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. Despite the fact that Long Term Evolution (LTE) supports high-speed data transmission and supports different packet sizes by using Adaptive Modulation and Coding (AMC), LTE is not yet optimized for low bit rate voice communication, especially in case of using LTE in Professional Mobile Radio (PMR) context. In LTE, one pair of Physical Resource Blocks (PRBs) is the smallest User Assignment Unit. However, the smallest LTE packet size is still too large in case that low bit rate voice communication is transmitted in high Modulation and Coding Scheme (MCS). This reduces the voice capacity over LTE in PMR context. Therefore, we propose a new Multi-users Multiplexing Radio Voice Transmission method for enhancing voice capacity over LTE in PMR context. In this method, voice packets from different users are multiplexed into one LTE packet in the downlink transmission. In this paper, we propose a new scheduling method that can be used to transmit the multiplexed packets to corresponding users. This paper gives also a novel algorithm for classifying and grouping voice packets of mobile users having different MCS values. In case of low and medium number of users, the results show that the average voice capacity gain of the proposed method can rise up to 89.6 . In case of high number of users, the proposed method can improve the voice capacity by factor of 6.5. Transmission capacity (TC) is a performance metric for wireless networks that measures the spatial intensity of successful transmissions per unit area, subject to a constraint on the permissible outage probability (where outage occurs when the signal to interference plus noise ratio (SINR) at a receiver is below a threshold). This volume gives a unified treatment of the TC framework that has been developed by the authors and their collaborators over the past decade. The mathematical framework underlying the analysis (reviewed in Section 2) is stochastic geometry: Poisson point processes model the locations of interferers, and (stable) shot noise processes represent the aggregate interference seen at a receiver. Section 3 presents TC results (exact, asymptotic, and bounds) on a simple model in order to illustrate a key strength of the framework: analytical tractability yields explicit performance dependence upon key model parameters. Section 4 presents enhancements to this basic model  channel fading, variable link distances (VLD), and multihop. Section 5 presents four network design case studies well-suited to TC: (i) spectrum management, (ii) interference cancellation, (iii) signal threshold transmission scheduling, and (iv) power control. Section 6 studies the TC when nodes have multiple antennas, which provides a contrast vs. classical results that ignore interference. Scalable video coding (SVC), together with adaptive modulation and coding (AMC), can improve wireless multicast streaming video by jointly performing radio resource allocation and modulation and coding scheme (MCS) selection. However, the existing schemes in the literature allocate radio resources for different video layers separately, which leads to a waste of radio resources. In this work, we introduce the notion of joint layer resource allocation which allows to jointly allocate resources to multiple video layers that are assigned the same MCS. We formulate this problem and prove it to be NP-hard. Then we develop a pseudo-polynomial algorithm that finds the optimal total system utility. Our algorithm assumes a very generic utility function and flexible video layer rates. To reduce the complexity of the algorithm, we also propose Fully Polynomial Time Approximation Schemes (FPTAS) for the same problem. Simulation results show that our optimal algorithm offers significant improvement on system utility over a previous optimal algorithm and a greedy algorithm both of which do not support joint layer resource allocation. The proposed approximation algorithm provides controllable tradeoff between performance and computational complexity and, with appropriately chosen parameters, it outperforms the greedy algorithm with 40 less running time.