A stable matching algorithm for resource allocation for underlaying device-to-device communications

Islam, Mohammad Tauhidul; Taha, Abd‐Elhamid M.; Akl, Selim G.; Abu-Elkheir, Mervat

doi:10.1109/icc.2016.7511459

Cited by 32 publications

(57 citation statements)

References 18 publications

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“…We examine a system model consisting of one eNB, a number of D2D pairs, and a number of cellular UEs. Generally, the number of the D2D pairs is lower than the number of the cellular UEs . Accordingly, we consider a system with n cellular UEs and m D2D pairs where a D2D pair reuses the RBs of a cellular UE.…”

Section: System Model and Channel Modelmentioning

confidence: 99%

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An online resource allocation algorithm to minimize system interference for inband underlay D2D communications

Hassan

Hussain

Hossen

et al. 2019

Int J Communication

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Summary This paper addresses the research question of total system interference minimization while maintaining a target system sum rate gain in an inband underlay device‐to‐device (D2D) communication. To the best of our knowledge, most of the state of the art research works exploit offline resource allocation algorithms to address the research problem. However, in Long‐Term Evolution (LTE) and beyond systems (4G, 5G, or 5G+), offline resource allocation algorithms do not comply with the fast scheduling requirements because of the high data rate demand. In this paper, we propose a bi‐phase online resource allocation algorithm to minimize the total system interference for inband underlay D2D communication. Our proposed algorithm assumes D2D pairs as a set of variable elements whereas takes the cellular user equipment (UEs) as a set of constant elements. The novelty of our proposed online resource allocation algorithm is that it incurs a minimum number of changes in radio resource assignment between two successive allocations among the cellular UEs and the D2D pairs. Graphical representation of the simulation results suggests that our proposed algorithm outperforms the existing offline algorithm considering number of changes in successive allocation for a certain percentage of sum rate gain maintaining the total system interference and total system sum rate very similar.

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Section: System Model and Channel Modelmentioning

confidence: 99%

“…Any D2D pair d j ∈ D consists of a transmitting device

d_{j}^{t}

and a receiving device

d_{j}^{r}

. Though the D2D pairs interact directly, the eNB handles the resource allocation …”

Section: System Model and Channel Modelmentioning

confidence: 99%

An online resource allocation algorithm to minimize system interference for inband underlay D2D communications

Hassan

Hussain

Hossen

et al. 2019

Int J Communication

View full text Add to dashboard Cite

show abstract

“…A deferred acceptance–based algorithm is proposed in Islam et al to solve the same problem where the D2D pairs and the cellular UEs maintain a preference list of nodes (D2D pairs or cellular UEs) they wish to share with. The preference list is calculated on the basis of the increasing order of the proximity, which is not the best approach for this optimization problem.…”

Section: Background and Related Workmentioning

confidence: 99%

“…A D2D pair d j ∈ D contains a receiving device

d_{j}^{r}

and a transmitting device

d_{j}^{t}

. Though the D2D pairs directly communicate with each other, the connection establishment and the resource allocation are handled by the eNB …”

Section: System Model and Channel Modelmentioning

confidence: 99%

“…We consider an urban microsystem, which follows Rayleigh fading path loss model. () As the channels are assumed to be orthogonal, only intrachannel interference is present. The path loss (db unit) equation is

P L = 36.7 {normall normalo normalg}_{10} false(d i s t false) + 22.7 + 26 {normall normalo normalg}_{10} false(f_{c} false),

where d i s t (meter) is the distance between a D2D transmitter and a receiver and f c (GHz) is the medium frequency.…”

Section: System Model and Channel Modelmentioning

confidence: 99%

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Relax online resource allocation algorithms for D2D communication

Hossen

Hassan

Hussain

et al. 2018

Int J Communication

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Summary Maximizing the system sumrate by sharing the resource blocks among the cellular user equipments and the D2D (device to device) pairs while maintaining the quality of service is an important research question in a D2D communication underlaying cellular networks. The problem can be optimally solved in offline by using the weighted bipartite matching algorithm. However, in long‐term evolution and beyond (4G and 5G) systems, scheduling algorithms should be very efficient where the optimal algorithm is quite complex to implement. Hence, a low complexity algorithm that returns almost the optimal solution can be an alternative to this research problem. In this paper, we propose 2 less complex stable matching–based relax online algorithms those exhibit very close to the optimal solution. Our proposed algorithms deal with fixed number of cellular user equipments and a variable number of D2D pairs those arrive in the system online. Unlike online matching algorithms, we consider that an assignment can be revoked if it improves the objective function (total system sumrate). However, we want to minimize the number of revocation (ie, the number of changes in the assignments) as a large number of changes can be expensive for the networks too. We consider various offline algorithms proposed for the same research problem as relaxed online algorithms. Through extensive simulations, we find that our proposed algorithms outperform all of the algorithms in terms of the number of changes in assignment between 2 successive allocations while maintaining the total system sumrate very close to the optimal algorithm.

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Optimizing resources allocation in a heterogeneous cloud radio access network using machine learning

Shirmohamadi

Bakhshi

Dosaranian‐Moghadam

2022

Trans Emerging Tel Tech

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In this article, we propose an iterative algorithm based on lazy learning. The proposed scheme considers two consecutive algorithms for optimal resource allocation in heterogeneous cloud radio access networks (H-CRAN). In the conventional approach, resources allocation is usually presented as a mathematical optimization problem and solved online using a real-time scenario. Due to the non-convex nature of resource allocation problems, finding optimal solutions in real-time is a difficult challenge and has high computational complexity. To overcome these challenges in this paper, the problem is first formulated as a mixed-integer nonlinear programming (MINLP) to optimize the system capacity and assign the underlay users to the subchannels, which is NP-hard. To obtain the solution, in the first algorithm, the proposed problem is first reformulated as a convex problem using a matching theory and coalition formation method. Then, in the second algorithm, the lazy learning method is employed to find the optimal solution to the resources allocation problem. The lazy algorithm uses the output results of the first algorithm as training data, which are known as historical data. Similarities between current scenario data and historical scenario data, as well as the solution of resources allocation in historical scenarios, can be exploited to improve the resource allocation of the current scenario. Simulation results confirm that compared with the conventional approach, the suggested technique can significantly improve the system performance in terms of the total users assigned, overall throughput, the pace of resource allocation, and user fairness.

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A stable matching algorithm for resource allocation for underlaying device-to-device communications

Cited by 32 publications

References 18 publications

An online resource allocation algorithm to minimize system interference for inband underlay D2D communications

An online resource allocation algorithm to minimize system interference for inband underlay D2D communications

Relax online resource allocation algorithms for D2D communication

Optimizing resources allocation in a heterogeneous cloud radio access network using machine learning

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