Joint resource optimization for OFDMA cellular networks with user cooperation and QoS provisioning

Abstract

In this paper, a joint resource optimization scheme is designed for orthogonal frequency division multiple access (OFDMA) cellular wireless networks with multi-user cooperation. Joint relay selection, subcarrier allocation and pairing and power allocation algorithms are developed with the objective of maximizing the total capacity of the system considering the quality of service (QoS) requirements of the users. The optimization problem is a mixed integer nonlinear program (MINLP), which is often very difficult to solve in its original form. We provide a novel optimization framework to solve such non-linear optimization problems. The joint relay selection and subcarrier allocation problem is modified to a linear assignment problem and an efficient algorithm is developed to obtain the optimal assignment solution based on the Hungarian method. We propose computationally efficient solution to the joint resource optimization problem via dual decomposition method. Numerical results demonstrate the effectiveness of our proposed scheme. I. INTRODUCTIONCooperative relaying technology has been found to greatly improve the spectral efficiency, coverage area, and network lifetime. This technology is promising for next generation wireless networks such as 3GPP, LTE-A and IEEE 802.16. There are different relaying protocols and among them decodeand-forward (DF) and amplify-and-forward (AF) are more popular [1]. In DF relaying, the relay receives the signal from the source, decodes it, and forwards it to the destination, while in AF relaying, the relay simply amplifies the received signal from the source and forwards it to the destination. However, installing fixed relay nodes has a significant increase in cost for the operators. Therefore, users with good channel condition and low traffic demands can be used as mobile relays without increasing infrastructure cost to the operators [2], [3].Orthogonal frequency division multiple access (OFDMA) systems are appealing for broadband cellular networks. In OFDMA systems, the frequency spectrum is divided into a number of smaller bands known as subcarriers which can be allocated to multiple users. Due to frequency diversity and multiuser diversity, OFDMA systems have high spectral efficiency and they are robust against multipath interference. These properties make OFDMA suitable for wireless networks such as worldwide interoperability for microwave access (WiMAX) and LTE-A. To improve the performance with OFDMA further, several works have been done combining OFDMA with cooperative relaying systems [4]- [6].

Energy-Efficient Small Cell With Spectrum-Power Trading

“…From (39), (40), and (41), we note that the feasible transmit power region composed of S * and S is larger than the feasible region of S, which leads that the inequality (a) may not hold in (35). However, based on this, it is straightforward to show that inequality (d) still holds in (36).…”

“…First, the complexity for obtaining bandwidth and power allocation variables in Algorithm 1 linearly increases with the number of MUs and the number of SUs, i.e., O(K + N). Second, the complexities of the ellipsoid method for updating dual variables [37] and the Dinkelbach method for updating q [13], [35], [40], [41] are both independent of K and N. Finally, the complexity of performing the MU selection linearly increases with K. Therefore, the total complexity of Algorithm 2 is…”

Centralized versus decentralized multi-cell resource and power allocation for multiuser OFDMA networks

“…For a given multiuser OFDMA network, resource and power allocation problem is formulated as a centralized optimization problem [10,11,12]. Centralized 70 inter-cell coordination is therefore required to find the optimal solution, where the necessary information about SINR, power allocation, and resource usage are sent to a centralized coordination entity.…”

“…The majority of state-of-the-art contributions formulate the resource and 35 power allocation problem for a single cell network [10,11,12], or do not consider the impact of ICI on system performance. For instance, the tradeoff between spectral efficiency and energy efficiency is addressed in [12], and a low-complexity suboptimal algorithm is proposed to allocate RBs for practical applications of the tradeoff.…”

“…The majority of state-of-the-art contributions that formulate spectral efficiency or energy efficiency problems as centralized optimization problems, neglect the impact of ICI on system performance [10,11,12], or introduce suboptimal approaches to solve resource and power allocation problems [21,22,23].…”