An Efficient Spectral Leakage Filtering for IEEE 802.11af in TV White Space

Orthogonal frequency division multiplexing (OFDM) has been widely adopted for modern wireless standards and become a key enabling technology for cognitive radios. However, one of its main drawbacks is significant spectral leakage due to the accumulation of multiple sinc-shaped subcarriers. In this paper, we present a novel pulse shaping scheme for efficient spectral leakage suppression in OFDM based physical layer of IEEE 802.11af standard. With conventional pulse shaping filters such as a raised-cosine filter, vestigial symmetry can be used to reduce spectral leakage very effectively. However, these pulse shaping filters require long guard interval, i.e., cyclic prefix in an OFDM system, to avoid inter-symbol interference (ISI), resulting in a loss of spectral efficiency. The proposed pulse shaping method based on asymmetric pulse shaping achieves better spectral leakage suppression and decreases ISI caused by filtering as compared to conventional pulse shaping filters

딥러닝과 최적화를 활용한 무선 통신 시스템 설계

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2021.8. 캄포싸이.최근 5G 시스템의 등장으로 고신뢰 저지연 통신(ultra reliable low-latency communications, URLLC)과 대규모 사물 통신(massive machine-type communications, mMTC)이 주목을 받고 있다. 의료 서비스, 커넥티드 카, 로봇 공학, 제조업, 자유 시점 비디오 등 다양한 서비스들이 저지연 통신에서 예상되고, 이들은 1 ms 정도의 극도로 낮은 지연 시간을 요구한다. 한편, 대규모 사물 통신은 기지국에서 많은 기기(예를 들어 센서, 로봇, 자동차, 기계)의 방대한 연결성에 관한 것이다. 기존 통신 시스템(예를 들어 Long-Term Evolution (LTE))은 저지연 통신과 대규모 사물 통신의 요구 사항을 만족하기 어렵기에 이 통신 환경에 적합한 새로운 기술이 필요하다. 본 학위논문에서는 대규모 사물 통신과 저지연 통신을 위한 세 가지 기술을 제안한다. 논문의 첫 부분에서는 많은 기기가 비직교 확산 시퀀스를 사용해 기지국에 접속하는 대규모 사물 통신을 지원하는 딥러닝 기반의 확산 시퀀스 설계 및 활성 사용자 검출(active user detection, AUD) 방법을 제안한다. 검출 오류를 최소화하는 전체 통신 시스템을 설계하기 위해, 종단 간 심층 신경 네트워크(deep neural network, DNN)를 활용한다. 이 신경 네트워크에서 확산 네트워크는 송신기를 모델링하고 검출 네트워크는 활성 기기를 추정한다. 검출 오류를 손실 함수로 사용함으로써, 확산 시퀀스를 포함한 네트워크 변수들은 검출 오류를 최소화하도록 학습된다. 시뮬레이션 결과에서는 제안한 방법으로 얻어진 확산 시퀀스가 압축센싱 기반의 검출 기법과 제안한 검출 기법 모두에서 기존의 시퀀스보다 더 좋은 검출 성능을 달성하는 것을 보여준다. 논문의 두 번째 부분에서는 직교 주파수 분할 다중 방식(orthogonal frequency division multiplexing, OFDM) 시스템에서 프리코딩된 채널의 RMS (root mean square) 지연 확산을 줄이는 프리코딩 기법을 제안한다. OFDM 시스템에서 오버헤드를 증가시키지 않으면서 지연을 줄이기 위해서는 채널의 지연 확산과 그로 인한 CP (cyclic prefix)의 길이를 줄이는 것이 무엇보다 중요하다. 제안하는 기법에서는 RMS 지연 확산의 상한을 목적 함수로 하고 각 부반송파의 신호 대 잡음비를 제약조건으로 하는 최적화 문제를 설정한다. 최적화된 프리코딩을 찾을 수 있도록 원래 문제를 볼록 문제로 변환하기 위해 SDR (semi-definite relaxation) 기법을 사용한다. 시뮬레이션 결과에서는 제안한 프리코딩 설계가 특히 기지국에서 안테나의 수가 많을 때 RMS 지연 확산을 크게 줄이는 것을 보여준다. 논문의 마지막 부분에서는 저지연 OFDM 시스템에서 전송률 최대화를 위한 선형 프리코딩 설계를 다룬다. 저지연 통신에서 짧아지는 심볼 주기로 인한 CP의 오버헤드를 완화하기 위해 5G 무선 시스템은 짧은 CP를 사용할 필요가 있다. 채널의 지연 확산은 CP 길이보다 짧아야 하므로 먼저 실질적인 RMS 지연 확산과 달성 가능한 전송률을 제로 포싱 조건을 사용하여 유도한다. 다음으로 지연 확산 제약조건을 만족하는 전송률 최적화 문제를 사용자마다 정립하고 SDR 기법으로 해결 가능한 볼록 문제로 변환한다. 모든 사용자에 대해 최적화 문제를 푸는 것으로 전체 프리코딩 행렬을 얻는다. 시뮬레이션 결과에서는 제안한 기법이 작은 RMS 지연 확산과 함께 기존의 전송률 최적화보다 월등한 성능을 달성하는 것을 보여준다.With the advent of 5G wireless systems, ultra reliable low-latency communications (URLLC) and massive machine-type communications (mMTC) have recently attracted growing attention. Applications in health care, connected cars, robotics, manufacturing, and free-viewpoint video are expected in low-latency communications, and they demand extremely short round-trip latency levels as low as 1 ms. On the other hand, mMTC mainly concerns the massive connectivity of a large number of devices (e.g. sensors, robots, vehicles, and machines) to the base station (BS). Since conventional communications systems (e.g. Long-Term Evolution (LTE)) are difficult to meet the requirements of low-latency communications or mMTC, novel techniques suitable for these communications environments are required. This dissertation proposes three techniques for mMTC or low-latency communications. In the first part of the dissertation, we propose a deep learning-based spreading sequence design and active user detection (AUD) to support mMTC where a large number of devices access the base station using non-orthogonal spreading sequences. To design the whole communications system minimizing AUD error, we employ an end-to-end deep neural network (DNN) where the spreading network models the transmitter side and the AUD network estimates active devices. By using the AUD error as a loss function, network parameters including the spreading sequences are learned to minimize the AUD error. Numerical results reveal that the spreading sequences obtained from the proposed approach achieve higher AUD performance than the conventional spreading sequences in the compressive sensing-based AUD schemes, as well as in the proposed AUD scheme. In the second part of the dissertation, a precoding scheme to reduce the root mean square (RMS) delay spread of precoded channels in a orthogonal frequency division multiplexing (OFDM) system is proposed. In order to reduce latency in OFDM systems while not increasing the overhead, it is of primary importance to reduce the effective delay spread of the channel and thus the length of the cyclic prefix (CP). We formulate an optimization problem with an upper bound of the RMS delay spread as the objective function and a signal-to-noise ratio for each subcarrier as constraints. Semi-definite relaxation (SDR) technique is used to convert the problem into a convex problem so as to find the optimal precoding vector. Numerical results confirm that the proposed precoding design provides a significant reduction in the RMS delay spread, especially when there are a large number of antennas at the base station. In the last part of the dissertation, we addresses linear precoding design for sum rate maximization in low-latency OFDM systems. In order to mitigate the overhead of CP originating from shortened symbol duration for low-latency communications, 5G wireless systems need to adopt short CP lengths. As channel delay spread must be less than the CP length, we first derive the effective RMS delay spread and the achievable rate using the zero-forcing assumption. We construct a sum rate optimization problem for each user subject to delay spread constraints and then convert the problem into a solvable convex problem along with a SDR technique. The precoding matrix is finally obtained by solving optimization problems for all users. Numerical results reveal that the proposed scheme attains superior performance to the conventional sum rate optimization, as well as small RMS delay spread.1 INTRODUCTION 1 1.1 Deep Learning-based Spreading Sequence Design and Active User Detection for Massive Machine-Type Communications 2 1.2 Precoding Design for Cyclic Prefix Overhead Reduction in a MISO-OFDM System 5 1.3 Sum Rate Maximization with Shortened Cyclic Prefix in a MIMO-OFDM System 6 2 DEEP LEARNING-BASED SPREADING SEQUENCE DESIGN AND ACTIVE USER DETECTION FOR MASSIVE MACHINE-TYPE COMMUNICATIONS 8 2.1 System Model 8 2.2 DNN-based Spreading Sequence Design and Active User Detection 10 2.2.1 SN Architecture 13 2.2.2 AUDN Architecture 15 2.2.3 Operation 17 2.3 Numerical Results 18 2.3.1 Simulation Setup 18 2.3.2 Homogeneous Activities 19 2.3.3 Heterogeneous Activities 21 3 PRECODING DESIGN FOR CYCLIC PREFIX OVERHEAD REDUCTION IN A MISO-OFDM SYSTEM 26 3.1 System Model 26 3.2 Precoding Design 27 3.2.1 Effective RMS Delay Spread and SNR 28 3.2.2 Precoding Optimization 29 3.3 Numerical Results 31 4 SUM RATE MAXIMIZATION WITH SHORTENED CYCLIC PREFIX IN A MIMO-OFDM SYSTEM 37 4.1 System Model 37 4.2 Preliminaries for Precoding Design 38 4.2.1 Zero-Forcing Conditions 38 4.2.2 Effective RMS Delay Spread 40 4.2.3 Achievable Rate 41 4.3 Precoding Optimization 42 4.4 Numerical Results 43 5 CONCLUSION 53 5.1 Deep Learning-based Spreading Sequence Design and Active User Detection for Massive Machine-Type Communications 53 5.2 Precoding Design for Cyclic Prefix Overhead Reduction in a MISO-OFDM System 54 5.3 Sum Rate Maximization with Shortened Cyclic Prefix in a MIMO-OFDM System 54 Abstract (In Korean) 60 Acknowledgments 62박

A Comparison of CP-OFDM, PCC-OFDM and UFMC for 5G Uplink Communications

Polynomial-cancellation-coded orthogonal frequency division multiplexing (PCC-OFDM) is a form of OFDM that has waveforms which are very well localized in both the time and frequency domains and so it is ideally suited for use in the 5G network. This paper analyzes the performance of PCC-OFDM in the uplink of a multiuser system using orthogonal frequency division multiple access (OFDMA) and compares it with conventional cyclic prefix OFDM (CP-OFDM), and universal filtered multicarrier (UFMC). PCC-OFDM is shown to be much less sensitive than either CP-OFDM or UFMC to time and frequency offsets. For a given constellation size, PCC-OFDM in additive white Gaussian noise (AWGN) requires 3dB lower signal-to-noise ratio (SNR) for a given bit-error-rate, and the SNR advantage of PCC-OFDM increases rapidly when there are timing and/or frequency offsets. For PCC-OFDM no frequency guard band is required between different OFDMA users. PCC-OFDM is completely compatible with CP-OFDM and adds negligible complexity and latency, as it uses a simple mapping of data onto pairs of subcarriers at the transmitter, and a simple weighting-and-adding of pairs of subcarriers at the receiver. The weighting and adding step, which has been omitted in some of the literature, is shown to contribute substantially to the SNR advantage of PCC-OFDM. A disadvantage of PCC-OFDM (without overlapping) is the potential reduction in spectral efficiency because subcarriers are modulated in pairs, but this reduction is more than regained because no guard band or cyclic prefix is required and because, for a given channel, larger constellations can be used

Investigations on Filtered OFDM with Selective Mapping Method and Partial Transmit Sequence Technique for Future Generation Mobile Communication Systems

Future generation mobile communication system requires asynchronous transmission of data, reduced out-of-band power emission, low peak-to-average power ratio, low latency, high data transmission rate, better spectrum, energy, and power efficiency, etc. Investigations on suitable waveform candidates for future-generation mobile communication have been reported in this paper. Filtered Orthogonal Frequency Division Multiplexing (F- OFDM), F- OFDM with Selective Mapping Method (SLM), and F- OFDM with Partial Transmit Sequence (PTS) technique, have been investigated. Its performances have been evaluated in terms of peak-to-average power ratio (PAPR), bit error rate (BER), and out-of-band power emissions. F–OFDM is a suitable candidate for future-generation mobile communication systems that can be used with single-rate or multirate filters. It can also be used in combination with other PAPR reduction techniques. F-OFDM with PTS technique requires a smaller number of IFFT operations than F-OFDM with SLM. The result obtained from my present investigations reveals that F-OFDM with the PTS technique has 4.3 dB less PAPR than that of OFDM at the cost of marginal increase in the BER value