35 research outputs found
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
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 and is
generated as the allotted subchannels are in the same subframe
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
Partial Enumerative Sphere Shaping
The dependency between the Gaussianity of the input distribution for the
additive white Gaussian noise (AWGN) channel and the gap-to-capacity is
discussed. We show that a set of particular approximations to the
Maxwell-Boltzmann (MB) distribution virtually closes most of the shaping gap.
We relate these symbol-level distributions to bit-level distributions, and
demonstrate that they correspond to keeping some of the amplitude bit-levels
uniform and independent of the others. Then we propose partial enumerative
sphere shaping (P-ESS) to realize such distributions in the probabilistic
amplitude shaping (PAS) framework. Simulations over the AWGN channel exhibit
that shaping 2 amplitude bits of 16-ASK have almost the same performance as
shaping 3 bits, which is 1.3 dB more power-efficient than uniform signaling at
a rate of 3 bit/symbol. In this way, required storage and computational
complexity of shaping are reduced by factors of 6 and 3, respectively.Comment: 6 pages, 6 figure
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