Unified approach to cross-layer scheduling and resource allocation in OFDMA wireless networks

Abstract

Orthogonal frequency division multiple access (OFDMA) has been selected as the core physical layer access scheme for state-of-the-art and next-generation wireless communications standards. In these systems, scheduling and resource allocation algorithms, jointly assigning transmission data rates, bandwidth and power, become crucial to optimize the resource utilization while providing support to multimedia applications with heterogeneous quality of service (QoS) requirements. In this article, a unified framework for channeland queue-aware QoS-guaranteed cross-layer scheduling and resource allocation algorithms for heterogeneous multiservice OFDMA wireless networks is presented. The framework encompasses different types of traffic, uniform and continuous power allocation, discrete and continuous rate allocation, and protocols with different amounts of channel-and queue-awareness. System parameters and QoS requirements are projected into utility functions and the optimization problem is then formulated as a constrained utility maximization problem. Optimal solutions for this problem are obtained for the uniform power allocation schemes, and novel quasioptimal algorithms are proposed for the adaptive power allocation strategies. Remarkably, these techniques exhibit complexities that are linear in the number of resource units and users. Simulation results demonstrate the validity and merits of the proposed cross-layer unified approach.

Adaptive Uniform Channel Decomposition in MU-MIMO-OFDM: Application to IEEE 802.11ac

“…As summarized in [20], the weights w k can be chosen based on different optimization criteria resulting in a wide class of scheduling algorithms (e.g., the max-sum rate (MSR), the proportional fair (PF), the modified largest weighted delay first (M-LWDF), or the exponential (EXP)). Building on the semiorthogonal user selection (SUS) algorithm proposed by Yoo and Goldsmith in [3], the GMSUS algorithm can be summarized as follows:…”

Dynamic Resource Management for LTE-Based Hybrid Access Femtocell Systems

“…: R → R, p ∈ R |K h | , and U is the number of constraints. By mapping (26) to (16), it can be observed that S = P max,h and U = 1. To prove that the duality gap is zero, we first provide the definition of the time-sharing condition as follows.…”

“…This weight setting allows for achieving a good balance between spectral efficiency, QoS provisioning and fairness for GBR flows [21]. For nonGBR flows, we follow the proportional fairness (PF) rule [26] by setting w c (t) = 1 R (t) to attain a good tradeoff between spectral efficiency and fairness for the nonGBR flows [21].…”

“…For GBR flows, we follow the modified-largest weighted delay first (M-LWDF) rule in [26] and [27] by setting w c (t) = γ c d c (t) R c (t) ; where γ c = − log δ c d c , max with δ c being the maximum probability of d c (t) exceeding d c,max andR c (t) is the average transmission rate which can be estimated asR c (t) = 0.8R c (t − 1) + 0.2R c (t) [28]. This weight setting allows for achieving a good balance between spectral efficiency, QoS provisioning and fairness for GBR flows [21].…”

Population-adaptive differential evolution-based power allocation algorithm for cognitive radio networks

“…Existing allocation algorithms include water-filling algorithm [10], transmit power adaptation method [11], and linear programming relaxation method of integer programming [12]. Femenias et al presented a unified framework for quality of service guaranteed cross-layer scheduling and resource allocation in orthogonal frequency division multiple access (OFDMA) wireless networks [13]; a new quasioptimal algorithm was proposed to handle allocation problem and simulation results demonstrated the goodness of the framework. Chen et al studied adaptive resource allocation in downlink OFDM wireless systems by using a three-round subcarrier assignment step [14].…”