Localizing knowledge within Large Language Models (LLMs) is crucial for interpreting their mechanisms and outcomes. Whereas knowledge attribution has so far provided local sample-level explanations, in this work we argue that whenever LLMs are used for classification tasks, a class-level explanation is preferable. We therefore define broader concept areas, i.e., regions of the LLM comprising a small set of neurons that contains the most salient knowledge pertaining to each class and propose methods to identify such areas. We apply our methodology to BERT-based LLMs fine-tuned for downstream classification tasks such as sentiment analysis and attack classification: our results show that it is possible to (i) identify crucial sets of neurons that determine the behaviour of fine-tuned LLMs for explanation purposes, as well as (ii) exploit such concept areas to improve their classification outcomes-yielding up to 6% macro F1-Score improvement on sentiment analysis (public dataset) and 2% on attack classification (private dataset) without requiring further fine-tuning.

Over the last years we witnessed a renewed interest toward Traffic Classification (TC) captivated by the rise of Deep Learning (DL). Yet, the vast majority of TC literature lacks code artifacts, performance assessments across datasets and reference comparisons against Machine Learning (ML) methods. Among those works, a recent study from IMC'22 [16] is worth of attention since it adopts recent DL methodologies (namely, few-shot learning, self-supervision via contrastive learning and data augmentation) appealing for networking as they enable to learn from a few samples and transfer across datasets. The main result of [16] on the UCDAVIS, ISCXVPN and ISCXTOR datasets is that, with such DL methodologies, 100 input samples are enough to achieve very high accuracy using an input representation called "flowpic'' (i.e., a per-flow 2d histograms of the packets size evolution over time).

Today's WLANs rely on a centralized Access Controller (AC) entity for managing distributed wireless Access Points (APs) to which user devices connect. The availability of real-time analytics at the AC opens the possibility to automate the allocation of scarce radio resources, continuously adapting to changes in traffic demands. Often, the allocation problem is formulated in terms of weighted graph coloring, which is NP-hard, and custom heuristics are used to find satisfactory solutions. In this paper, we contrast solutions that are based on (and even improve) state of the art heuristics to a data-driven solution that leverages Deep Reinforcement Learning (DRL). Based on both simulation results as well as experiments in a real deployment, we show that our DRL-based scheme not only learns to solve the complex combinatorial problem in bounded time, outperforming heuristics, but it also exhibits appealing generalization properties, e.g. to different network sizes and densities.

Caching popular content at the wireless edge is recently proposed as a means to reduce congestion at the backbone of cellular networks. The two main actors involved are Mobile Network Operators (MNOs) and Content Providers (CPs). In this work, we consider the following arrangement: an MNO pre-installs memory on its wireless equipment (e.g. Base Stations) and invites a unique CP to use them, with monetary cost. The CP will lease memory space and place its content; the MNO will associate network users to stations. For a given association policy, the MNO may help (or not) the CP to offload traffic, depending on whether the association takes into account content placement. We formulate an optimization problem from the CP perspective, which aims at maximizing traffic offloading with minimum leasing costs. This is a joint optimization problem that can include any association policy, and can also derive the optimal one. We present a general exact solution using Benders decomposition. It iteratively updates decisions of the two actors separately and converges to the global optimum. We illustrate the optimal CP leasing/placement strategy and hit probability gains under different association policies. Performance is maximised when the MNO association follows CP actions.

Caching popular content at the wireless edge promises performance benefits as well as business perspectives. In this paper, we study the following arrangement: a mobile network operator (MNO) pre-installs memory on its wireless equipment, sets a price, and invites a unique content provider (CP) to invest. The CP leases memory space and places its content; the MNO then associates network users to stations, aiming for offloading CP traffic or not. We formulate an optimization problem, which maximizes offloading with minimum leasing costs for the CP. This is an NP-hard mixed-integer non-linear optimization problem. We present an iterative exact solution using Generalized Benders decomposition into a content-related master problem and a user-association slave problem. Master is integer linear. Slave is convex for various association policies, including: 1) join-the-closest cache and 2) cache-aware association. For slave, we introduce a distributed exact solution, named generalized bucket-filling. Extensive simulations illustrate the performance benefits under different association policies. The solution helps to determine the optimal leasing price for the MNO, and the optimal investment budget for the CP. As a general conclusion, both actors profit when the MNO association supports CP decisions.

Caching of popular content on wireless nodes is recently proposed as a means to reduce congestion in the backbone of cellular networks and to improve Quality of Service. From a network point of view, the goal is to offload as many users as possible from the backbone network to the wireless caches while at the same time offering good service to cache-unrelated users. Aggressive offloading can lead to an unbalanced user association. Some wireless nodes can be overloaded by cache-related traffic while the resources of others remain underused. Given a fixed content placement, this work proposes an efficient distributed algorithm to control and balance the association of cache-related traffic among cellular cache memories. The algorithm allows the network to achieve the globally optimal solution and can be executed on base stations using a limited amount of information exchange between them. It is based on a novel algorithm we call Bucket-filling. The solution limits the cache-users per node by balancing the total load among the nodes in a fair way. The improvement compared to common user assignment policies is highlighted for single-as well as for multi-tier random networks.

Base station cooperation in the downlink of cellular systems has been recently suggested as a promising concept towards a better exploitation of the communication system's physical resources. It may offer a high gain in capacity through interference mitigation. This however, comes at a cost of high information exchange between cooperating entities and a high computational burden. Clustering of base stations into subgroups is an alternative to guarantee such cooperation benefits in a lower scale. The optimal definition of clusters, however, and a systematic way to find a solution to such problem is not yet available. In this work, we highlight the combinatorial nature of the problem, exploit this to describe the system of users and base stations as a graph and formulate a pure 0-1 program. Its solution suggests a cost optimal way to form clusters and assign user subsets to them.