Sample efficiency and robustness remain significant challenges in applying Reinforcement Learning (RL) to dynamic and heterogeneous telecommunications environments. In this paper, we propose a unifying framework for causal reinforcement learning that integrates counterfactual reasoning and guided causal action selection to improve learning efficiency and policy robustness. Our approach introduces two key techniques: (i) Counterfactual Data Augmentation (CDA), which enriches training data by generating hypothetical outcomes for unobserved actions using structural causal models (SCM), and (ii) Causal Action Selection (CAS), which integrates causal-effect estimation into policy optimization to prioritize high-utility interventions. We validate our approach on 2 standard RL benchmarks as well as a domain-specific Synthetic Slicing Environment simulating Layer 2 scheduling in telecommunications networks. We demonstrate that our framework can reduce the number of environment interactions upto 56% to learn an optimal policy. Our results demonstrate that incorporating causal interventions significantly enhances sample efficiency, stability, and performance of trained RL model compared to conventional RL methods, highlighting the potential for safer and more effective deployment of RL in real-world telecom applications.
Modern financial applications demand architectures that ensure high throughput, low latency, and strong resilience. Traditional CRUD-based systems often encounter transactional bottlenecks and limited fault tolerance, making them less suitable for real-time workloads. In this work, we design and benchmark a Kafka-based event-sourcing architecture that integrates CQRS, schema-registry-backed versioning, Zstandard compression, dynamic Dead Letter Queue (DLQ) handling, and RocksDB-backed local state caching.We evaluate this system against a CRUD-based REST+SQL implementation. To ensure a rigorous comparison, we enforce network symmetry by deploying both the Kafka cluster and the PostgreSQL database on managed cloud infrastructure, eliminating placement bias. Benchmarks were conducted on a dataset of 50,000 banking events (generating over 150,000 persistent state transitions) to measure sustained performance. Our findings show that the Kafka-based approach achieves a sustained throughput of 105.4 events/sec (2.69x over CRUD) while maintaining consistent low-latency reads, validating its suitability for production-grade financial platforms.
5G/6G networks play a major role in the realization of practical systems for Extended Reality (XR) applications. These networks provide high bandwidth and low latency requirements through a typical enhanced Mobile Broad-Band (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC) slices for XR applications. In this paper, we share the lessons learnt from the implementations: i) Private 5G for XR-assisted indoor facility tour and ii) Public 5G for XR-assisted Medical Emergency Care. We evaluate the performance of XR use cases, present the insights, and propose future work based on implementations.
Traditionally telecom networks are monolithic in nature and vertically integrated. Single entity takes care of network deployment as well as service delivery. Internet service provider takes care of identity, access, transport, core and backhaul functions. Such monolithic and vertical integrated architecture lack technical innovation and economic competition. Monolithic architectures are also prone to proprietary interface implementations which leads to non-interoperability among communication layers, leading to cost inefficiency and network non-scalability. Whereas open and disaggregated network architecture induces innovation, competition, scalability and cost efficiency. There is multiple industry led efforts to transform telecom networks into open, disaggregated and federated architecture. Open RAN in cellular world and OpenRoaming in Wi-Fi world are few of the noticeable efforts. In India, WANI (Wi-Fi Access Network Interface) is one such disaggregated and federated public Wi-Fi initiative where identity, access, core and backhaul functions are unbundled and offered by independent entities, evolving the ecosystem as a federation. Interoperability among these functions is ensured through a secure exchange of well-defined federation protocols i.e., wani tokens. Disaggregation and federation also add up the network complexity. Enabling roaming in such disaggregated and federated network poses multiple technical challenges. Roaming in telecom network enables a compelling business case for the service providers as well as improve network usability for the end users. In this paper, we present current WANI technical framework, learning from initial deployment, existing technical gaps, ongoing efforts to standardize the architecture and a novel architecture choice for efficient roaming in such network.
