Wireless backhauling is envisioned to play a pivotal role in 6G non-terrestrial networks (NTNs) due to its ability to deliver cable-free connectivity between edge nodes and gateways. However, the dynamic network topology and time-varying channels inherent to NTNs pose significant challenges for real-time network status monitoring. To address these challenges, we propose PhyINT, a novel in-band network telemetry approach that collects telemetry data at the physical layer for time-slotted NTNs. PhyINT allows network nodes to encode telemetry data onto resource elements (REs) in a distributed manner. Since REs are consistently available in every time slot, regardless of wireless channel variability, the encoding process can be made highly predictable and faithfully reconstructed at the gateway for decoding. Moreover, we formulate a multi-objective optimization problem that jointly minimizes the resource consumption and the telemetry collection completion latency. Extensive simulations across NTNs demonstrate that PhyINT significantly outperforms existing methods in reliability, latency, and goodput.

The interference graph, depicting the intra- and inter-cell interference channel gains, is indispensable for resource allocation in multi-cell networks. However, there lacks viable methods of interference graph estimation (IGE) for multi-cell multi-numerology (MN) networks. To fill this gap, we propose an efficient power-domain approach to IGE for the resource allocation in multi-cell MN networks. Unlike traditional reference signal-based approaches that consume frequency-time resources, our approach uses power as a new dimension for the estimation of channel gains. By carefully controlling the transmit powers of base stations, our approach is capable of estimating both intra- and inter-cell interference channel gains. As a power-domain approach, it can be seamlessly integrated with the resource allocation such that IGE and resource allocation can be conducted simultaneously using the same frequency-time resources. We derive the necessary conditions for the power-domain IGE and design a practical power control scheme. We formulate a multi-objective joint optimization problem of IGE and resource allocation, propose iterative solutions with proven convergence, and analyze the computational complexity. Our simulation results show that power-domain IGE can accurately estimate strong interference channel gains with low power overhead and is robust to carrier frequency and timing offsets.

Ray tracing is widely employed to model the propagation of radio-frequency (RF) signal in complex environment. The modelling performance greatly depends on how accurately the target scene can be depicted, including the scene geometry and surface material properties. The advances in computer vision and LiDAR make scene geometry estimation increasingly accurate, but there still lacks scalable and efficient approaches to estimate the material reflectivity in real-world environment. In this work, we tackle this problem by learning the material reflectivity efficiently from the path loss of the RF signal from the transmitters to receivers. Specifically, we want the learned material reflection coefficients to minimize the gap between the predicted and measured powers of the receivers. We achieve this by translating the neural reflectance field from optics to RF domain by modelling both the amplitude and phase of RF signals to account for the multipath effects. We further propose a differentiable RF ray tracing framework that optimizes the neural reflectance field to match the signal strength measurements. We simulate a complex real-world environment for experiments and our simulation results show that the neural reflectance field can successfully learn the reflection coefficients for all incident angles. As a result, our approach achieves better accuracy in predicting the powers of receivers with significantly less training data compared to existing approaches.

Resource allocation in multi-cell networks typically requires knowing the inter-cell interference channel gains. When multiple numerologies are employed, resource allocation further needs to estimate the inter-numerology interference within each cell. However, there lacks viable methods of interference graph estimation (IGE), depicting the intra- and inter-cell interference channel gains, for multi-cell multi-numerology networks. To fill this gap, we propose an efficient power-domain approach to IGE for the resource allocation in multi-cell multi-numerology networks. Unlike traditional reference signal-based approaches that consume frequency-time resources, our approach uses power as a new dimension for the estimation of channel gains. By carefully controlling the transmit powers of base stations, our approach is capable of estimating both the intra- and inter-cell interference channel gains, useful to resource allocation. Further, as a power-domain approach, it can be seamlessly integrated with the resource allocation in multi-numerology networks, such that IGE and resource allocation can be conducted simultaneously. We derive the necessary conditions for power-domain IGE and formulate the joint optimization problem of IGE and resource allocation. Our simulation results show that power-domain IGE can accurately estimate the interference channel gains and incurs low power overhead.

In-band Network Telemetry (INT) has emerged as a promising network measurement technology. However, existing network telemetry systems lack the flexibility to meet diverse telemetry requirements and are also difficult to adapt to dynamic network environments. In this paper, we propose AdapINT, a versatile and adaptive in-band network telemetry framework assisted by dual-timescale probes, including long-period auxiliary probes (APs) and short-period dynamic probes (DPs). Technically, the APs collect basic network status information, which is used for the path planning of DPs. To achieve full network coverage, we propose an auxiliary probes path deployment (APPD) algorithm based on the Depth-First-Search (DFS). The DPs collect specific network information for telemetry tasks. To ensure that the DPs can meet diverse telemetry requirements and adapt to dynamic network environments, we apply the deep reinforcement learning (DRL) technique and transfer learning method to design the dynamic probes path deployment (DPPD) algorithm. The evaluation results show that AdapINT can flexibly customize the telemetry system to accommodate diverse requirements and network environments. In latency-aware networks, AdapINT effectively reduces telemetry latency, while in overhead-aware networks, it significantly lowers the control overheads.

Traditional wisdom for network management allocates network resources separately for the measurement and communication tasks. Heavy measurement tasks may compete limited resources with communication tasks and significantly degrade overall network performance. It is therefore challenging for the interference graph, deemed as incurring heavy measurement overhead, to be used in practice in wireless networks. To address this challenge in wireless sensor networks, our core insight is to use power as a new dimension for interference graph estimation (IGE) such that IGE can be done simultaneously with the communication tasks using the same frequency-time resources. We propose to marry power-domain IGE with concurrent flooding to achieve simultaneous measurement and communication in BLE networks, where the power linearity prerequisite for power-domain IGE holds naturally true in concurrent flooding. With extensive experiments, we conclude the necessary conditions for the power linearity to hold and analyze several non-linearity issues of power related to hardware imperfections. We design and implement network protocols and power control algorithms for IGE in multi-hop BLE networks and conduct experiments to show that the marriage is mutually beneficial for both IGE and concurrent flooding. Furthermore, we demonstrate the potential of IGE in improving channel map convergence and convergecast in BLE networks.

Traditional wisdom for network resource management allocates separate frequency-time resources for measurement and data transmission tasks. As a result, the two types of tasks have to compete for resources, and a heavy measurement task inevitably reduces available resources for data transmission. This prevents interference graph estimation (IGE), a heavy yet important measurement task, from being widely used in practice. To resolve this issue, we propose to use power as a new dimension for interference measurement in full-duplex millimeter-wave backhaul networks, such that data transmission and measurement can be done simultaneously using the same frequency-time resources. Our core insight is to consider the mmWave network as a linear system, where the received power of a node is a linear combination of the channel gains. By controlling the powers of transmitters, we can find unique solutions for the channel gains of interference links and use them to estimate the interference. To accomplish resource allocation and IGE simultaneously, we jointly optimize resource allocation and IGE with power control. Extensive simulations show that significant links in the interference graph can be accurately estimated with minimal extra power consumption, independent of the time and carrier frequency offsets between nodes.

The massiveness of the Internet makes it rather difficult to achieve high-coverage monitoring at scale with reasonable overhead. The traditional wisdom for scalable and high-coverage Internet monitoring is to consider clients in each /24 as a whole and only monitor the representatives, either by active probing or by passive traffic sniffing, such that performance of the rest can be predicted for high coverage. There are two basic assumptions behind this traditional wisdom: 1) clients in the same /24 have similar performance, and 2) tracking all targeted /24s equates to full-coverage monitoring. With the increasing prevalence of load balancing, both assumptions are now questionable. Through large-scale measurements, we evaluate the coverage and predictability issues of current practices, motivate the necessity of link-level fine-grained, high-coverage monitoring, and present new insights on how to achieve it. Our key findings are: 1) the current practices using the representatives of /24s may fail to capture the changes of up to 85% of links in the Internet; 2) the path difference between client flows to the same /24 is both significant and prevalent; 3) it is possible to cover most of the visible links from DCs to both small and large prefixes by carefully choosing client flows; 4) high-coverage monitoring can be achieved with at least three times less overhead than direct link monitoring.

Traditional wisdom for network resource manage-ment is to allocate separate frequency-time resources for measurement and data transmission tasks. As a result, the two types of tasks have to compete for resources, and a heavy measurement task inevitably reduces available resources for data transmission. This prevents interference graph estimation (IGE), a heavy yet important measurement task, from being widely used in practice. To resolve this issue, we propose to use power as a new dimension for interference measurement in full-duplex mmWave backhaul networks, such that no extra frequency-time resources are needed for measurement. Our core insight is to consider the mm Wave network as a linear system, where the received powers of a node can be expressed as the product of the powers of transmitters and the equivalent channel gains from the transmitters to the node. By controlling the powers of transmitters, we can find unique solutions for the equivalent channel gains, which will then be used to estimate interference. To accomplish resource allocation and IGE simultaneously, we jointly optimize resource allocation and IGE with power control. Extensive simulations show that significant links in the interference graph can be accurately estimated with less than 3 % increase in power consumption, independent of the time synchronization and carrier frequency offset (CFO) estimation errors between nodes.

In Internet mapping, IP address space is divided into a set of client aggregation units, which are the finest-grained units for global load balancing. Choosing the proper level of aggregation is a complex problem, which determines the number of aggregation units that a mapping system has to maintain and client redirection. In this paper, using Internet-wide measurements provided by a commercial global load balancing service provider, we show that even for the best existing client aggregation, almost 17% of clients have latency more than 50 ms apart from the average latency of clients in the same aggregation unit. To address this, we propose a data-driven client aggregation, AP-atoms, which can trade off scalability for accuracy and adapt for changing network conditions. Since AP-atoms are obtained from the passive measurements of existing traffic between server providers and clients, no extra measurement overheads are incurred. Our experiments show that by using the same scale of client aggregations, AP-atoms can reduce the number of widely dispersed clients by almost $2\times $ and the 98th percentile difference in clients' latencies by almost 100 ms.

Charging electric vehicles (EVs) at home is attractive to EV users. However, when the penetration level of EVs becomes high, a distribution grid suffers from problems such as under-voltage and transformer overloading. EV users also experience a fairness problem, i.e., the limited capacity is unfairly shared among EVs. To solve these problems, a physical fair-queueing framework is established for EV charging. In this framework, a distribution sub-grid is first mapped to a multi-server queueing system, and then a fluid-model based queueing scheme called physical multi-server generalized processor sharing (pMGPS) is designed. pMGPS ensures perfect fairness but cannot be used practically due to its nature of fluid model. To this end, a packetized scheme called physical start-time fair queueing (pSTFQ) is developed to schedule tasks of EV charging. The fairness performance of the pSTFQ scheduling scheme is characterized by the ratio of energy difference between pSTFQ and pMGPS. This critical performance metric is studied through theoretical analysis and is also evaluated via simulations. Performance results show that the pSTFQ scheduling scheme achieves an energy difference ratio of less than 4 percent in various scenarios without causing under-voltage and transformer overloading problems.

Maximum power point tracking (MPPT) achieves maximum power output for a photovoltaic (PV) system under various environmental conditions. It significantly improves the energy efficiency of a specific PV system. However, when an increasing number of PV systems are connected to a distribution grid, MPPT poses several risks to the grid: 1)over-voltage problem, i.e., voltage in the distribution grid exceeds its rating; and 2)reverse power-flow problem, i.e., power that flows into the grid exceeds an allowed level. To solve these problems, power point tracking of all PV systems in the same distributed grid needs to be coordinated via a communication network. Thus, coordinated power point tracking (CPPT) is studied in this paper. First, an optimization problem is formulated to determine the power points of all PV systems, subject to the constraints of voltage, reverse power flow, and fairness. Conditions that obtain the optimal solution are then derived. Second, based on these conditions, a distributed and practical CPPT scheme is developed. It coordinates power points of all PV systems via a communication network, such that: 1) voltage and reverse power flow are maintained at a normal level; and 2) each PV system receives a fair share of surplus power. Third, a wireless mesh network (WMN) is designed to support proper operation of the distributed CPPT scheme. CPPT is evaluated through simulations that consider close interactions between WMN and CPPT. Performance results show that: 1) CPPT significantly outperforms MPPT by gracefully avoiding both overvoltage and reverse power-flow problems; 2) CPPT achieves fair sharing of surplus power among all PV systems; and 3) CPPT can be reliably conducted via a WMN.