Yeakub Hassan

In a device to device (D2D) communication underlaying cellular network, total system sum rate (capacity) can be improved if cellular user equipments (UEs) and D2D pairs share resource blocks (RBs). We consider an optimization problem where the objective is to maximize the total sum rate of the system by sharing RBs among cellular UEs and D2D pairs while maintaining the quality of service (QoS) requirements. We consider three approaches depending on the degree of sharing i.e., “One to One Sharing”, “One to Many Sharing” and “Many to Many Sharing”. Most of the existing algorithms consider that sharing of RBs can only improve the total system sum rate. However, sharing of RBs between a cellular UE and a D2D pair can also decrease the total system sum rate. Considering this observation, we propose an algorithm based on the weighted bipartite matching algorithm which avoids such sharing and maximize the total system sum rate for the “One to One Sharing” approach. Moreover,We propose resource allocation algorithms for “One to Many Sharing” and “Many to Many Sharing” with a target to maximize the system capacity and also provide the analysis of the proposed algorithms. Through simulations, we find that our proposed algorithms outperform the existing algorithms in terms of maximizing total system sum rate. Our proposed algorithms also perform better in terms of total interference introduced due to the sharing of RBs among cellular UEs and D2D pairs.

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.

Interference minimization while maintaining a target system sum rate by sharing radio resources among cellular user equipments (UEs) and device-to-device (D2D) pairs is an important research question in long term evolution (LTE) and beyond (4G and 5G). Total system sum rate of a cellular network can be improved if cellular UEs and D2D pairs share resource blocks. However, some sharing can also decrease the sum rate and increase the system interference. Considering this observation, we address two types of assignments (fair and restricted) in resource allocation for the interference minimization resource allocation problem. We propose a two-phase resource allocation algorithm for both fair and restricted assignments, where our objective is to minimize the system interference and at the same time, maintaining a target system sum rate. In the phase-I of our proposed algorithm, a weighted bipartite matching algorithm is used to minimize the interference and get a feasible initial solution. In some cases, we can decrease the interference introduced in phase-I of our algorithm. Therefore, in the phase-II, local search techniques are used to improve the solution. We compare the fair assignment of our proposed algorithms with a two-phase auction-based fair and interference aware resource allocation algorithm (TAFIRA), which addresses the same research problem. As well as, we compare the restricted assignment of our proposed algorithm with a minimum knapsack-based interference resource allocation algorithm (MIKIRA). We prove that the MIKIRA fails to provide feasible solutions in most of the cases. We also show that the performance ratio of the TAFIRA can be unbounded in the worst case. Moreover, in some cases, TAFIRA cannot provide any solution to the problem though the solutions exist, whereas our proposed algorithms always provide a solution whenever the solution exists. We perform extensive simulations of the algorithms and find that in all the cases, our proposed algorithm outperforms a number of state-of-the-art algorithms.

Interference minimization while maintaining a target system sum rate by sharing resources among cellular User Equipments (UEs) and Device-to-Device pairs (D2D) is an important research question in Long Term Evolution (LTE). We propose a two phase resource allocation algorithm for this research problem. In the first phase, we use the bipartite matching algorithm to minimize the interference which also avoids matching that can decrease the total system sum rate. In some cases, after the first phase, the solution may not be the optimal one. Therefore, in the second phase, we use a local search algorithm to improve the solution. We compare our algorithm with two other existing algorithms (MIKIRA and TAFIRA) which address the same research problem. We prove that MIKIRA fails to provide feasible solutions in most of the cases. We also show that the performance ratio of TAFIRA can be unbounded in the worst case. Moreover, in some cases, TAFIRA can not provide any solution of the problem where solutions exist. We prove that, our algorithm always provides the solution whenever it exists. We perform extensive simulations of the algorithms and find that in all cases, our solution is either optimal or very close to the optimal.

In a device to device (D2D) communication underlaying cellular network, total system sum rate can be improved if cellular user equipments (UEs) and D2D pairs share resource blocks (RBs). We consider such an optimization problem where the objective is to maximize the total sum rate of the system while sharing RBs among cellular UEs and D2D pairs and maintaining some quality of service (QoS) requirements. Most of the existing algorithms consider that sharing can only improve the sum rate. However, some sharing can also decrease the sum rate. Considering this observation, we design an optimal algorithm based on weighted bipartite matching which avoids such sharing and maximize the total system sum rate. We prove that our algorithm is optimal and validate the results through simulations which shows that our algorithm outperforms other existing heuristics in terms of maximizing system sum rate. Our algorithm also performs better in terms of total interference introduced through the sharing of resource blocks among cellular equipments and D2D pairs.