Grant-Free Massive MTC-Enabled Massive MIMO: A Compressive Sensing Approach

A key challenge of massive MTC (mMTC), is the joint detection of device activity and decoding of data. The sparse characteristics of mMTC makes compressed sensing (CS) approaches a promising solution to the device detection problem. However, utilizing CS-based approaches for device detection along with channel estimation, and using the acquired estimates for coherent data transmission is suboptimal, especially when the goal is to convey only a few bits of data. First, we focus on the coherent transmission and demonstrate that it is possible to obtain more accurate channel state information by combining conventional estimators with CS-based techniques. Moreover, we illustrate that even simple power control techniques can enhance the device detection performance in mMTC setups. Second, we devise a new non-coherent transmission scheme for mMTC and specifically for grant-free random access. We design an algorithm that jointly detects device activity along with embedded information bits. The approach leverages elements from the approximate message passing (AMP) algorithm, and exploits the structured sparsity introduced by the non-coherent transmission scheme. Our analysis reveals that the proposed approach has superior performance compared to application of the original AMP approach.Comment: Submitted to IEEE Transactions on Communication

Sparse Signal Processing Concepts for Efficient 5G System Design

As it becomes increasingly apparent that 4G will not be able to meet the emerging demands of future mobile communication systems, the question what could make up a 5G system, what are the crucial challenges and what are the key drivers is part of intensive, ongoing discussions. Partly due to the advent of compressive sensing, methods that can optimally exploit sparsity in signals have received tremendous attention in recent years. In this paper we will describe a variety of scenarios in which signal sparsity arises naturally in 5G wireless systems. Signal sparsity and the associated rich collection of tools and algorithms will thus be a viable source for innovation in 5G wireless system design. We will discribe applications of this sparse signal processing paradigm in MIMO random access, cloud radio access networks, compressive channel-source network coding, and embedded security. We will also emphasize important open problem that may arise in 5G system design, for which sparsity will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces

Signal Processing for Compressed Sensing Multiuser Detection

The era of human based communication was longly believed to be the main driver for the development of communication systems. Already nowadays we observe that other types of communication impact the discussions of how future communication system will look like. One emerging technology in this direction is machine to machine (M2M) communication. M2M addresses the communication between autonomous entities without human interaction in mind. A very challenging aspect is the fact that M2M strongly differ from what communication system were designed for. Compared to human based communication, M2M is often characterized by small and sporadic uplink transmissions with limited data-rate constraints. While current communication systems can cope with several 100 transmissions, M2M envisions a massive number of devices that simultaneously communicate to a central base-station. Therefore, future communication systems need to be equipped with novel technologies facilitating the aggregation of massive M2M. The key design challenge lies in the efficient design of medium access technologies that allows for efficient communication with small data packets. Further, novel physical layer aspects have to be considered in order to reliable detect the massive uplink communication. Within this thesis physical layer concepts are introduced for a novel medium access technology tailored to the demands of sporadic M2M. This concept combines advances from the field of sporadic signal processing and communications. The main idea is to exploit the sporadic structure of the M2M traffic to design physical layer algorithms utilizing this side information. This concept considers that the base-station has to jointly detect the activity and the data of the M2M nodes. The whole framework of joint activity and data detection in sporadic M2M is known as Compressed Sensing Multiuser Detection (CS-MUD). This thesis introduces new physical layer concepts for CS-MUD. One important aspect is the question of how the activity detection impacts the data detection. It is shown that activity errors have a fundamentally different impact on the underlying communication system than data errors have. To address this impact, this thesis introduces new algorithms that aim at controlling or even avoiding the activity errors in a system. It is shown that a separate activity and data detection is a possible approach to control activity errors in M2M. This becomes possible by considering the activity detection task in a Bayesian framework based on soft activity information. This concept allows maintaining a constant and predictable activity error rate in a system. Beyond separate activity and data detection, the joint activity and data detection problem is addressed. Here a novel detector based on message passing is introduced. The main driver for this concept is the extrinsic information exchange between different entities being part of a graphical representation of the whole estimation problem. It can be shown that this detector is superior to state-of-the-art concepts for CS-MUD. Besides analyzing the concepts introduced simulatively, this thesis also shows an implementation of CS-MUD on a hardware demonstrator platform using the algorithms developed within this thesis. This implementation validates that the advantages of CS-MUD via over-the-air transmissions and measurements under practical constraints

Reliable recovery of hierarchically sparse signals for Gaussian and Kronecker product measurements

We propose and analyze a solution to the problem of recovering a block sparse signal with sparse blocks from linear measurements. Such problems naturally emerge inter alia in the context of mobile communication, in order to meet the scalability and low complexity requirements of massive antenna systems and massive machine-type communication. We introduce a new variant of the Hard Thresholding Pursuit (HTP) algorithm referred to as HiHTP. We provide both a proof of convergence and a recovery guarantee for noisy Gaussian measurements that exhibit an improved asymptotic scaling in terms of the sampling complexity in comparison with the usual HTP algorithm. Furthermore, hierarchically sparse signals and Kronecker product structured measurements naturally arise together in a variety of applications. We establish the efficient reconstruction of hierarchically sparse signals from Kronecker product measurements using the HiHTP algorithm. Additionally, we provide analytical results that connect our recovery conditions to generalized coherence measures. Again, our recovery results exhibit substantial improvement in the asymptotic sampling complexity scaling over the standard setting. Finally, we validate in numerical experiments that for hierarchically sparse signals, HiHTP performs significantly better compared to HTP.Comment: 11+4 pages, 5 figures. V3: Incomplete funding information corrected and minor typos corrected. V4: Change of title and additional author Axel Flinth. Included new results on Kronecker product measurements and relations of HiRIP to hierarchical coherence measures. Improved presentation of general hierarchically sparse signals and correction of minor typo

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