Page 88 - Kaleidoscope Academic Conference Proceedings 2024

2024 ITU Kaleidoscope Academic Conference

Algorithm 3 Reinforcement Learning-Based Resource Algorithm 4 Beamforming Optimization for Sidelink
Allocation Communication
1: Data: State space , action space , parameters Θ 1: Data: Channel states H, number of users , number of
2: Result: Optimized Θ time slots
3: Initialization: Randomize Θ 2: Result: Beamforming vectors W
4: while not converged do 3: Initialization: Randomize W
5: Sample action from Θ ( | ) 4: while not converged do
6: Execute and observe reward and new state +1 5: Calculate SINR for each user using current
7: Update Θ using gradient descent on L(Θ) beamforming vectors W
8: end while 6: Update W to maximize total network throughput
7: Maintain power constraints and ensure fairness
4.3.3 Adaptive Resource Management
8: end while
We develop a framework for adaptive resource management 5. SIMULATION RESULTS
that adjusts to the varying demand in high-density V2X
environments. This is represented through a multi-objective As per the table 3, we present the parameters used in our
optimization model: simulation for novel approaches proposed.

5.1 Dynamic Spectrum Sharing
∑︁ ∑︁
∑︁ ∑︁
max , , − , , (5)
,
=1 =1 =1 =1
subject to ∑︁ , ≤ , ∀ ∈ {1, . . . , },
=1
, ≥ 0, ∀ , ,
where , represents the allocation of resources to user
at time , , and , represent the utility and penalty
of allocating resources at time , respectively, and Figure 1 – Utility and Penalty Over Time.
are trade-off parameters between utility maximization and
Figure 1 illustrates the temporal changes in total utility
penalty minimization.
and total penalty during the spectrum allocation process for
These models provide a robust framework for addressing
sidelink V2X communication.
the complex and dynamic needs of next-generation V2X
Total Utility (Blue Line): Aggregated utility from spectrum
communications.
allocation to all vehicles at each time step. Higher utility
indicates more effective allocation.
4.3.4 Integration with 6G Technologies: Beamforming in
Total Penalty (Orange Line): Represents allocation
Sidelink Communication
variance across vehicles, measuring fairness. A lower penalty
We propose a novel integration of beamforming techniques, suggests a more balanced distribution.
which are part of the potential 6G enhancements, with Interpretation: This figure highlights the trade-off between
sidelink V2X communication. This approach directly maximizing spectrum efficiency and ensuring equitable
addresses the need for ultra-reliable communication in dense distribution among vehicles, which is critical for maintaining
vehicular networks as discussed in [7, 9]: high performance and fairness in V2X communications.
Relevance to 6G V2X Standardization: The insights gained
from these figures are pivotal for the standardization of
∑︁
max log(1 + SINR , (w , )) (6) 6G V2X communication systems. By analyzing utility
w ,
=1 and penalty metrics, standardization efforts can prioritize
mechanisms that maximize utility while minimizing
|h w , | 2

where SINR , (w , ) = Í , , disparities, leading to more robust and equitable V2X
2
|h w , | + 2 communication frameworks.
≠ ,
2
∥w , ∥ = 1, ∀ , ,
where w , is the beamforming vector for user at time , h ,
2
is the channel vector, and is the noise power.
This formulation and associated algorithms effectively
leverage the latest technological enhancements in NR-V2X
and potential 6G technologies, providing a framework
that not only meets but exceeds current 3GPP standards,
addressing the complex and dynamic needs of modern V2X
communications. Figure 2 – Spectrum Allocation Over Time.

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