as priorities to access specific satellites for fine-grained CSI (i.e., precise CSI). The prediction results are validated using a small-scale brute force method to determine the final formation. Our intensive simulation results suggest that the proposed method can aid current LEO SATCOM by providing real-time formation results, particularly in low-transmit power regions. Specifically, the proposed method can achieve 90% of full capacity with only 32% signaling overhead to build high-throughput LEO SATCOM.

In next-generation (i.e., 6G) networking systems, the data-driven approach will play an essential role, being an efficient tool for networking system management and bringing popular user applications. With those unprecedented and novel usages, existing frameworks fail to consider the complex nature of the next-generation networking system and consequently fail to be applied to future communication systems directly. Moreover, existing frameworks also fail to support popular privacy-preserving learning strategies efficiently by presenting special designs to respond to the resource-demanding nature of the aforementioned strategies. To fill this gap, this paper extends conventional serverless platforms with serverless edge learning architectures, providing a mature and efficient distributed training framework by fully exploiting limited wireless communication and edge computation resources in the considered networking system with the following three features. Firstly, this framework dynamically orchestrates resources among heterogeneous physical units to efficiently fulfill privacy-preserving learning objectives. The design jointly considers learning task requests and underlying infrastructure heterogeneity, including last-mile transmissions, computation abilities of edge and cloud computing centers, and loading status of infrastructure. Secondly, the proposed framework can easily work with data-driven approaches to improve network management efficiency, realizing AI for network promise of next-generation networking systems to provide efficient network automation. Lastly, to significantly reduce distributed training overheads, small-scale data training is proposed by integrating with a general, simple data classifier. This low-load enhancement can seamlessly work with various distributed deep models in the proposed framework to improve communications and computation efficiencies during the training phase. Based on the above innovations, open challenges, and future research directions encourage the research community to develop efficient privacy-preserving learning techniques.

Elastic optical networks (EONs) operating in the C-band have been widely deployed worldwide. However, two major technologies—multiband elastic optical networks (MB-EONs) and space division multiplexed elastic optical networks (SDM-EONs)—can significantly increase network capacity beyond traditional EONs. A one-time greenfield deployment of these flexible-grid technologies may not be practical, as existing investments in flexible-grid EONs need to be preserved and ongoing services must face minimal disruption. Therefore, we envision the coexistence of flexible-grid, multiband, and multicore technologies during the brownfield migration. Each technology represents a tradeoff between higher capacity and greater deployment overhead, directly impacting network performance. Moreover, as traffic demands continue rising, capacity exhaustion becomes inevitable. Considering the different characteristics of these technologies, we propose a robust network planning solution called Progressive Optics Deployment and Integration for Growing Yields (PRODIGY+) to gradually migrate current C-band EONs. PRODIGY+ employs proactive measures inspired by the Swiss Cheese Model, making the network robust to traffic peaks while meeting service level agreements. The upgrade strategy enables a gradual transition to minimize migration costs while continuously supporting increasing traffic demands. We provide a detailed comparison of our proposed PRODIGY+ strategy against baseline strategies, demonstrating its superior performance.