90 research outputs found
Survey on the state-of-the-art in device-to-device communication: A resource allocation perspective
Device to Device (D2D) communication takes advantage of the proximity between the communicating devices in order to achieve efficient resource utilization, improved throughput and energy efficiency, simultaneous serviceability and reduced latency. One of the main characteristics of D2D communication is reuse of the frequency resource in order to improve spectral efficiency of the system. Nevertheless, frequency reuse introduces significantly high interference levels thus necessitating efficient resource allocation algorithms that can enable simultaneous communication sessions through effective channel and/or power allocation. This survey paper presents a comprehensive investigation of the state-of-the-art resource allocation algorithms in D2D communication underlaying cellular networks. The surveyed algorithms are evaluated based on heterogeneous parameters which constitute the elementary features of a resource allocation algorithm in D2D paradigm. Additionally, in order to familiarize the readers with the basic design of the surveyed resource allocation algorithms, brief description of the mode of operation of each algorithm is presented. The surveyed algorithms are divided into four categories based on their technical doctrine i.e., conventional optimization based, Non-Orthogonal-MultipleAccess (NOMA) based, game theory based and machine learning based techniques. Towards the end, several open challenges are remarked as the future research directions in resource allocation for D2D communication
Distributed Artificial Intelligence Solution for D2D Communication in 5G Networks
Device to Device (D2D) Communication is one of the technology components of
the evolving 5G architecture, as it promises improvements in energy efficiency,
spectral efficiency, overall system capacity, and higher data rates. The above
noted improvements in network performance spearheaded a vast amount of research
in D2D, which have identified significant challenges that need to be addressed
before realizing their full potential in emerging 5G Networks. Towards this
end, this paper proposes the use of a distributed intelligent approach to
control the generation of D2D networks. More precisely, the proposed approach
uses Belief-Desire-Intention (BDI) intelligent agents with extended
capabilities (BDIx) to manage each D2D node independently and autonomously,
without the help of the Base Station. The paper includes detailed algorithmic
description for the decision of transmission mode, which maximizes the data
rate, minimizes the power consumptions, while taking into consideration the
computational load. Simulations show the applicability of BDI agents in jointly
solving D2D challenges.Comment: 10 pages,9 figure
Energy-Efficiency Maximization for a WPT-D2D Pair in a MISO-NOMA Downlink Network
The combination of non-orthogonal multiple access (NOMA) and wireless power
transfer (WPT) is a promising solution to enhance the energy efficiency of
Device-to-Device (D2D) enabled wireless communication networks. In this paper,
we focus on maximizing the energy efficiency of a WPT-D2D pair in a
multiple-input single-output (MISO)-NOMA downlink network, by alternatively
optimizing the beamforming vectors of the base station (BS) and the time
switching coefficient of the WPT assisted D2D transmitter. The formulated
energy efficiency maximization problem is non-convex due to the highly coupled
variables. To efficiently address the non-convex problem, we first divide it
into two subproblems. Afterwards, an alternating algorithm based on the
Dinkelbach method and quadratic transform is proposed to solve the two
subproblems iteratively. To verify the proposed alternating algorithm's
accuracy, partial exhaustive search algorithm is proposed as a benchmark. We
also utilize a deep reinforcement learning (DRL) method to solve the non-convex
problem and compare it with the proposed algorithm. To demonstrate the
respective superiority of the proposed algorithm and DRL-based method,
simulations are performed for two scenarios of perfect and imperfect channel
state information (CSI). Simulation results are provided to compare NOMA and
orthogonal multiple access (OMA), which demonstrate the superior performance of
energy efficiency of the NOMA scheme
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