1,367 research outputs found
Energy-Efficient Resource Allocation for Device-to-Device Underlay Communication
Device-to-device (D2D) communication underlaying cellular networks is
expected to bring significant benefits for utilizing resources, improving user
throughput and extending battery life of user equipments. However, the
allocation of radio and power resources to D2D communication needs elaborate
coordination, as D2D communication can cause interference to cellular
communication. In this paper, we study joint channel and power allocation to
improve the energy efficiency of user equipments. To solve the problem
efficiently, we introduce an iterative combinatorial auction algorithm, where
the D2D users are considered as bidders that compete for channel resources, and
the cellular network is treated as the auctioneer. We also analyze important
properties of D2D underlay communication, and present numerical simulations to
verify the proposed algorithm.Comment: IEEE Transactions on Wireless Communication
Radio Link Enabler for Context-aware D2D Communication in Reuse Mode
Device-to-Device (D2D) communication is considered as one of the key
technologies for the fifth generation wireless communication system (5G) due to
certain benefits provided, e.g. traffic offload and low end-to-end latency. A
D2D link can reuse resource of a cellular user for its own transmission, while
mutual interference in between these two links is introduced. In this paper, we
propose a smart radio resource management (RRM) algorithm which enables D2D
communication to reuse cellular resource, by taking into account of context
information. Besides, signaling schemes with high efficiency are also given in
this work to enable the proposed RRM algorithm. Simulation results demonstrate
the performance improvement of the proposed scheme in terms of the overall cell
capacity
Resource Allocation for Network-Integrated Device-to-Device Communications Using Smart Relays
With increasing number of autonomous heterogeneous devices in future mobile
networks, an efficient resource allocation scheme is required to maximize
network throughput and achieve higher spectral efficiency. In this paper,
performance of network-integrated device-to-device (D2D) communication is
investigated where D2D traffic is carried through relay nodes. An optimization
problem is formulated for allocating radio resources to maximize end-to-end
rate as well as conversing QoS requirements for cellular and D2D user equipment
under total power constraint. Numerical results show that there is a distance
threshold beyond which relay-assisted D2D communication significantly improves
network performance when compared to direct communication between D2D peers
Deep Reinforcement Learning for Resource Allocation in V2V Communications
In this article, we develop a decentralized resource allocation mechanism for
vehicle-to-vehicle (V2V) communication systems based on deep reinforcement
learning. Each V2V link is considered as an agent, making its own decisions to
find optimal sub-band and power level for transmission. Since the proposed
method is decentralized, the global information is not required for each agent
to make its decisions, hence the transmission overhead is small. From the
simulation results, each agent can learn how to satisfy the V2V constraints
while minimizing the interference to vehicle-to-infrastructure (V2I)
communications
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