Resource Allocation for Delay Differentiated Traffic in Multiuser OFDM Systems

Most existing work on adaptive allocation of subcarriers and power in multiuser orthogonal frequency division multiplexing (OFDM) systems has focused on homogeneous traffic consisting solely of either delay-constrained data (guaranteed service) or non-delay-constrained data (best-effort service). In this paper, we investigate the resource allocation problem in a heterogeneous multiuser OFDM system with both delay-constrained (DC) and non-delay-constrained (NDC) traffic. The objective is to maximize the sum-rate of all the users with NDC traffic while maintaining guaranteed rates for the users with DC traffic under a total transmit power constraint. Through our analysis we show that the optimal power allocation over subcarriers follows a multi-level water-filling principle; moreover, the valid candidates competing for each subcarrier include only one NDC user but all DC users. By converting this combinatorial problem with exponential complexity into a convex problem or showing that it can be solved in the dual domain, efficient iterative algorithms are proposed to find the optimal solutions. To further reduce the computational cost, a low-complexity suboptimal algorithm is also developed. Numerical studies are conducted to evaluate the performance the proposed algorithms in terms of service outage probability, achievable transmission rate pairs for DC and NDC traffic, and multiuser diversity.Comment: 29 pages, 8 figures, submitted to IEEE Transactions on Wireless Communication

Energy-Efficient Resource Allocation in Multiuser OFDM Systems with Wireless Information and Power Transfer

In this paper, we study the resource allocation algorithm design for multiuser orthogonal frequency division multiplexing (OFDM) downlink systems with simultaneous wireless information and power transfer. The algorithm design is formulated as a non-convex optimization problem for maximizing the energy efficiency of data transmission (bit/Joule delivered to the users). In particular, the problem formulation takes into account the minimum required system data rate, heterogeneous minimum required power transfers to the users, and the circuit power consumption. Subsequently, by exploiting the method of time-sharing and the properties of nonlinear fractional programming, the considered non-convex optimization problem is solved using an efficient iterative resource allocation algorithm. For each iteration, the optimal power allocation and user selection solution are derived based on Lagrange dual decomposition. Simulation results illustrate that the proposed iterative resource allocation algorithm achieves the maximum energy efficiency of the system and reveal how energy efficiency, system capacity, and wireless power transfer benefit from the presence of multiple users in the system.Comment: 6 pages. The paper has been accepted for publication at the IEEE Wireless Communications and Networking Conference (WCNC) 2013, Shanghai, China, Apr. 201

Subcarrier and Power Allocation in WiMAX

Worldwide Interoperability for Microwave Access (WiMAX) is one of the latest technologies for providing Broadband Wireless Access (BWA) in a metropolitan area. The use of orthogonal frequency division multiplexing (OFDM) transmissions has been proposed in WiMAX to mitigate the complications which are associated with frequency selective channels. In addition, the multiple access is achieved by using orthogonal frequency division multiple access (OFDMA) scheme which has several advantages such as flexible resource allocation, relatively simple transceivers, and high spectrum efficient. In OFDMA the controllable resources are the subcarriers and the allocated power per subband. Moreover, adaptive subcarrier and power allocation techniques have been selected to exploit the natural multiuser diversity. This leads to an improvement of the performance by assigning the proper subcarriers to the user according to their channel quality and the power is allocated based on water-filling algorithm. One simple method is to allocate subcarriers and powers equally likely between all users. It is well known that this method reduces the spectral efficiency of the system, hence, it is not preferred unless in some applications. In order to handle the spectral efficiency problem, in this thesis we discuss three novel resources allocation algorithms for the downlink of a multiuser OFDM system and analyze the algorithm performances based on capacity and fairness measurement. Our intensive simulations validate the algorithm performances.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Optimization of resource allocation for the downlink of multiuser MISO-OFDM systems

Proceedings of the IEEE International Conference on Telecommunications, 2010, p. 266-271In this paper, we investigate the optimization problem of resource allocation in downlink of multiuser MISO-OFDM system. Multiple users with different BER and minimum transmission rate requirements are considered. We propose a novel heuristic allocation algorithm (HAA) of radio resource, which minimizes the total transmit power of the base station while meeting individual users'QoS requirements. The proposed algorithm combines antenna selection, subcarrier, bit and power allocation together, pre-estimating number of subcarriers assigned to each user and number of bits loaded for each subcarrier to reduce search number, reducing about 8 dB average bit SNR comparing with fixed allocation algorithm (FAA), and acquiring asymptotic average bit SNR of optimal allocation algorithm (OAA) with much lower complexity. © 2009 IEEE.published_or_final_versio

Low complexity subcarrier and power allocation algorithm for uplink OFDMA systems

In this article, we consider the joint subcarrier and power allocation problem for uplink orthogonal frequency division multiple access system with the objective of weighted sum-rate maximization. Since the resource allocation problem is not convex due to the discrete nature of subcarrier allocation, the complexity of finding the optimal solution is extremely high. We use the optimality conditions for this problem to propose a suboptimal allocation algorithm. A simplified implementation of the proposed algorithm has been provided, which significantly reduced the algorithm complexity. Numerical results show that the presented algorithm outperforms the existing algorithms and achieves performance very close to the optimal solution