Search

Reduced feedback schemes are crucial in achieving the high data rates expected of orthogonal frequency-division multiplexing-based 4G and 5G cellular systems. They ensure that the feedback overhead required for acquiring the channel-state information to enable downlink scheduling and adaptive modulation and coding at the base station (BS) does not overwhelm the uplink. We present a novel modeling and analysis of the single-user and multi-user throughputs of the differential feedback scheme that is used in both 4G and 5G standards. In this feedback scheme, a user feeds back a 4-bit wideband channel quality indicator (CQI), which indicates the rate that the user can decode if the BS was to transmit to it over the entire system bandwidth, and a 2-bit differential CQI for each subband relative to it. Our analysis incorporates co-channel interference, different single-stream multi-antenna modes, and different schedulers, which cover a wide range of the tradeoff between the throughput and user fairness. It brings out several insights such as the increase in the throughput as the correlation between subbands increases and how differential feedback reduces the overhead significantly while only marginally reducing the throughput. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel machine learning (ML) based link-to-system (L2S) mapping technique for inter-connecting a link-level simulator (LLS) and a system-level simulator (SLS). For validating the proposed technique, we utilized 5G K-Simulator, which was developed through a collaborative research project in Republic of Korea and includes LLS, SLS, and network-level simulator (NS). We first describe a general procedure of the L2S mapping methodology for 5G new radio (NR) systems, and then, we explain the proposed ML-based exponential effective signal-to-noise ratio (SNR) mapping (EESM) method with a deep neural network (DNN) regression algorithm. We compared the proposed ML-based EESM method with the conventional L2S mapping method. Through extensive simulation results, we show that the proposed ML-based L2S mapping technique yielded better prediction accuracy in regards to block error rate (BLER) while reducing the processing time.

In contemporary orthogonal frequency division multiplexing (OFDM) systems, such as Long Term Evolution (LTE), LTE-Advanced, and WiMAX, a codeword is transmitted over a group of subcarriers. Since different subcarriers see different channel gains in frequency-selective channels, the modulation and coding scheme (MCS) of the codeword must be selected based on the vector of signal-to-noise-ratios (SNRs) of these subcarriers. Exponential effective SNR mapping (EESM) maps the vector of SNRs into an equivalent flat-fading SNR, and is widely used to simplify this problem. We develop a new analytical framework to characterize the throughput of EESM-based rate adaptation in such wideband channels in the presence of feedback delays. We derive a novel accurate approximation for the throughput as a function of feedback delay. We also propose a novel bivariate gamma distribution to model the time evolution of EESM between the times of estimation and data transmission, which facilitates the analysis. These are then generalized to a multi-cell, multi-user scenario with various frequency-domain schedulers. Unlike prior work, most of which is simulation-based, our framework encompasses both correlated and independent subcarriers and various multiple antenna diversity modes; it is accurate over a wide range of delays. [ABSTRACT FROM PUBLISHER]

In this paper, we analyze the physical layer abstraction for bit interleaved coded orthogonal frequency division multiplexing (BIC-OFDM) system from a parallel bit channel perspective. By combining the exponential effective SNR (signal-to-noise ratio) mapping (EESM) with the maximum a posteriori (MAP) algorithm, a bit LLR (log-likelihood ratio) wise EESM (BL-EESM) method is proposed. This method can abstract the link performance with high accuracy, especially for the case when channel estimation is imperfect. Afterward, the BL-EESM method is simplified by utilizing the non-linear quantization idea, which can reduce the times of exponential operation by two orders of magnitude at wide system bandwidth, yet shows little loss in accuracy. Our proposal can be applied to both system level simulations to save the time consumption and to practical terminals to facilitate the adaptive modulation and coding (AMC) procedure, bringing about throughput improvement at low hardware cost. [ABSTRACT FROM AUTHOR]

In contemporary wideband orthogonal frequency division multiplexing (OFDM) systems, such as Long Term Evolution (LTE) and WiMAX, different subcarriers over which a codeword is transmitted may experience different signal-to-noise-ratios (SNRs). Thus, adaptive modulation and coding (AMC) in these systems is driven by a vector of subcarrier SNRs experienced by the codeword, and is more involved. Exponential effective SNR mapping (EESM) simplifies the problem by mapping this vector into a single equivalent flat-fading SNR. Analysis of AMC using EESM is challenging owing to its non-linear nature and its dependence on the modulation and coding scheme. We first propose a novel statistical model for the EESM, which is based on the Beta distribution. It is motivated by the central limit approximation for random variables with a finite support. It is simpler and as accurate as the more involved ad hoc models proposed earlier. Using it, we develop novel expressions for the throughput of a point-to-point OFDM link with multi-antenna diversity that uses EESM for AMC. We then analyze a general, multi-cell OFDM deployment with co-channel interference for various frequency-domain schedulers. Extensive results based on LTE and WiMAX are presented to verify the model and analysis, and gain new insights. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a novel machine learning (ML) based link-to-system (L2S) mapping technique for inter-connecting a link-level simulator (LLS) and a system-level simulator (SLS). For validating the proposed technique, we utilized 5G K-Simulator, which was developed through a collaborative research project in Republic of Korea and includes LLS, SLS, and network-level simulator (NS). We first describe a general procedure of the L2S mapping methodology for 5G new radio (NR) systems, and then, we explain the proposed ML-based exponential effective signal-to-noise ratio (SNR) mapping (EESM) method with a deep neural network (DNN) regression algorithm. We compared the proposed ML-based EESM method with the conventional L2S mapping method. Through extensive simulation results, we show that the proposed ML-based L2S mapping technique yielded better prediction accuracy in regards to block error rate (BLER) while reducing the processing time. [ABSTRACT FROM AUTHOR]

The Probability Density Function (PDF) or Cumulative Distribution Function (CDF) of the effective Signal to Noise Ratio (SNR) is an important statistical characterization in the performance analysis of an Orthogonal Frequency Division Multiple Access (OFDMA) system using Exponential Effective SNR Mapping (EESM). However, the exact closed form of PDF is extremely difficult to obtain. A general approximation method known as Moment Matching Approximating (MMA) is used to approximate the distribution of effective SNR by a simple expression. In this paper, the approximation by Gaussian, Generalized Extreme Value (GEV) and Pearson distribution are studied. Results show that Gaussian approximation is very useful when the number of sub-carriers is sufficiently large. Both GEV and Pearson approximation are accurate enough in approximating the distribution of effective SNR in a general case. [ABSTRACT FROM AUTHOR]