On Improving Throughput of Multichannel ALOHA using Preamble-based Exploration

Machine-type communication (MTC) has been extensively studied to provide connectivity for devices and sensors in the Internet-of-Thing (IoT). Thanks to the sparse activity, random access, e.g., ALOHA, is employed for MTC to lower signaling overhead. In this paper, we propose to adopt exploration for multichannel ALOHA by transmitting preambles before transmitting data packets in MTC, and show that the maximum throughput can be improved by a factor of 2 - exp(-1) = 1.632, In the proposed approach, a base station (BS) needs to send the feedback information to active users to inform the numbers of transmitted preambles in multiple channels, which can be reliably estimated as in compressive random access. A steady-state analysis is also performed with fast retrial, which shows that the probability of packet collision becomes lower and, as a result, the delay outage probability is greatly reduced for a lightly loaded system. Simulation results also confirm the results from analysis.Comment: 10 pages, 7 figures, to appear in the Journal of Communications and Networks. arXiv admin note: substantial text overlap with arXiv:2001.1111

Channel estimation techniques for filter bank multicarrier based transceivers for next generation of wireless networks

A dissertation submitted to Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in fulfillment of the requirements for the degree of Master of Science in Engineering (Electrical and Information Engineering), August 2017The fourth generation (4G) of wireless communication system is designed based on the principles of cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) where the cyclic prefix (CP) is used to combat inter-symbol interference (ISI) and inter-carrier interference (ICI) in order to achieve higher data rates in comparison to the previous generations of wireless networks. Various filter bank multicarrier systems have been considered as potential waveforms for the fast emerging next generation (xG) of wireless networks (especially the fifth generation (5G) networks). Some examples of the considered waveforms are orthogonal frequency division multiplexing with offset quadrature amplitude modulation based filter bank, universal filtered multicarrier (UFMC), bi-orthogonal frequency division multiplexing (BFDM) and generalized frequency division multiplexing (GFDM). In perfect reconstruction (PR) or near perfect reconstruction (NPR) filter bank designs, these aforementioned FBMC waveforms adopt the use of well-designed prototype filters (which are used for designing the synthesis and analysis filter banks) so as to either replace or minimize the CP usage of the 4G networks in order to provide higher spectral efficiencies for the overall increment in data rates. The accurate designing of the FIR low-pass prototype filter in NPR filter banks results in minimal signal distortions thus, making the analysis filter bank a time-reversed version of the corresponding synthesis filter bank. However, in non-perfect reconstruction (Non-PR) the analysis filter bank is not directly a time-reversed version of the corresponding synthesis filter bank as the prototype filter impulse response for this system is formulated (in this dissertation) by the introduction of randomly generated errors. Hence, aliasing and amplitude distortions are more prominent for Non-PR. Channel estimation (CE) is used to predict the behaviour of the frequency selective channel and is usually adopted to ensure excellent reconstruction of the transmitted symbols. These techniques can be broadly classified as pilot based, semi-blind and blind channel estimation schemes. In this dissertation, two linear pilot based CE techniques namely the least square (LS) and linear minimum mean square error (LMMSE), and three adaptive channel estimation schemes namely least mean square (LMS), normalized least mean square (NLMS) and recursive least square (RLS) are presented, analyzed and documented. These are implemented while exploiting the near orthogonality properties of offset quadrature amplitude modulation (OQAM) to mitigate the effects of interference for two filter bank waveforms (i.e. OFDM/OQAM and GFDM/OQAM) for the next generation of wireless networks assuming conditions of both NPR and Non-PR in slow and fast frequency selective Rayleigh fading channel. Results obtained from the computer simulations carried out showed that the channel estimation schemes performed better in an NPR filter bank system as compared with Non-PR filter banks. The low performance of Non-PR system is due to the amplitude distortion and aliasing introduced from the random errors generated in the system that is used to design its prototype filters. It can be concluded that RLS, NLMS, LMS, LMMSE and LS channel estimation schemes offered the best normalized mean square error (NMSE) and bit error rate (BER) performances (in decreasing order) for both waveforms assuming both NPR and Non-PR filter banks. Keywords: Channel estimation, Filter bank, OFDM/OQAM, GFDM/OQAM, NPR, Non-PR, 5G, Frequency selective channel.CK201

CELLULAR-ENABLED MACHINE TYPE COMMUNICATIONS: RECENT TECHNOLOGIES AND COGNITIVE RADIO APPROACHES

The scarcity of bandwidth has always been the main obstacle for providing reliable high data-rate wireless links, which are in great demand to accommodate nowadays and immediate future wireless applications. In addition, recent reports have showed inefficient usage and under-utilization of the available bandwidth. Cognitive radio (CR) has recently emerged as a promising solution to enhance the spectrum utilization, where it offers the ability for unlicensed users to access the licensed spectrum opportunistically. By allowing opportunistic spectrum access which is the main concept for the interweave network model, the overall spectrum utilization can be improved. This requires cognitive radio networks (CRNs) to consider the spectrum sensing and monitoring as an essential enabling process for the interweave network model. Machine-to-machine (M2M) communication, which is the basic enabler for the Internet-of-Things (IoT), has emerged to be a key element in future networks. Machines are expected to communicate with each other exchanging information and data without human intervention. The ultimate objective of M2M communications is to construct comprehensive connections among all machines distributed over an extensive coverage area. Due to the radical change in the number of users, the network has to carefully utilize the available resources in order to maintain reasonable quality-of-service (QoS). Generally, one of the most important resources in wireless communications is the frequency spectrum. To utilize the frequency spectrum in IoT environment, it can be argued that cognitive radio concept is a possible solution from the cost and performance perspectives. Thus, supporting numerous number of machines is possible by employing dual-mode base stations which can apply cognitive radio concept in addition to the legacy licensed frequency assignment. In this thesis, a detailed review of the state of the art related to the application of spectrum sensing in CR communications is considered. We present the latest advances related to the implementation of the legacy spectrum sensing approaches. We also address the implementation challenges for cognitive radios in the direction of spectrum sensing and monitoring. We propose a novel algorithm to solve the reduced throughput issue due to the scheduled spectrum sensing and monitoring. Further, two new architectures are considered to significantly reduce the power consumption required by the CR to enable wideband sensing. Both systems rely on the 1-bit quantization at the receiver side. The system performance is analytically investigated and simulated. Also, complexity and power consumption are investigated and studied. Furthermore, we address the challenges that are expected from the next generation M2M network as an integral part of the future IoT. This mainly includes the design of low-power low-cost machine with reduced bandwidth. The trade-off between cost, feasibility, and performance are also discussed. Because of the relaxation of the frequency and spatial diversities, in addition, to enabling the extended coverage mode, initial synchronization and cell search have new challenges for cellular-enabled M2M systems. We study conventional solutions with their pros and cons including timing acquisition, cell detection, and frequency offset estimation algorithms. We provide a technique to enhance the performance in the presence of the harsh detection environment for LTE-based machines. Furthermore, we present a frequency tracking algorithm for cellular M2M systems that utilizes the new repetitive feature of the broadcast channel symbols in next generation Long Term Evolution (LTE) systems. In the direction of narrowband IoT support, we propose a cell search and initial synchronization algorithm that utilizes the new set of narrowband synchronization signals. The proposed algorithms have been simulated at very low signal to noise ratios and in different fading environments

Channel estimation with TCH codes for machine-type communications

TCH codes possess several properties that allow us to use them efficiently in various applications. One of these applications is channel estimation and, in this dissertation, it is studied the performance of TCH codes to estimate the channel in an Orthogonal Frequency Division Multiplexing system, regarding Machine-Type Communications. Bit error rate performance results were obtained by executing simulations that allowed the evaluation of the impact of using two different pilot techniques, such as data multiplexed and implicit pilots, different pilot power levels and different modulations, QPSK and 64-QAM. Pilots based on TCH codes are also compared with other conventional pilots. Results show that TCH codes have a very positive and reliable performance. Joint timing synchronization and channel estimation is also performed using different sparse based approaches, such as Orthogonal Matching Pursuit, L1- regularized and Iterative Reweighted L1. TCH codes are compared against different sequence types, namely Zadoff-Chu sequences and pseudorandom codewords, and variations in the pilot size, the channel length and the observation window size are executed in order to understand their effects. Results ultimately illustrate that TCH codes can be effectively used in joint channel estimation and synchronization, managing to withstand worst simulation conditions better than its counterparts. It is also proven that compressed sensing can successfully be utilized in joint synchronization and channel estimation, an area where its use has not been very explored.Os códigos TCH possuem várias propriedades que nos permitem usá-los eficientemente em diversas aplicações. Uma delas é a estimação de canal e nesta dissertação é estudado o desempenho dos códigos TCH em estimação de canal num sistema OFDM, tendo em conta as comunicações Machine-Type. Resultados que ilustram a taxa de erro de bit foram obtidos através de simulações que permitem avaliar o impacto de usar diferentes técnicas de pilotos, nomeadamente multiplexados e implícitos, diferentes valores de potência para os pilotos e diferentes modulações, QPSK e 64-QAM. Também é feita a comparação entre os pilotos TCH e pilotos convencionais. Os resultados mostram que os pilotos TCH tem um desempenho muito positivo e confiável, dentro dos parâmetros testados. Também é efetuado o estudo de sincronização e estimação de canal conjunta usando métodos esparsos como o OMP, o L1-regularized e o Iterative Reweighted L1. Os códigos TCH são comparados com outros tipos de sequências, tais como as sequências Zadoff-Chu e os códigos pseudo-aleatórios. São consideradas variações no tamanho dos pilotos, no comprimento do canal e no tamanho da janela de observação para perceber quais são os seus efeitos no desempenho. Os resultados demonstram que os códigos TCH podem ser utilizados com sucesso em estimação de canal e sincronização conjunta e conseguem aguentar condições adversas de simulação melhor que os outros pilotos utilizados. Também é provado que compressed sensing pode ser utilizado com sucesso em sincronização e estimação conjunta, que é uma área onde o seu uso ainda não foi explorado aprofundadamente

Channel estimation techniques for next generation mobile communication systems

Mención Internacional en el título de doctorWe are witnessing a revolution in wireless technology, where the society is demanding new services, such as smart cities, autonomous vehicles, augmented reality, etc. These challenging services not only are demanding an enormous increase of data rates in the range of 1000 times higher, but also they are real-time applications with an important delay constraint. Furthermore, an unprecedented number of different machine-type devices will be also connected to the network, known as Internet of Things (IoT), where they will be transmitting real-time measurements from different sensors. In this context, the Third Generation Partnership Project (3GPP) has already developed the new Fifth Generation (5G) of mobile communication systems, which should be capable of satisfying all the requirements. Hence, 5G will provide three key aspects, such as: enhanced mobile broad-band (eMBB) services, massive machine type communications (mMTC) and ultra reliable low latency communications (URLLC). In order to accomplish all the mentioned requirements, it is important to develop new key radio technologies capable of exploiting the wireless environment with a higher efficiency. Orthogonal frequency division multiplexing (OFDM) is the most widely used waveform by the industry, however, it also exhibits high side lobes reducing considerably the spectral efficiency. Therefore, filter-bank multi-carrier combined with offset quadrature amplitude modulation (FBMC-OQAM) is a waveform candidate to replace OFDM due to the fact that it provides extremely low out-ofband emissions (OBE). The traditional spectrum frequencies range is close to saturation, thus, there is a need to exploit higher bands, such as millimeter waves (mm-Wave), making possible the deployment of ultra broad-band services. However, the high path loss in these bands increases the blockage probability of the radio-link, forcing us to use massive multiple-input multiple-output (MIMO) systems in order to increase either the diversity or capacity of the overall link. All these emergent radio technologies can make 5G a reality. However, all their benefits can be only exploited under the knowledge and availability of the channel state information (CSI) in order to compensate the effects produced by the channel. The channel estimation process is a well known procedure in the area of signal processing for communications, where it is a challenging task due to the fact that we have to obtain a good estimator, maintaining at the same time the efficiency and reduced complexity of the system and obtaining the results as fast as possible. In FBMC-OQAM, there are several proposed channel estimation techniques, however, all of them required a high number of operations in order to deal with the self-interference produced by the prototype filter, hence, increasing the complexity. The existing channel estimation and equalization techniques for massive MIMO are in general too complex due to the large number of antennas, where we must estimate the channel response of each antenna of the array and perform some prohibitive matrix inversions to obtain the equalizers. Besides, for the particular case of mm-Wave, the existing techniques either do not adapt well to the dynamic ranges of signal-to-noise ratio (SNR) scenarios or they assume some approximations which reduce the quality of the estimator. In this thesis, we focus on the channel estimation for different emerging techniques that are capable of obtaining a better performance with a lower number of operations, suitable for low complexity devices and for URLLC. Firstly, we proposed new pilot sequences for FBMC-OQAM enabling the use of a simple averaging process in order to obtain the CSI. We show that our technique outperforms the existing ones in terms of complexity and performance. Secondly, we propose an alternative low-complexity way of computing the precoding/postcoding equalizer under the scenario of massive MIMO, keeping the quality of the estimator. Finally, we propose a new channel estimation technique for massive MIMO for mm-Wave, capable of adapting to very variable scenarios in terms of SNR and outperforming the existing techniques. We provide some analysis of the mean squared error (MSE) and complexity of each proposed technique. Furthermore, some numerical results are given in order to provide a better understanding of the problem and solutions.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Antonia María Tulino.- Secretario: Máximo Morales Céspedes.- Vocal: Octavia A. Dobr

One-Shot Messaging at Any Load Through Random Sub-Channeling in OFDM

Compressive Sensing has well boosted massive random access protocols over the last decade. In this paper we apply an orthogonal FFT basis as it is used in OFDM, but subdivide its image into so-called sub-channels and let each sub-channel take only a fraction of the load. In a random fashion the subdivision is consecutively applied over a suitable number of time-slots. Within the time-slots the users will not change their sub-channel assignment and send in parallel the data. Activity detection is carried out jointly across time-slots in each of the sub-channels. For such system design we derive three rather fundamental results: i) First, we prove that the subdivision can be driven to the extent that the activity in each sub-channel is sparse by design. An effect that we call sparsity capture effect. ii) Second, we prove that effectively the system can sustain any overload situation relative to the FFT dimension, i.e. detection failure of active and non-active users can be kept below any desired threshold regardless of the number of users. The only price to pay is delay, i.e. the number of time-slots over which cross-detection is performed. We achieve this by jointly exploring the effect of measure concentration in time and frequency and careful system parameter scaling. iii) Third, we prove that parallel to activity detection active users can carry one symbol per pilot resource and time-slot so it supports so-called one-shot messaging. The key to proving these results are new concentration results for sequences of randomly sub-sampled FFTs detecting the sparse vectors "en bloc". Eventually, we show by simulations that the system is scalable resulting in a coarsely 30-fold capacity increase compared to standard OFDM

Signal Processing and Learning for Next Generation Multiple Access in 6G

Wireless communication systems to date primarily rely on the orthogonality of resources to facilitate the design and implementation, from user access to data transmission. Emerging applications and scenarios in the sixth generation (6G) wireless systems will require massive connectivity and transmission of a deluge of data, which calls for more flexibility in the design concept that goes beyond orthogonality. Furthermore, recent advances in signal processing and learning have attracted considerable attention, as they provide promising approaches to various complex and previously intractable problems of signal processing in many fields. This article provides an overview of research efforts to date in the field of signal processing and learning for next-generation multiple access, with an emphasis on massive random access and non-orthogonal multiple access. The promising interplay with new technologies and the challenges in learning-based NGMA are discussed