arXiv subjects

Sonia Heemstra de Groot

Publications and source records attributed to Sonia Heemstra de Groot.

13 recordsLinked to original sources

arXiv2026

Discrete Time Credit-Based Shaping for Time-Sensitive Applications in 5G/6G Networks

Future wireless networks must deliver deterministic end-to-end delays for workloads such as smart-factory control loops. On Ethernet these guarantees are delivered by the set of tools within IEEE 802.1 time sensitive networking~(TSN) standards. Credit-based shaper (CBS) is one such tool which enforces bounded latency. Directly porting CBS to 5G/6G New Radio (NR) is non-trivial because NR schedules traffic in discrete-time, modulation-dependent resource allocation, whereas CBS assumes a continuous, fixed-rate link. Existing TSN-over-5G translators map Ethernet priorities to 5G quality of service (QoS) identifiers but leave the radio scheduler unchanged, so deterministic delay is lost within the radio access network (RAN). To address this challenge, we propose a novel slot-native approach that adapts CBS to operate natively in discrete NR slots. We first propose a per-slot credit formulation for each user-equipment ({UE}) queue that debits credit by the granted transport block size~(TBS); we call this discrete-time CBS (CBS-DT). Recognizing that debiting the full {TBS} can unduly penalize transmissions that actually use only part of their grant, we then introduce and analyze {CBS} with Partial Usage ({CBS-PU}). {CBS-PU} scales the credit debit in proportion to the actual bytes dequeued from the downlink queue. The resulting CBS-PU algorithm is shown to maintain bounded credit, preserve long-term rate reservations, and guarantees worst-case delay performance no worse than {CBS-DT}. Simulation results show that slot-level credit gating--particularly CBS-PU--enables NR to export TSN class QoS while maximizing resource utilization.

cs.NI↗

arXiv2026

LOREN: Low Rank-Based Code-Rate Adaptation in Neural Receivers

Neural network based receivers have recently demonstrated superior system-level performance compared to traditional receivers. However, their practicality is limited by high memory and power requirements, as separate weight sets must be stored for each code rate. To address this challenge, we propose LOREN, a Low Rank-Based Code-Rate Adaptation Neural Receiver that achieves adaptability with minimal overhead. LOREN integrates lightweight low rank adaptation adapters (LOREN adapters) into convolutional layers, freezing a shared base network while training only small adapters per code rate. An end-to-end training framework over 3GPP CDL channels ensures robustness across realistic wireless environments. LOREN achieves comparable or superior performance relative to fully retrained base neural receivers. The hardware implementation of LOREN in 22nm technology shows more than 65% savings in silicon area and up to 15% power reduction when supporting three code rates.

cs.LG↗

arXiv2025

Reliability Modeling for Beyond-5G Mission Critical Networks Using Effective Capacity

Accurate reliability modeling for ultra-reliable low latency communication (URLLC) and hyper-reliable low latency communication (HRLLC) networks is challenging due to the complex interactions between network layers required to meet stringent requirements. In this paper, we propose such a model. We consider the acknowledged mode of the radio link control (RLC) layer, utilizing separate buffers for transmissions and retransmissions, along with the behavior of physical channels. Our approach leverages the effective capacity (EC) framework, which quantifies the maximum constant arrival rate a time-varying wireless channel can support while meeting statistical quality of service (QoS) constraints. We derive a reliability model that incorporates delay violations, various latency components, and multiple transmission attempts. Our method identifies optimal operating conditions that satisfy URLLC/HRLLC constraints while maintaining near-optimal EC, ensuring the system can handle peak traffic with a guaranteed QoS. Our model reveals critical trade-offs between EC and reliability across various use cases, providing guidance for URLLC/HRLLC network design for service providers and system designers.

cs.NI↗

arXiv2021

System Level Simulation of Scheduling Schemes for C-V2X Mode-3

The 3rd Generation Partnership Project (3GPP) introduced Cellular Vehicle-to-Everything (C-V2X) as a novel technology for enabling sidelink vehicular communications. While a distributed scheduling scheme (i.e., C-V2X mode-4) has been standardized by 3GPP in order to support out-of-coverage scenarios, the design of centralized infrastructure-based schemes (i.e., C-V2X mode-3) is open to implementation. In this paper, we propose two C-V2X mode-3 schemes based on bipartite graph matching (BGM), called BGM - Pow and BGM - Dist. Specifically, BGM - Pow allocates subchannels based on the minimization of the overall power perceived by the vehicles whereas BGM - Dist is based on the maximization of the subchannels reusage distance. Through simulations we show that the proposed centralized schemes outperform C-V2X mode-4 as the subchannels can be assigned more efficiently with reduced interference.

eess.SP↗

arXiv2018

Enhanced C-V2X Mode-4 Subchannel Selection

In Release 14, the 3rd Generation Partnership Project (3GPP) introduced Cellular Vehicle--to--Everything (C-V2X) \textit{mode-4} as a novel disruptive technology to support sidelink vehicular communications in out--of--coverage scenarios. C-V2X \textit{mode-4} has been engineered to operate in a distributed manner, wherein vehicles autonomously monitor the received power across sidelink subchannels before selecting one for utilization. By means of such an strategy, vehicles attempt to $(i)$ discover and $(ii)$ reserve subchannels with low interference that may have the potential to maximize the reception likelihood of their own broadcasted safety messages. However, due to dynamicity of the vehicular environment, the subchannels optimality may fluctuate rapidly over time. As a consequence, vehicles are required to make a new selection every few hundreds of milliseconds. In consonance with 3GPP, the subchannel selection phase relies on the linear average of the perceived power intensities on each of the subchannels during a monitoring window. However, in this paper we propose a nonlinear power averaging phase, where the most up--to--date measurements are assigned higher priority via exponential weighting. We show through simulations that the overall system performance can be leveraged in both urban and freeway scenarios. Furthermore, the linear averaging can be considered as a special case of the exponentially-weighted moving average, ensuring backward compatibility with the standardized method. Finally, the 3GPP \textit{mode-4} scheduling approach is described in detail.

eess.SP↗

arXiv2018

TDOA-based Localization via Stochastic Gradient Descent Variants

Source localization is of pivotal importance in several areas such as wireless sensor networks and Internet of Things (IoT), where the location information can be used for a variety of purposes, e.g. surveillance, monitoring, tracking, etc. Time Difference of Arrival (TDOA) is one of the well-known localization approaches where the source broadcasts a signal and a number of receivers record the arriving time of the transmitted signal. By means of computing the time difference from various receivers, the source location can be estimated. On the other hand, in the recent few years novel optimization algorithms have appeared in the literature for $(i)$ processing big data and for $(ii)$ training deep neural networks. Most of these techniques are enhanced variants of the classical stochastic gradient descent (SGD) but with additional features that promote faster convergence. In this paper, we compare the performance of the classical SGD with the novel techniques mentioned above. In addition, we propose an optimization procedure called RMSProp+AF, which is based on RMSProp algorithm but with the advantage of incorporating adaptation of the decaying factor. We show through simulations that all of these techniques---which are commonly used in the machine learning domain---can also be successfully applied to signal processing problems and are capable of attaining improved convergence and stability. Finally, it is also shown through simulations that the proposed method can outperform other competing approaches as both its convergence and stability are superior.

eess.SP↗

arXiv2018

Network-Assisted Resource Allocation with Quality and Conflict Constraints for V2V Communications

The 3rd Generation Partnership Project (3GPP) has recently established in Rel. 14 a network-assisted resource allocation scheme for vehicular broadcast communications. Such novel paradigm is known as vehicle--to--vehicle (V2V) \textit{mode-3} and consists in eNodeBs engaging only in the distribution of sidelink subchannels among vehicles in coverage. Thereupon, without further intervention of the former, vehicles will broadcast their respective signals directly to their counterparts. Because the allotment of subchannels takes place intermittently to reduce signaling, it must primarily be conflict-free in order not to jeopardize the reception of signals. We have identified four pivotal types of allocation requirements that must be guaranteed: one quality of service (QoS) requirement and three conflict conditions which must be precluded in order to preserve reception reliability. The underlying problem is formulated as a maximization of the system sum-capacity with four types of constraints that must be enforced. In addition, we propose a three-stage suboptimal approach that is cast as multiple independent knapsack problems (MIKPs). We compare the two approaches through simulations and show that the latter formulation can attain acceptable performance at lesser complexity.

eess.SP↗

arXiv2018

Impact of Quantized Side Information on Subchannel Scheduling for Cellular V2X

In Release 14, 3GPP completed a first version of cellular vehicle--to--everything (C-V2X) communications wherein two modalities were introduced. One of these schemes, known as \textit{mode-3}, requires support from eNodeBs in order to realize subchannel scheduling. This paper discusses a graph theoretical approach for semi-persistent scheduling (SPS) in \textit{mode-3} harnessing a sensing mechanism whereby vehicles can monitor signal--to--interference--plus--noise ratio (SINR) levels across sidelink subchannels. eNodeBs request such measurements from vehicles and utilize them to accomplish suitable subchannel assignments. However, since SINR values---herein also referred to as side information---span a wide range, quantization is required. We conclude that 3 bits per vehicle every 100 ms can provide sufficient granularity to maintain appropriate performance without severe degradation. Furthermore, the proposed algorithm is compared against pseudo-random and greedy SPS algorithms.

eess.SP↗

arXiv2018

Poster Abstract: Hierarchical Subchannel Allocation for Mode-3 Vehicle-to-Vehicle Sidelink Communications

In V2V Mode-3, eNodeBs assign subchannels to vehicles in order for them to periodically broadcast CAM messages \cite{b2}. A crucial aspect is to ensure that vehicles in the same cluster will broadcast in orthogonal time subchannels\footnote{A subchannel is a time-frequency resource chunk capable of sufficiently conveying a CAM message.} to avoid conflicts. In general, resource/subchannel allocation problems can be represented as weighted bipartite graphs. However, in this scenario there is an additional time orthogonality constraint which cannot be straightforwardly handled by conventional graph matching methods \cite{b3}. Thus, in our approach the mentioned constraint has been taken into account. We also perform the allocation task in a sequential manner based on the constrainedness of each cluster. To illustrate the gist of the problem, in Fig. 1 we show two partially overlapping clusters where a conflict between vehicles $V_8$ and $V_{10}$ is generated as the allotted subchannels are in the same subframe.

eess.SP↗

arXiv2018

Subchannel Allocation for Vehicle-to-Vehicle Broadcast Communications in Mode-3

Conversely to mainstream cellular networks where uplink / downlink data traffic is centrally managed by eNodeBs, in vehicle-to-vehicle (V2V) broadcast communications \textit{mode-3} eNodeBs engage solely in subchannel assignment but ultimately do not intervene in data traffic control. Accordingly, vehicles communicate directly with their counterparts utilizing the allotted subchannels. Due to its loosely controlled one-to-all nature, V2V \textit{mode-3} is advantageous for time-critical applications. Nevertheless, it is imperative that the assignment of subchannels is accomplished without conflicts while at the same time satisfying quality of service (QoS) requirements. To the best of our knowledge, there exists no unified framework for V2V \textit{mode-3} that contemplates both prevention of allocation conflicts and fulfillment of QoS. Thus, four types of conditions that are of forceful character for attaining QoS-aware conflict-free allocations have been identified: $(i)$ assure differentiated QoS per vehicle, $(ii)$ preclude intra-cluster subframe conflicts, $(iii)$ secure minimal time dispersion of allotted subchannels and $(iv)$ forestall one-hop inter-cluster subchannel conflicts. Such conditions have been systematized and merged in an holistic manner allowing non-complex manipulation to perform subchannel allocation optimization. In addition, we propose a surrogate relaxation of the problem that does not affect optimality provided that certain requisites are satisfied.

eess.SP↗

arXiv2018

Parallel and Successive Resource Allocation for V2V Communications in Overlapping Clusters

The 3rd Generation Partnership Project (3GPP) has introduced in Rel. 14 a novel technology referred to as vehicle--to--vehicle (V2V) \textit{mode-3}. Under this scheme, the eNodeB assists in the resource allocation process allotting sidelink subchannels to vehicles. Thereupon, vehicles transmit their signals in a broadcast manner without the intervention of the former one. eNodeBs will thereby play a determinative role in the assignment of subchannels as they can effectively manage V2V traffic and prevent allocation conflicts. The latter is a crucial aspect to be enforced in order for the signals to be received reliably by other vehicles. To this purpose, we propose two resource allocation schemes namely bipartite graph matching-based successive allocation (BGM-SA) and bipartite graph matching-based parallel allocation (BGM-PA) which are suboptimal approaches with lesser complexity than exhaustive search. Both schemes incorporate constraints to prevent allocation conflicts from emerging. In this research, we consider overlapping clusters only, which could be formed at intersections or merging highways. We show through simulations that BGM-SA can attain near-optimal performance whereas BGM-PA is subpar but less complex. Additionally, since BGM-PA is based on inter-cluster vehicle pre-grouping, we explore different metrics that could effectively portray the overall channel conditions of pre-grouped vehicles. This is of course not optimal in terms of maximizing the system capacity---since the allocation process would be based on simplified surrogate information---but it reduces the computational complexity.

eess.SP↗

arXiv2018

Poster: Resource Allocation with Conflict Resolution for Vehicular Sidelink Broadcast Communications

In this paper we present a graph-based resource allocation scheme for sidelink broadcast V2V communications. Harnessing available information on geographical position of vehicles and spectrum resources utilization, eNodeBs are capable of allotting the same set of sidelink resources to different vehicles distributed among several communications clusters. Within a communications cluster, it is crucial to prevent time-domain allocation conflicts since vehicles cannot transmit and receive simultaneously, i.e., they must transmit in orthogonal time resources. In this research, we present a solution based on a bipartite graph, where vehicles and spectrum resources are represented by vertices whereas the edges represent the achievable rate in each resource based on the SINR that each vehicle perceives. The aforementioned time orthogonality constraint can be approached by aggregating conflicting vertices into macro-vertices which, in addition, reduces the search complexity. We show mathematically and through simulations that the proposed approach yields an optimal solution. In addition, we provide simulations showing that the proposed method outperforms other competing approaches, specially in scenarios with high vehicular density.

eess.SP↗

arXiv2018

Graph-Based Resource Allocation with Conflict Avoidance for V2V Broadcast Communications

In this paper we present a graph-based resource allocation scheme for sidelink broadcast vehicle-to-vehicle (V2V) communications. Harnessing available information on the geographical position of vehicles and spectrum resources utilization, eNodeBs are capable of allotting the same set of sidelink resources to several different vehicles in order for them to broadcast their signals. Hence, vehicles sharing the same resources would ideally be in different communications clusters for the interference level-generated due to resource repurposing-to be maintained under control. Within a communications cluster, it is crucial that vehicles transmit in orthogonal time resources to prevent conflicts as vehicles-with half-duplex radio interfaces--cannot transmit and receive simultaneously. In this research, we have envisaged a solution based on a bipartite graph, where vehicles and spectrum resources are represented by vertices whereas the edges represent the achievable rate in each resource based on the signal-to-interference-plus-noise ratio (SINR) that vehicles perceive. The aforementioned constraint on time orthogonality of allocated resources can be approached by aggregating conflicting vertices into macro-vertices which, in addition, narrows the search space yielding a solution with computational complexity equivalent to the conventional graph matching problem. We show mathematically and through simulations that the proposed approach yields an optimal solution. In addition, we provide simulations showing that the proposed method outperforms other competing approaches, specially in scenarios with high vehicular density.

eess.SP↗