Joint Opportunistic Scheduling of Cellular and Device-to-Device Communications

Batista, Rodrigo L.; Silva, Carlos Felipe M.; Maciel, Tarcísio F.; Silva, José Mairton B.; Cavalcanti, Francisco R. P.

doi:10.14209/jcis.2017.7

Cited by 4 publications

(6 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, the proposed model adopts equal power allocation to guarantee that the same transmission power is allocated to all RBs. In this case, this allocated power is equal to the maximum power assigned to the BS divided by the total number of RBs [38]- [40]. This system model is illustrated in Fig.…”

Section: System Modelmentioning

confidence: 99%

Spectrum Allocation Techniques for Cognitive Radio Networks

2022

View full text Add to dashboard Cite

The remarkable growth in the demand for multimedia streaming over wireless networks is challenged by spectrum scarcity. To mitigate the effect of such a challenge, Cognitive Radio Networks (CRNs) were introduced as a promising technology since CRNs offer a great advantage to unlicensed users, also known as secondary users (SUs), by allowing them to opportunistically access the licensed bands when these bands are not used by their primary users (PUs). In this work, an LTE-based CRN is proposed with the objective of guaranteeing continuous video playback at the SUs end at acceptable perceptual quality. To accomplish this objective, different resource allocation schemes are introduced to adaptively assign the available channels to SUs while considering the quality of these channels as well as the buffer occupancies of the different SUs. In addition, a streaming algorithm is proposed to ensure the delivery of the base and enhancement layers of the scalable video frames within the delay constraints with priority given to the baselayers of the video frames to guarantee the continuity of video playback. Furthermore, adaptive modulation is used based on the channel state information (CSI) as fed-back by the SUs. The performance of the proposed schemes is evaluated through extensive Monte-Carlo simulations.INDEX TERMS Cognitive radio networks, Markov chains, primary radio user activity, resource allocation, LTE.

show abstract

Section: System Modelmentioning

confidence: 99%

Spectrum Allocation Techniques for Cognitive Radio Networks

2022

View full text Add to dashboard Cite

show abstract

“…In the same time, let the minimum transmit power of the transmitter of any D2D pair needed to properly establish and maintain a D2D connection be denoted by P min cu . As in [29]- [31], equal power allocation is considered at the BS such that all RBs have the same power. Two scenarios will be considered in this work where all nodes are assumed to be either static or will follow a specific mobility model as will be explained later in the sequel.…”

Section: System Model a Network Descriptionmentioning

confidence: 99%

“…Different metrics in the literature have been proposed to decide on the compatibility of D2D pairs. These are detailed as follows [29], [34]:…”

Section: ) Reuse Modementioning

confidence: 99%

“…Other exact methods include solving a smooth continuous relaxation of the problem using methods such as branch-and-bound [40], the standard interior-point [41] or the generalized bender decomposition algorithms [42]. The use of heuristic approaches to find suboptimal solutions with low computational complexity for such problems is a very common approach in the literature as clearly used in [29], [43], [44], and many other works. This is done without having to report the optimal solution due to its prohibitive complexity from a practical point of view.…”

Section: Proposed Qoe-aware Heuristic Algorithm For D2d Video Stmentioning

confidence: 99%

“…In more details, the mode that maximizes the D2D rate is selected for transmission taking into account the minimum rate requirements of both the CUs and D2D pairs, the D2D buffers occupancies and video quality. Different from the assumption of assigning one channel to each user in [9] and [29] or dividing the available resources equally between users in [10], here we estimate the number of Resource Blocks (RBs) to be assigned to CUs and D2D pairs according to the required rate and SNR of each user. In addition, instead of constant bit rate streaming (CBR) as in [9], we adopt SVC where each frame is encoded into one base layer and multiple enhancement layers, and the number of layers to be transmitted is determined by a proposed SVC layer selection algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A QoE-Aware Mode Selection Framework for Video Streaming in D2D Networks

2020

View full text Add to dashboard Cite

Device-to-Device (D2D) communication is a novel approach that has been proposed to enhance the performance of traditional cellular networks. This work focuses on video streaming using D2D communications underlaying a Long Term Evolution (LTE) network where resources are allocated to each D2D pair in three modes of operation; cellular, dedicated and reuse. The objective is to maximize the sumrate of the D2D pairs while maintaining continuous video playback with an acceptable quality level. Unlike previous works, which assign one channel to each user or divide the available resources equally between them or assume constant bit rate streaming (CBR), we estimate the number of Resource Blocks (RBs) needed for all users according to their rate and Signal-to-Noise Ratio (SNR) requirements and consider Scalable Video Coding (SVC). A complete Quality of Experience (QoE)-aware framework for D2D-based video streaming that includes resource allocation, power control, mode selection and SVC layer selection is then proposed while taking into consideration buffer occupancies and the video peak signal-to-noise ratio (PSNR). The efficacy of the proposed scheme is evaluated through extensive simulations and compared to the scenario where only one mode of operation is assigned to all D2D pairs. Furthermore, the effect of the number of D2D users as well as their mobility is also investigated. Simulation results show that the proposed framework improves the throughput of the D2D pairs when compared to a single mode of operation while at the same time providing better video quality and continuity.

show abstract

Energy-Efficient Interference-Aware Cognitive Machine-to-Machine Communications Underlaying Cellular Networks

Alhussien

Gulliver

2022

IEEE Access

View full text Add to dashboard Cite

Machine-to-machine (M2M) communications can effectively utilize cognitive radio (CR) to coexist with cellular users in what is known as cognitive M2M (CM2M) communications. In this system, underlay CR is used to manage spectrum sharing among machine type communication devices (MTCDs) and cellular user equipment (CUE) where the CUE are considered to be primary users (PUs) and the MTCDs are secondary users (SUs). Moreover, due to the limited battery capacity of MTCDs and diverse quality-of-service (QoS) requirements of both MTCDs and CUE, energy efficiency (EE) is critical to prolonging network lifetime. This paper investigates the power allocation problem for energy-efficient CM2M communications underlaying cellular networks. Underlay CR is employed to manage the coexistence of MTCDs and CUE and exploit spatial spectrum opportunities to improve spectrum utilization. Two power allocation problems are proposed where the first targets MTCD power consumption minimization while the second considers MTCD EE maximization subject to MTCD transmit power constraints, MTCD minimum data rate requirements, and CUE interference limits. The proposed power consumption minimization problem is transformed into a geometric programming (GP) problem and solved iteratively. The proposed EE maximization problem is a nonconvex fractional programming problem. Hence, a parametric transformation is used to convert it into an equivalent convex form and this is solved using an iterative approach. Simulation results are presented which show that the proposed algorithms provide MTCD power allocation with lower power consumption and higher EE than the equal power allocation (EPA) scheme while satisfying the constraints.INDEX TERMS Cellular user equipment (CUE), energy efficiency (EE), machine-to-machine (M2M) communications, power allocation, underlay cognitive radio (CR).

show abstract

Joint Opportunistic Scheduling of Cellular and Device-to-Device Communications

Cited by 4 publications

References 15 publications

Spectrum Allocation Techniques for Cognitive Radio Networks

Spectrum Allocation Techniques for Cognitive Radio Networks

A QoE-Aware Mode Selection Framework for Video Streaming in D2D Networks

Energy-Efficient Interference-Aware Cognitive Machine-to-Machine Communications Underlaying Cellular Networks

Contact Info

Product

Resources

About