Device-to-Device Communications Underlaying Cellular Networks

By Daquan Feng, Lu Lu, Yuan-Wu Yi, Geoffrey Y. Li, Gang Feng, Shaoqian Li. IEEE Transactions on Communications, 2013

861 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/ee6bbd2dfbf402a6315b286f4874b691da36728a

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In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

An Overview of Sustainable Green 5G Networks (2016, background): “D2D communications [11] enable densified local reuse of spectrum and can be regarded as a special case of ultra-dense networks with the smallest cell consisting of two devices as the transmitter and the receiver.”
Device-to-device communication in 5G cellular networks: challenges, solutions, and future directions (2014, background): “To address resource allocation for this type of communication, different approaches such as resource pooling [4], non-cooperative game or bargaining game [12], admission control and power allocation [6], cluster partitioning, and relay selection [13] can be employed.”
Modulation and Multiple Access for 5G Networks (2017, background): “Device-todevice (D2D) communications [92], [93], a popular research area recently, turn to a special CR if the two users in D2D communications are regarded as a simple CR network.”
A Survey on Device-to-Device Communication in Cellular Networks (2013, methodology): “There are many works which focus on the improving the system performance while maintaining certain QoS/power constraints [63]–[68].”
Fundamental Green Tradeoffs: Progresses, Challenges, and Impacts on 5G Networks (2016, background): “The overall system EE and the individual user EE are both optimized in [82] via joint channel assignment and power allocation under the assumption that there is no interference among D2D pairs.”
A Survey of Device-to-Device Communications: Research Issues and Challenges (2018, background): “introduced in the fourth generation, after LTE release 12 in 2012 [5].”
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, background): “g the network capacity. Moreover, it is potentially beneﬁcial in terms of enhancing the energy efﬁciency (EE), also reducing the transmission delay and improving the network’s fairness to users [73], [74], which is also closely related to machine-to-machine (M2M) communications. The corresponding ma”
Device-to-device communications in cellular networks (2016, methodology): “It has been demonstrated in [5] that up to 60 percent of additional users (D2D pairs) can be accessed while maintaining the performance of the existing RCUs even in a fully loaded cellular network.”
5G roadmap: 10 key enabling technologies (2016, methodology): “Approaches like resource ooling [162] , admission control and power allocation [158] , reay selection and game theoretic techniques like non-cooperative ame and bargaining can be used to moderate the interference 163,164] .”
Deep Learning Based Transmit Power Control in Underlaid Device-to-Device Communication (2019, background): “Moreover, the resource allocation to maximize the throughput of both CUE and DUE was considered in [7] where the quality-of-service requirements of both CUE and DUE were also taken into account.”
Survey of Fog Computing: Fundamental, Network Applications, and Research Challenges (2018, background): “ing, the edge devices which are in proximity, can obtain data from other users through direct link such as Device-to-Device (D2D) communication [21], [22] and adjacent Small Cell (SC)”
Resource Allocation for D2D-Enabled Vehicular Communications (2017, methodology): “For more practical scenarios with multiple cellular and D2D users, spectrum and power allocation design has been considered in [16] and [17].”