The rapid evolution of the telecommunication industry necessitates efficient troubleshooting processes to maintain network reliability, software maintainability, and service quality. Trouble Reports (TRs), which document issues in Ericsson's production system, play a critical role in facilitating the timely resolution of software faults. However, the complexity and volume of TR data, along with the presence of diverse criteria that reflect different aspects of each fault, present challenges for retrieval systems. Building on prior work at Ericsson, which utilized a two-stage workflow, comprising Initial Retrieval (IR) and Re-Ranking (RR) stages, this study investigates different TR observation criteria and their impact on the performance of retrieval models. We propose \textbf{CREST} (\textbf{C}riteria-specific \textbf{R}etrieval via \textbf{E}nsemble of \textbf{S}pecialized \textbf{T}R models), a criterion-driven retrieval approach that leverages specialized models for different TR fields to improve both effectiveness and interpretability, thereby enabling quicker fault resolution and supporting software maintenance. CREST utilizes specialized models trained on specific TR criteria and aggregates their outputs to capture diverse and complementary signals. This approach leads to enhanced retrieval accuracy, better calibration of predicted scores, and improved interpretability by providing relevance scores for each criterion, helping users understand why specific TRs were retrieved. Using a subset of Ericsson's internal TRs, this research demonstrates that criterion-specific models significantly outperform a single model approach across key evaluation metrics. This highlights the importance of all targeted criteria used in this study for optimizing the performance of retrieval systems.

This paper presents our findings on the automatic summarization of Java methods within Ericsson, a global telecommunications company. We evaluate the performance of an approach called Automatic Semantic Augmentation of Prompts (ASAP), which uses a Large Language Model (LLM) to generate leading summary comments for Java methods. ASAP enhances the $LLM's$ prompt context by integrating static program analysis and information retrieval techniques to identify similar exemplar methods along with their developer-written Javadocs, and serves as the baseline in our study. In contrast, we explore and compare the performance of four simpler approaches that do not require static program analysis, information retrieval, or the presence of exemplars as in the ASAP method. Our methods rely solely on the Java method body as input, making them lightweight and more suitable for rapid deployment in commercial software development environments. We conducted experiments on an Ericsson software project and replicated the study using two widely-used open-source Java projects, Guava and Elasticsearch, to ensure the reliability of our results. Performance was measured across eight metrics that capture various aspects of similarity. Notably, one of our simpler approaches performed as well as or better than the ASAP method on both the Ericsson project and the open-source projects. Additionally, we performed an ablation study to examine the impact of method names on Javadoc summary generation across our four proposed approaches and the ASAP method. By masking the method names and observing the generated summaries, we found that our approaches were statistically significantly less influenced by the absence of method names compared to the baseline. This suggests that our methods are more robust to variations in method names and may derive summaries more comprehensively from the method body than the ASAP approach.

Machine learning methods are increasingly adopted in communications problems, particularly those arising in next generation wireless settings. Though seen as a key climate mitigation and societal adaptation enabler, communications related energy consumption is high and is expected to grow in future networks in spite of anticipated efficiency gains in 6G due to exponential communications traffic growth. To make meaningful climate mitigation impact in the communications sector, a mindset shift away from maximizing throughput at all cost and towards prioritizing energy efficiency is needed. Moreover, this must be adopted in both existing (without incurring further embodied carbon costs through equipment replacement) and future network infrastructure, given the long development time of mobile generations. To that end, we present summaries of two such problems, from both current and next generation network specifications, where probabilistic inference methods were used to great effect: using Bayesian parameter tuning we are able to safely reduce the energy consumption of existing hardware on a live communications network by $11\%$ whilst maintaining operator specified performance envelopes; through spatiotemporal Gaussian process surrogate modeling we reduce the overhead in a next generation hybrid beamforming system by over $60\%$, greatly improving the networks' ability to target highly mobile users such as autonomous vehicles. The Bayesian paradigm is itself helpful in terms of energy usage, since training a Bayesian optimization model can require much less computation than, say, training a deep neural network.

Ericsson Artificial Intelligence (AI)-based network solution provides capabilities to maximize customers' network investment with a focus on …

If I could give a Noble Prize to a year, it would be 2022! With deep impact on society, this year marked a turning point in the development of 5G, extended reality (XR), computer generated imagery (CGI), artificial intelligence (AI), quantum computers (QC) and robotics. This year, we have published eight thought-leadership blogs on topics related to its role in our telco industry; the importance of 5G; key metaverse use cases in society, enterprise and education; the role of Web3.0 and related blockchain challenges & opportunities; as well as very personal metaverse experiences. So, what happened with 5G over the past 12 months? We introduced radios that are substantially more energy efficient and we augmented the in-network intelligence by a huge margin.

Modern telecom systems are monitored with performance and system logs from multiple application layers and components. Detecting anomalous events from these logs is key to identify security breaches, resource over-utilization, critical/fatal errors, etc. Current supervised log anomaly detection frameworks tend to perform poorly on new types or signatures of anomalies with few or unseen samples in the training data. In this work, we propose a meta-learning-based log anomaly detection framework (LogAnMeta) for detecting anomalies from sequence of log events with few samples. LoganMeta train a hybrid few-shot classifier in an episodic manner. The experimental results demonstrate the efficacy of our proposed method

The Cognitive Software leverages automation, big data scalability, speed, accuracy, and consistency for improved network optimization. The AI-based Cognitive Software solution also contributes to reducing carbon dioxide emissions from operational activities, for example, through the use of virtual drive-testing and remote automatic spectrum analysis. Additionally, stc Group has deployed 5G AI root-cause analysis capabilities to enable a better 5G experience for its subscribers. This future-proof deployment enables stc Group to leverage the Ericsson Performance Optimizers portfolio for surgical optimization analysis and recommendation. Ericsson Performance Optimizers use digital twin technology and advanced AI techniques like deep reinforcement learning and expert recommender systems to proactively provide mobile network optimization recommendations and resolve specific network performance issues, enabling a superior subscriber experience, while reducing operating costs.

One of the most widely used buzzwords in the logistics sector in 2022 is "digitalization." The word is a useful umbrella term for the evolution to computer-based processes from manual procedures that relied on pencils and clipboards in the warehouse or printed manifests at the loading dock. But references to the trend nearly always ignore the tactical steps needed to make digitalization happen. Your DC probably doesn't have a magic wand that transforms basic paper checklists into cloud-based software platforms. So how are practitioners driving toward the goal of pulling logistics processes into the 21st century?

In a world where connectivity has become a necessity, automation enabled by these AI apps makes it easier for CSPs to deal with technology challenges while ensuring a prominent level of network quality and stability. The Service Continuity AI app suite is the latest addition to Ericsson's network Support Services portfolio. This suite has been developed in collaboration with CSPs for predictive and preemptive support. It uses AI and machine learning (ML) technologies to identify and address issues before they impact network performance. "Ericsson Service Continuity solution is a reliable and preemptive way to succeed with consistent performance for complex services. We appreciate the great collaborative experience the last two years between Vodafone and Ericsson," says Georgios Anastopoulos, Core & Transport Network Manager, at Vodafone Greece.

Sweden's attitude towards innovation is perhaps best exemplified by the Swedish innovation agency, Vinnova, a government agency founded in 2001 based on a series of predecessors going back to at least 1968. The innovation agency functions much like its counterparts in other countries, similarly to the Finnish Funding Agency for Technology and Innovation (Tekes) in neighbouring Finland, and to the part of the US National Science Foundation (NSF) that does seed funding on the other side of the Atlantic. The Swedish government gives Vinnova more than €300m each year to invest through grants to different kinds of actors, which might be small companies, research institutes, large competence centres, or consortia of companies working together on projects. Vinnova invests this money along 10 different themes, including sustainable industry and digital transformation. To report on the social and economic effects of its funding, the agency produces two impact studies annually.