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

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

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.Comment: arXiv admin note: substantial text overlap with arXiv:1805.0655

Adaptive Kalman Filtering for Multi-Step ahead Traffic Flow Prediction

International audienceGiven the importance of continuous traffic flow forecasting in most of Intelligent Transportation Systems (ITS) applications, where every new traffic data become available in every few minutes or seconds, the main objective of this study is to perform a multi-step ahead traffic flow forecasting that can meet a trade-off between accuracy, low computational load, and limited memory capacity. To this aim, based on adaptive Kalman filtering theory, two forecasting approaches are proposed. We suggest solving a multi-step ahead prediction problem as a filtering one by considering pseudo-observations coming from the averaged historical flow or the output of other predictors in the literature. For taking into account the stochastic modeling of the process and the current measurements we resort to an adaptive scheme. The proposed forecasting methods are evaluated by using measurements of the Grenoble south ring

Preliminary thermodynamic assessment for advanced-cycle marine gas turbines.

Nowadays, around 80% to 90% of the international trade is made by sea, and the vast majority of the large merchant ships are powered by means of low-speed two-stroke diesel engines. Currently, gas turbines have only found niche markets in certain marine applications —such as the military or fast ferries, in which reduced size, mass, noise and vibration levels of the power plant are of paramount importance—, but their capability for entering the sector of the large cargo vessels (containers, bulk carriers, etc.) remains unexplored. Main disadvantages for the gas turbines to become a viable option for powering these ships are the very high thermal efficiency and extremely low running costs that the large diesel engines actually offer. However, as long as the current trend continues, diesel power plants are likely to lose one of their major advantages —i.e. their low operating costs— due to the ongoing regulations regarding ship emissions (IMO’s MARPOL 73/78 Annex VI) and the severe reduction in the established limits for Sulphur contents in fuels for marine applications. In such a scenario, in which emissions from ships are relentlessly being cut down lower and lower, diesel engines will have to move to more expensive distillate fuels, likely equalising their fuel prices with the gas-turbine-based power plants and, therefore, opening a potential window for the latter to enter the market. Hence, the main question posed at this point would be: given the expected equalisation in fuel prices between the gas turbines and the diesel engines, is it possible to devise an advanced gas turbine-based power plant that could outcompete the low-speed two-stroke diesel engines as prime mover for marine commercial applications? Working with this hypothesis of fuel prices no longer posing a competitive advantage for diesel engines, and making the simplifying assumption (as a sensible first-order approach to the problem) that the targeted marine applications spend most of their time at cruise speed —i.e. with the prime mover operating very close to its design point—the overall thermal efficiency at nominal conditions will be the main driving parameter to carry out the comparative assessment among the different power plants considered herein. The aim of this research is, thus, to investigate if and which sort of advanced gas-turbine-based power plants could be able to deliver thermal efficiencies high enough to outperform modern diesel engines in this regard, at least at design-point operation. The research comprises two markedly different phases: in phase one of this project, a wide range of candidate gas-turbine-based power plants are proposed and parametrically optimised for maximum thermal efficiency at design point, so that their relative merits against one another can be established consistently and the few most promising cycles can be downselected for further research; in phase two, these few downselected candidates will be physically characterised, carrying out preliminary size and mass estimations of the whole prime mover, along with some acquisition costs assessment. The results presented in this thesis demonstrate the great potential of certain advanced thermodynamic cycles, still based on a gas turbine as the core of the power plant, to achieve outstanding levels of performance at design point, effectively outcompeting the diesels in this regard by a good margin, potentially between 5 and 10 percentage points. In particular, the evaporative gas turbines and the combined-cycle power plants were the most promising candidates analysed, yielding thermal efficiencies at design point in the surrounds of 60%–65%. As it will be shown, these advanced gas turbine-based power plants are, in general, comparable in size to the diesel engines, but can mean huge reductions in the overall uninstalled dry mass of the prime mover. Despite the acquisition costs being significantly higher for the gas turbines, the great potential savings in fuel consumption — because of the higher thermal efficiencies— could make out of these advanced gas-turbine-based power plants a cost-effective solution in the long run for powering large merchant ships.PhD in Aerospac

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

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