5g network operators

Abstract: Vehicle-to-Everything (V2X) communications rely on elliptic-curve signatures that a quantum adversary breaks and on pseudonym certificates whose anonymity rests on rotation rate, not cryptography. This work presents qDGSP, a post-quantum V2X infrastructure: a hash-based dynamic group signature scheme (DGSP, over SPHINCS+ and WOTS+) distributed between a central controller and roadside verifiers. Group keys derive deterministically from quantum key distribution (QKD) seeds served over the ETSI GS QKD 014 interface, so no group-signature secret crosses the classical channel. qDGSP-EA (Ephemeral Association) binds an ephemeral ML-DSA-65 key to one group signature per 5G cell: messages remain unlinkable across cells, linkable within one. On a testbed with emulated 5G links, median end-to-end latency stayed within the 100ms ETSI budget for every variant. The qDGSP-EA intra-cell DENM matched the NIST ML-DSA-65 baseline on the wire, and under load EA mode raised delivery from roughly 78% to 93%.

Abstract: Many 6G use cases such as tactile Internet and eXtended reality applications would simultaneously require a high data rate and low latency wireless connectivity.It is increasingly being realized that the current transport protocols relying on packet loss and delay are insufficient to satisfy the data rate and latency requirements of such applications. To address this problem, we propose RAPID, a transport layer solution designed for private 5G edge networks, where the bottleneck is located at the base station. We show that by regulating the send rate to maintain a target physical resource block utilization, we can achieve high data rate, low latency communication. We design a simple yet effective policy that adapts to the dynamic nature of wireless channel conditions. In a multi-UE scenario with two RAPID flows and one TCP Cubic flow, our testbed results show that RAPID flows achieve significant improvements in one-way delay, i.e., 72x and 55x lower compared to TCP Cubic, with negligible packet loss at the cost of modest goodput reductions of 16% and 27%, respectively.

Abstract: This paper presents a theory-driven characterization of type-safe decision frameworks for the agentic control of 5G networks, where every decision must be an element of a declared option set rather than free text. Three design points are evaluated on an Open5GS/UERANSIM testbed with a closed core-policy loop, namely a hosted typed model (Jev), an open fine-tunable typed encoder (Laya), and a zero-label retrofit of a general language model (AnyJev). The proposed theoretical framework turns timeliness, type conformance, certification cost and cardinality into checkable applicability predicates, supported by an optimal act/escalate/abstain gate, an escalation-feasibility floor, a co-location stability condition, per-type conformal risk control with a certification label floor, and a type-mismatch bound. Measuring every predicate yields an applicability map from framework to 5G decision class. Type safety removes format failures but not the question: the fine-tuned typed encoder returned its training answer for 98-99.5% of changed questions, and its calibrated gate then acted wrongly on up to 80% of them, whereas the question-reading frameworks acted wrongly on at most 0.143 (Jev) and 0.137 (AnyJev) of any changed question, but were either hosted and 11-29 times slower (Jev) or reliant on an 8B language model (AnyJev).

Abstract: The transition to virtualized Radio Access Networks (vRAN) enables dynamic power control through fine-grained CPU resource management. However, existing approaches treat gNB as a monolithic entity, failing to exploit the heterogeneous computational characteristics of different protocol layers. This paper proposes ALARM, a layer-aware adaptive resource management framework for constrained 5G vRAN. ALARM decomposes the gNB into functional layers representing distinct RAN tasks with heterogeneous computational demands, and controls CPU resources at the layer level based on processing weights. The framework monitors per-layer performance to detect violations and responds by scaling only the affected layer. This targeted adaptation avoids the over-provisioning inherent in traditional uniform scaling approaches. Experimental validation on two constrained platforms demonstrates up to 33% power reduction versus non-optimized baseline and 19% beyond uniform approaches, with dynamic adaptation achieving 9.8% additional savings while preserving strict real-time guarantees.

Abstract: End-to-end (E2E) network slices (NSs) are provisioned across multiple domains of the 5G network. In hierarchical NS management, a tenant submits a network slice request (NSR), which specifies E2E service level agreement (SLA) requirements. Rather than managing these domains directly, an E2E controller decomposes each NSR into domain-level SLA requirements and delegates resource allocation to domain-specific controllers, which return feasibility and resource-consumption feedback. A poor decomposition policy can therefore cause rejection of the current request by producing infeasible requirements or reduce future admission opportunities by concentrating resource consumption in bottleneck domains. We call this decomposition-policy optimization problem the network slice request decomposition problem (NSR-DP). For practical operation, online approaches to NSR-DP have been proposed. Such approaches must jointly meet two requirements: (R1) control long-term resource budgets and (R2) adapt each decomposition to the performance targets and guarantee levels specified in the arriving NSR's SLA. To meet these requirements, we introduce contextual constrained kernel bandits (CCKB) as an online solution for NSR-DP. To address (R1), CCKB raises penalties for using resources that become tight, thereby discouraging decompositions that consume bottleneck resources. To address (R2), it uses Gaussian processes (GPs) to predict, for the current NSR, the reward and resource usage of candidate decompositions, allowing it to select a decomposition suited to the performance targets and guarantee levels. We establish high-probability guarantees for the resulting formulation and show through extensive 5G simulations across topology, bottleneck, and traffic-mixture settings that CCKB outperforms the baselines in the large majority of conditions.