대규모 사물 통신을 위한 압축센싱 기반 다중 사용자 검출

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 전기·컴퓨터공학부, 2019. 2. 이광복.Massive machine-type communication (mMTC) is a newly introduced service category in 5G wireless communication systems to support a variety of Internet-of-Things (IoT) applications. In the mMTC network, a large portion of devices is inactive and hence does not transmit data. Thus, the transmit vector consisting of data symbols of both active and inactive devices can be readily modeled as a sparse vector. In recovering sparsely represented multi-user vectors, compressed sensing based multi-user detection (CS-MUD) can be used. CS-MUD is a feasible solution to the grant-free uplink non-orthogonal multiple access (NOMA) environments. In this dissertation, two novel techniques regarding CS-MUD for mMTC networks are proposed. In the first part of the dissertation, the sparsity-aware ordered successive interference cancellation (SA-OSIC) technique is proposed. In CS-MUD, multi-user vectors are detected based on a sparsity-aware maximum a posteriori probability (S-MAP) criterion. To reduce the computational complexity of S-MAP detection, sparsity-aware successive interference cancellation (SA-SIC) can be used. SA-SIC is a simple low-complexity scheme that recovers transmit symbols in a sequential manner. However, SA-SIC does not perform well without proper layer sorting due to error propagation. When multi-user vectors are sparse and each device is active with a distinct probability, the detection order determined solely by channel gains might not be optimal. In this dissertation, to reduce the error propagation and enhance the performance of SA-SIC, an activity-aware sorted QR decomposition (A-SQRD) algorithm that finds the optimal detection order is proposed. The proposed technique finds the optimal detection order based on the activity probabilities and channel gains of machine-type devices. Numerical results verify that the proposed technique greatly improves the performance of SA-SIC. In the second part of the dissertation, the expectation propagation based joint AUD and CE (EP-AUD/CE) technique is proposed. In several studies regarding CS-MUD, the uplink channel state information (CSI) from the MTD to the BS is assumed to be perfectly known to the BS. In practice, however, the uplink CSI from the devices to the BS should be estimated before data detection. To address this issue, various joint active user detection (AUD) and channel estimation (CE) schemes have been proposed. Since only a few devices are active at one time, an element-wise (i.e., Hadamard) product of the binary activity pattern and the channel vector is also a sparse vector and thus compressed sensing (CS)-based technique is a good fit for the problem at hand. One potential shortcoming in these studies is that a prior distribution of the sparse vector is not exploited. In fact, these studies are based on the non-Bayesian greedy algorithms such as the orthogonal matching pursuit (OMP) and approximate message passing (AMP) algorithms, which do not require a prior distribution of the sparse vector. In essence, these algorithms find out non-zero values based on the instantaneous correlation between the sensing matrix and the observation vector so that they might not be effective in the situation where the prior distribution is available. In this case, clearly, by exploiting the statistical distribution of the sparse vector, the performance of AUD and CE can be improved substantially. The proposed technique finds the best approximation of the posterior distribution of the sparse channel vector based on the expectation propagation (EP) algorithm. Using the approximate distribution, AUD and CE are jointly performed. Numerical simulations show that the proposed technique substantially enhances AUD and CE performances over competing algorithms.대규모 사물 통신(massive machine-type communications, mMTC)은 다양한 사물 인터넷(internet of things, IoT) 서비스를 지원하기 위해 차세대 무선 통신 표준에 새로 도입된 서비스 범주이다. 대규모 사물 통신 환경에서는 많은 수의 사물 기기(machine-type device, MTD)가 대부분의 타임 슬롯(time slot)에서 비활성 상태이며 데이터를 전송하지 않는다. 따라서, 활성 및 비활성 기기 모두의 데이터 심볼로 구성된 전송 벡터는 희소(sparse) 벡터로 표현될 수 있다. 희소 벡터로 표현된 다중 사용자 벡터를 복원하기 위해, 압축 센싱 기반 다중 사용자 검출(CS-MUD)이 사용될 수 있다. CS-MUD는 스케줄링(scheduling) 절차가 없는 상향링크(uplink) 비직교 다중 접속(non-orthogonal multiple access, NOMA)을 위한 핵심 기술 중 하나이다. 본 학위 논문에서는 대규모 사물 통신을 위한 새로운 CS-MUD 기술들을 제안한다. 논문의 첫 번째 부분에서는, 희소성을 고려한 정렬 순차적 간섭 제거(sparsity-aware ordered successive interference cancellation, SA-OSIC) 기술을 제안한다. CS-MUD에서 다중 사용자 벡터는 희소성을 고려한 최대 사후 확률(sparsity-aware maximum a posteriori probability, S-MAP) 기준에 따라 검출된다. S-MAP 검출의 계산 복잡성을 줄이기 위해 희소성을 고려한 순차적 간섭 제거(sparsity-aware successive interference cancellation, SA-SIC)를 사용할 수 있다. 희소 데이터 벡터 검출의 계산 복잡성을 줄이기 위해 사용되는 희소성 고려 연속 간섭 제거 기술은 오류 전파(error propagation)로 인해 적절한 사용자 정렬 없이는 성능이 좋지 않다. 다중 사용자 벡터가 희소 벡터이고 각 기기가 다른 확률로 활성일 경우 채널 이득(channel gain)에 의해서만 결정된 사용자 검출 순서는 최적이 아닐 수 있다. 본 논문에서는, 오류 전파를 줄이고 희소성 고려 연속 간섭 제거의 성능을 향상하기 위해 각 사물 기기의 활성 확률(activity probability)과 채널 이득을 기반으로 최적의 검출 순서를 찾는 새로운 알고리즘을 제안한다. 시뮬레이션을 통해 제안한 검출 순서 정렬 기술은 데이터 검출의 성능을 크게 향상함을 검증하였다. 논문의 두 번째 부분에서는, 기댓값 전파 기반 활성 사용자 검출 및 채널 추정(expectation propagation based active user detection channel estimation, EP-AUD/CE) 기술을 제안한다. CS-MUD에 관한 몇몇 연구에서, 각 사물 기기로부터 기지국(base station, BS)으로의 상향링크 채널 상태 정보(channel state information, CSI)는 기지국에 완전히 알려져 있다고 가정된다. 그러나 실제로는, 데이터 검출 전에 각 기기로부터 기지국으로의 상향링크 채널 상태 정보를 추정해야 한다. 이 문제를 해결하기 위해 다양한 활성 사용자 검출(active user detection, AUD) 및 채널 추정(channel estimation, CE) 기술이 제안되었다. 대규모 사물 통신에서는 하나의 타임 슬롯에 적은 수의 장치만 활성화되기 때문에 이진(binary)값으로 이루어진 활성 여부 벡터와 채널 벡터의 곱은 희소 벡터가 되어 압축센싱 알고리즘으로 복원이 가능하다. 하지만, 이러한 연구들의 단점 중 하나는 희소 벡터의 사전 분포(prior distritubion)가 활용되지 않는다는 것이다. 희소 벡터의 통계적 사전 분포를 이용하면 활성 사용자 검출 및 채널 추정의 성능을 크게 향상할 수 있다. 본 학위 논문에서는, 기댓값 전파(expectation propagation, EP) 알고리즘을 이용해 희소 채널 벡터의 사후 분포(posterior distribution)의 근사 분포를 찾고, 해당 근사 분포를 이용하여 활성 사용자 검출과 채널 추정을 동시에 수행하는 기술을 제안한다. 시뮬레이션을 통해 제안한 사용자 검출 및 채널 추정 기술은 활성 사용자 검출 및 채널 추정의 성능을 상당히 향상함을 검증하였다.1 Introduction . . . . . 1 1.1 Sparsity-Aware Ordered Successive Interference Cancellation . . . . . 3 1.2 Expectation Propagation-based Joint Active User Detection and Channel Estimation . . . . . 4 2 Sparsity-Aware Ordered Successive Interference Cancellation . . . . . 7 2.1 System model . . . . . 7 2.2 Sparsity-Aware Successive Interference Cancellation (SA-SIC) . . . . . 9 2.2.1 Derivation of S-MAP Detection . . . . . 9 2.2.2 Sparsity-Aware SIC (SA-SIC) Detection . . . . . 10 2.3 Proposed Activity-Aware Sorted-QRD (A-SQRD) Algorithm . . . . . 11 2.4 Complexity Analysis . . . . . 15 2.5 Numerical Results . . . . . 15 2.5.1 Simulation Setup . . . . . 16 2.5.2 Simulation Results . . . . . 20 3 Expectation Propagation-based Joint Active User Detection and Channel Estimation . . . . . 21 3.1 System model . . . . . 21 3.2 Joint Active User Detection and Channel Estimation . . . . . 23 3.3 EP-Based Active User Detection and Channel Estimation . . . . . 26 3.3.1 A Brief Review of Expectation Propagation . . . . . 29 3.3.2 Form of the Approximation . . . . . 30 3.3.3 Iterative EP Update Rules . . . . . 31 3.3.4 Active User Detection and Channel Estimation . . . . . 36 3.3.5 Data Detection . . . . . 37 3.3.6 Comments on Complexity . . . . . 38 3.4 Simulation Results and Discussions . . . . . 39 3.4.1 Simulation Setup . . . . . 39 3.4.2 Simulation Results . . . . . 52 4 Conclusion . . . . . 54 Abstract (In Korean) . . . . . 60Docto

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