With the rapid development in 6G networks, traditional network operation and maintenance (O&M) approaches are insufficient to handle the scale and real-time demands. This paper presents a novel O&M system, applying a multiagent scheduling algorithm to autonomously detect, diagnose, and resolve network faults. The system is structured in five layers: Data, Data Model, Agent, Application, and Interaction layers. And five classes of agents are integrated. Experimental results show the system's ability to outperform manual processes, which demonstrates the efficiency for the demanding needs of nextgeneration network management.

The prevalence of fiber optic cable failures has been identified as a key contributor to failures across multiple network systems in the realm of network operations and maintenance. Meanwhile, with the continued expansion of fiber optic cable adoption and its widening coverage, the task of maintenance has grown increasingly complex for network operators. In the context of today’s highly efficient, expansive, and intricate networks, the limitations of conventional methods for operating and maintaining fiber optic cables have become unmistakably evident. To meet the pressing need for cost reduction, this paper introduces an innovative optical cable fault location method, leveraging automation and artificial intelligence technology. Tailored for practical network applications, this method conducts a comprehensive analysis of concurrent faults across multiple systems, pinpointing their origin in optical cable faults. Through the implementation of diverse navigation strategies, it achieves automatic optical cable fault location. The adoption of this method delivers a substantial reduction in fault location time, diminishing it from minutes to mere milliseconds. This dramatic increase in location speed leads to significant labor cost reductions, thereby greatly enhancing the efficiency of network maintenance.

It is difficult to find the root cause alarms by the traditional operation and maintenance methods quickly and accurately, due to the complex architecture of the 5G core network. At present, the library of cross-layer alarm association rules for the 5G core network is not complete. Therefore, the cross-layer alarm association rules mining algorithm of 5G core network is proposed in this paper, which is based on OPTICS algorithm and the weighted FP-growth algorithm. Initially, a time-correlated alarm transaction set is generated by OPTICS algorithm, and then a spatially correlated alarm transaction set is further extracted according to the three-layer topology. Finally, the weighted FP-growth algorithm is used to generate cross-layer alarm association rules. According to the experimental results, it suggests that the alarm transaction set is effectively compressed, and the accuracy of alarm rules mining is improved greatly by this algorithm.

Multi-behavior recommendation, which exploits auxiliary behaviors (e.g., click and cart) to help predict users’ potential interactions on the target behavior (e.g., buy), is regarded as an effective way to alleviate the data sparsity or cold-start issues in recommendation. Multi-behaviors are often taken in certain orders in real-world applications (e.g., click>cart>buy). In a behavior chain, a latter behavior usually exhibits a stronger signal of user preference than the former one does. Most existing multi-behavior models fail to capture such dependencies in a behavior chain for embedding learning. In this work, we propose a novel multi-behavior recommendation model with cascading graph convolution networks (named MB-CGCN). In MB-CGCN, the embeddings learned from one behavior are used as the input features for the next behavior’s embedding learning after a feature transformation operation. In this way, our model explicitly utilizes the behavior dependencies in embedding learning. Experiments on two benchmark datasets demonstrate the effectiveness of our model on exploiting multi-behavior data. It outperforms the best baseline by 33.7% and 35.9% on average over the two datasets in terms of Recall@10 and NDCG@10, respectively.

Future networks are towards autonomous, with a high level of automatic and intelligent abilities. There are several domains and layers in operator networks, malfunctions can be transmitted from lower layers to upper layers, and from one domain to another domain. At present, cross-domain network malfunctions are mainly relied on the operation and maintenance staff of each professional network to analyze and dispatch orders, resulting in repeated orders and increased human cost. The first and important step of malfunction diagnosis is the construction of network topology. However, cross-domain network topology cannot be associated automatically at present. Based on the performance data, a new method using AI technologies is proposed in this paper, which can associate the connecting cross-domain network ports automatically. The principle is that a same time sequence similarity is shared by the connected ports. Taking the data from real networks and comparing with the existing topology, the connecting relations can be 100% correctly recognized. This method can be widely used to any cross-domain networks, without changing current network equipment.

With the trend of 5G-A (5G-Advanced) harmonized network communications and sensing harmonized communication and sensing, network capability exposure technology will help operators, business providers and 3rd business parties to realize harmonized network communications and sensing harmonized communication and sensing business and applications. This paper will focus on capability exposure technology based on 5G-A harmonized communication and sensing. Initially, this paper discusses the capability exposure hierarchical architecture based on harmonized communication and sensing, secondly proposes the harmonized communication and sensing network architecture and basic network signaling process combined with capability exposure technology. Then, this paper discusses capability exposure application scenarios based on communication sensing. This paper can provide relevant reference for the technological evolution, network deployment and application discussion of the harmonized communication and sensing capability in the operator network.

Enterprises loss is a growth strategy, in which enterprises migrate across regions/cities to adapt to the changes of internal and external environment, in this way to seek new development space and further reach the growth again. As the carrier of local economic development, the transfer of enterprises from one region to another undoubtedly means the loss of regional resources for the region. This paper takes large- scale enterprises as the research object. Then, this paper uses questionnaire data and statistical data, and adopts the combination of PCA algorithm and extreme value standardization method to comprehensively evaluate the loss probability of enterprises. This method will reflect the loss tendency of enterprises in the region, and make an empirical analysis on the large-scale enterprises in region, in this way to help regional managers have an early insight into the loss tendency of enterprises in the region. Finally, it will provide a reference for stabilizing the regional economy and help reduce the loss risk of large-scale enterprises in the region.

The ITU-T Recommendation G.722 about subband adaptive differential pulse code modulation (SB-ADPCM) is the mandatory wideband speech codec in the new generation digital enhanced cordless telephony (NG-DECT). Although in ADPCM the difference signal instead of the original signal is quantized and adaptive prediction is employed, redundancy is yet observed within the quantized samples. In this paper we apply a soft-decision speech decoding technique which exploits this redundancy in terms of a priori knowledge and the channel reliability information to NG-DECT. In that way, we propose a novel scheme in a standard-compliant fashion which improves the robustness of the decoder. The performance of our proposal is evaluated in terms of speech quality and a noticeable improvement over the standard codec and its own packet loss concealment algorithm is observed.

Variable-length codes (VLCs) are widely used in media transmission. Compared to fixed-length codes (FLCs), VLCs can represent the same message with a lower bit rate, thus having a better compression performance. But inevitably, VLCs are very sensitive to transmission errors. In this work, based on the trellis representation for VLCs and the BCJR algorithm, we present a variable-length soft-decision decoder utilizing bit-wise channel reliability information and achieving a better error robustness in contrast to hard-decision decoding. Given the application of VLCs in audio coding showing both source correlation and variable block lengths, a strong dependency of performance is observed for both. Therefore, we point out tradeoffs of (soft-decision) decoded FLCs and VLCs depending on quantization bit rate, source correlation, and block length. We find that VLCs over AWGN channels are only recommended for very low source correlation in combination with very short block lengths and soft-decision decoding.