A Framework for Handling Heterogeneous M2M Traffic

Sensors, actuators and devices that compose the Internet of Things (IoT) world are becoming more diverse every day in terms of capabilities and amount of generated traffic. Current Machine-to-Machine (M2 M) communication standardization efforts try to formalize the interfaces between M2 M nodes based on the perspective of exchanging uniform small data size with low sampling rate only. However, many devices will require support for more heterogeneous traffic patterns, with different network capacity. This paper introduces a communication concept for supporting gracefully a heterogeneous set of devices. This paper analyses the effect of traffic size in M2 M transactions and propose a concept to adapt gracefully to support heterogeneous traffic patterns in M2 M systems. To prove its feasibility, the concept is exemplified on top of oneM2 M architecture and implemented as part of the Fraunhofer FOKUS OpenMTC toolkit. Additionally, the concept was applied to a deployment in an E-Health pilot and practical measurements during functional evaluation are reported

Novel approach for hybrid MAC scheme for balanced energy and transmission in sensor devices

Hybrid medium access control (MAC) scheme is one of the prominent mechanisms to offer energy efficiency in wireless sensor network where the potential features for both contention-based and schedule-based approaches are mechanized. However, the review of existing hybrid MAC scheme shows many loopholes where mainly it is observed that there is too much inclusion of time-slotting or else there is an inclusion of sophisticated mechanism not meant for offering flexibility to sensor node towards extending its services for upcoming applications of it. Therefore, this manuscript introduces a novel hybrid MAC scheme which is meant for offering cost effective and simplified scheduling operation in order to balance the performance of energy efficiency along with data aggregation performance. The simulated outcome of the study shows that proposed system offers better energy consumption, better throughput, reduced memory consumption, and faster processing in contrast to existing hybrid MAC protocols

Energy efficient and delay-aware adaptive slot allocation medium access control protocol for Wireless Body Area Network

Wireless Body Area Network (WBAN) is the cheapest solution using BioMedical Sensors. They monitor different physiological vital signs of a patient. The output of vital signs does not accept collision, delay, loss, and a high energy consumption of BMSs. This paper proposes Energy Efficient and Delay-Aware Adaptive Slots Allocation Medium Access Control (EED-MAC) Protocol for WBAN. This Proposed MAC provides sufficient and dedicated channels to all types of BMSs. The patient's data are divided according to the need of a patient. Moreover, the contentions of BMSs are reduced and does not drop data by proposing a Reduced Contention Adaptive Slots Allocation CSMA/CA (RCA-CSMA/CA) scheme. The third proposed scheme is Reliability-Aware Channel Allocation (RAC), which allocates channels for emergency-based BMSs using alert signals without contention. The simulation of the proposed MAC and other schemes achieve significant improvements against the state-of-the-art MAC protocols

An efficient MAC protocol with adaptive energy harvesting for machine-to-machine networks

In a machine-to-machine network, the throughput performance plays a very important role. Recently, an attractive energy harvesting technology has shown great potential to the improvement of the network throughput, as it can provide consistent energy for wireless devices to transmit data. Motivated by that, an efficient energy harvesting-based medium access control (MAC) protocol is designed in this paper. In this protocol, different devices first harvest energy adaptively and then contend the transmission opportunities with energy level related priorities. Then, a new model is proposed to obtain the optimal throughput of the network, together with the corresponding hybrid differential evolution algorithm, where the involved variables are energy-harvesting time, contending time, and contending probability. Analytical and simulation results show that the network based on the proposed MAC protocol has greater throughput than that of the traditional methods. In addition, as expected, our scheme has less transmission delay, further enhancing its superiority

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