In this paper, we demonstrate the optimal power allocation for QPSK, 16-QAM, and 64-QAM modulation schedules and the role of channel quality indicator (CQI). We used sigmoidal-like utility functions to represent the probability of successful reception of packets at user equipment (UE). CQI as a feedback to the base station (BS) indicates the data rate that a downlink channel can support. With Levenberg-Marquardt (LM) Optimization method, we present utility functions of different CQI values for standardized 15 Modulation order and Coding Scheme (MCS) in @math Generation Partnership Project (3GPP). Finally, we simulate and show the results of the optimal power allocation algorithm.
The MCS selection has been widely studied. Work in @cite_1 - @cite_17 has proposed adaptive modulation and coding (AMC) to enhance the system throughput according to the channel quality. The CQI, the only feedback to BS, corresponds to a resource block (RB) or multiple RB's in the form of MCS index @cite_3 . And CQI value provides important information in link adaption. The study in @cite_3 developed several MCS selection schemes for downlink transmission in LTE systems by using the effective packed-level SINR. Thresholds were set to the SINR values with the Block Error Rate (BLER) smaller than 10 This paper is organized as follows. We first introduce the system model set up in Section . Then we review the CQI with more details in Section . In Section , we describe the solution for mapping the CQI values to the utility function. Section that briefly describes the optimal power allocation algorithm that was proposed in @cite_7 . In Section , we discuss the simulation set up and the results along with a discussion. Finally, Section concludes this paper.
@cite_1 @cite_7 @cite_3 @cite_17
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In this paper, we investigate resource block (RB) assignment and modulation-and-coding scheme (MCS) selection to maximize downlink throughput of long-term evolution (LTE) systems, where all RB's assigned to the same user in any given transmission time interval (TTI) must use the same MCS. We develop several effective MCS selection schemes by using the effective packet-level SINR based on exponential effective SINR mapping (EESM), arithmetic mean, geometric mean, and harmonic mean. From both analysis and simulation results, we show that the system throughput of all the proposed schemes are better than that of the scheme in [7]. Furthermore, the MCS selection scheme using harmonic mean based effective packet-level SINR almost reaches the optimal performance and significantly outperforms the other proposed schemes. In this paper, we study resource allocation in a downlink OFDMA system assuming imperfect channel state information (CSI) at the transmitter. To achieve the individual QoS of the users in OFDMA system, adaptive resource allocation is very important, and has therefore been an active area of research. However, in most of the the previous work perfect CSI at the transmitter is assumed which is rarely possible due to channel estimation error and feedback delay. In this paper, we study the effect of channel estimation error on resource allocation in a downlink OFDMA system. We assume that each user terminal estimates its channel by using an MMSE estimator and sends its CSI back to the base station through a feedback channel. We approach the problem by using convex optimization framework, provide an explicit closed form expression for the users' transmit power and then develop an optimal margin adaptive resource allocation algorithm. Our proposed algorithm minimizes the total transmit power of the system subject to constraints on users' average data rate. The algorithm has polynomial complexity and solves the problem with zero optimality gaps. Simulation results show that our algorithm highly improves the system performance in the presence of imperfect channel estimation. In Long Term Evolution (LTE), the provision of Quality of Service (QoS) strongly depends on the efficiency with which control resources are allocated. Unfortunately, the design of the control channel, the Physical Downlink Control Channel (PDCCH), is not flexible enough to support Intercell Interference Coordination (ICIC) and hence, its degradation at cell edges (a well known issue in LTE) severely jeopardizes system performance. This problem acquires especial relevance in realistic large scale deployments where Signal to Interference plus Noise Ratio (SINR) distributions are much worse than the ones predicted by means of hexagonal layouts. Thus, this paper investigates the challenge of improving SINR levels at cell edges in order to enhance the performance of the PDCCH. The proposed scheme adjusts the power allocated to the PDCCH at each cell in order to improve SINR distributions (minimizing the usage of control resources) and, due to its multiobjective nature, it considers several perspectives of the problem. The results show that gains in the order of 25 can be obtained in severely interfered cells while overall energy savings are around 50 . We propose an optimal modulation and coding scheme (MCS) selection criterion for maximizing user throughput in cellular networks. The proposed criterion adopts both the Chase combining and incremental redundancy based hybrid automatic repeat request (HARQ) mechanisms and it selects an MCS level that maximizes the expected throughput which is estimated by considering both the number of transmissions and successful decoding probability in HARQ operation. We also prove that the conventional MCS selection rule is not optimized with mathematical analysis. Through link-level and system-level simulations, we show that the proposed MCS selection criterion yields higher average cell throughput than the conventional MCS selection schemes for slowly varying channels.