Multidimensional Index Modulation for 5G and Beyond Wireless Networks

This study examines the flexible utilization of existing IM techniques in a comprehensive manner to satisfy the challenging and diverse requirements of 5G and beyond services. After spatial modulation (SM), which transmits information bits through antenna indices, application of IM to orthogonal frequency division multiplexing (OFDM) subcarriers has opened the door for the extension of IM into different dimensions, such as radio frequency (RF) mirrors, time slots, codes, and dispersion matrices. Recent studies have introduced the concept of multidimensional IM by various combinations of one-dimensional IM techniques to provide higher spectral efficiency (SE) and better bit error rate (BER) performance at the expense of higher transmitter (Tx) and receiver (Rx) complexity. Despite the ongoing research on the design of new IM techniques and their implementation challenges, proper use of the available IM techniques to address different requirements of 5G and beyond networks is an open research area in the literature. For this reason, we first provide the dimensional-based categorization of available IM domains and review the existing IM types regarding this categorization. Then, we develop a framework that investigates the efficient utilization of these techniques and establishes a link between the IM schemes and 5G services, namely enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communication (URLLC). Additionally, this work defines key performance indicators (KPIs) to quantify the advantages and disadvantages of IM techniques in time, frequency, space, and code dimensions. Finally, future recommendations are given regarding the design of flexible IM-based communication systems for 5G and beyond wireless networks.Comment: This work has been submitted to Proceedings of the IEEE for possible publicatio

A generalized space-frequency index modulation scheme for downlink MIMO transmissions with improved diversity

Multidimensional Index Modulations (IM) are a novel alternative to conventional modulations which can bring considerable benefits for future wireless networks. Within this scope, in this paper we present a new scheme, named as Precoding-aided Transmitter side Generalized Space-Frequency Index Modulation (PT-GSFIM), where part of the information bits select the active antennas and subcarriers which then carry amplitude and phase modulated symbols. The proposed scheme is designed for multiuser multiple-input multiple-output (MU-MIMO) scenarios and incorporates a precoder which removes multiuser interference (MUI) at the receivers. Furthermore, the proposed PT-GSFIM also integrates signal space diversity (SSD) techniques for tackling the typical poor performance of uncoded orthogonal frequency division multiplexing (OFDM) based schemes. By combining complex rotation matrices (CRM) and subcarrier-level interleaving, PT-GSFIM can exploit the inherent diversity in frequency selective channels and improve the performance without additional power or bandwidth. To support reliable detection of the multidimensional PT-GSFIM we also propose three different detection algorithms which can provide different tradeoffs between performance and complexity. Simulation results shows that proposed PT-GSFIM scheme, can provide significant gains over conventional MU-MIMO and GSM schemes.info:eu-repo/semantics/publishedVersio

Index Modulation-based Information Harvesting for Far-Field RF Power Transfer

While wireless information transmission (WIT) is evolving into its sixth generation (6G), maintaining terminal operations that rely on limited battery capacities has become one of the most paramount challenges for Internet-of-Things (IoT) platforms. In this respect, there exists a growing interest in energy harvesting technology from ambient resources, and wireless power transfer (WPT) can be the key solution towards enabling battery-less infrastructures referred to as zero-power communication technology. Indeed, eclectic integration approaches between WPT and WIT mechanisms are becoming a vital necessity to limit the need for replacing batteries. Beyond the conventional separation between data and power components of the emitted waveforms, as in simultaneous wireless information and power transfer (SWIPT) mechanisms, a novel protocol referred to as information harvesting (IH) has recently emerged. IH leverages existing WPT mechanisms for data communication by incorporating index modulation (IM) techniques on top of the existing far-field power transfer mechanism. In this paper, a unified framework for the IM-based IH mechanisms has been presented where the feasibility of various IM techniques are evaluated based on different performance metrics. The presented results demonstrate the substantial potential to enable data communication within existing far-field WPT systems, particularly in the context of next-generation IoT wireless networks.Comment: 13 pages, 9 figure

Performance Enhancement by Exploiting the Spatial Domain for Cost, Space and Spectrum Constraint 5G Communication

With everlasting increase of connectivity demand and high speed data communication, lots of progresses have been made to provide a sufficient quality of services (QoS). Several advanced technologies have been the cornerstone of this trend in academia as well as in industry. Nevertheless, there are some implementation challenges, which needs to be closely investigated. In this thesis, among all challenges, we elaborate on those related to number of radio frequency (RF) chains and resource scarcity. The principle idea behind our proposed initial solution is to exploit the spatial domain as an additional degree of freedom. To be more specific, we benefit from spatial domain and antenna index in a multiple-input multiple-output (MIMO) system with dual-polarized (DP) antennas to convey the information. We develop a two-stage algorithm to groups the antennas which ends up to the optimum performance. Another advantage of this proposed algorithm is the complete complexity reduction of exhaustive search over the whole available space. Moreover, due to the continuous growth of demands which results in spectrum scarcity, we investigate the extension of long term evolution (LTE) spectrum. Such a paradigm shift is realized to offload part of the data to unlicensed band, which has been initially dedicated to other standardizations such as wireless local area networks (WLAN). As both LTE and wireless fidelity (Wi-Fi) networks have been widely deployed with solid infrastructures, it is significantly important to make their coexistence viable with a cost-effective approach which inherently requires the minimum protocol modification. Thus, we take the advantage of spatially located multiple antennas of base station (BS) and access point (AP) for the sake of beamforming and interference reduction. In addition to network coexistence, we approach the resource scarcity from the non-orthogonal multiple access (NOMA) point of view, where users share the frequency and time resources and are differentiated in power domain. In particular, we closely consider those users with limited number of RF chains. Similar to our first approach, we utilize spatial modulation (SM) in user end and after evaluating their performance, we propose to consider the capacity of SM NOMA to elaborate the impact of pairing on the achievable sum rate performance

Quadrature spatial modulation aided single-input multiple-output-media based modulation: application to cooperative network and golden code orthogonal super-symbol systems.

Doctoral Degree. University of KwaZulu-Natal, Durban.SIMO-MBM (single-input multiple-output media-based modulation) overcomes the limitations of SIMO (single-input multiple-output) systems by reducing the number of antennas required to achieve a high data rate and improved error performance. In this thesis, the quadrature dimension of the spatial constellation is used to improve the overall error performance of the conventional SIMO-MBM and to achieve a higher data rate by decomposing the amplitude/phase modulation (APM) symbol into real and imaginary components, similar to quadrature spatial modulation (QSM). The average bit error probability of the proposed technique is expressed using a lower bound approach and validated using the results of Monte Carlo simulation (MCS). The proposed system also investigates the effect of antenna correlation in combination with channel amplitude to select a sub-optimal mirror activation pattern. The results of MCS show a 3.5dB improvement at 10b/s/Hz with m =2 and a 7dB improvement at 12b/s/Hz with =2 over the traditional SIMO-MBM scheme. The effect of imperfect channel estimation on the proposed scheme is investigated, with a trade-off of 2dB in coding gain due to channel estimation errors. Cooperative Networking (CN) improves wireless network reliability, link quality, and spectrum efficiency by collaborating among nodes. The decode and forward relaying technique is used in this thesis to investigate the performance of QSM aided SIMO-MBM in a Cooperative Network (CN). This technique uses two source nodes that simultaneously transmit a unique message block on the same time slot to the relay node, which then decodes the received message block from both transmitting nodes before re-encoding and re-transmitting the decoded message block in the next time slot to the destinations in order to significantly improve the QSM aided SIMO-MBM’s error performance. Using network coding (NC) techniques, each Node can decode the data of the other Node. To enhance network performance, complexity, robustness, and minimize delays, data is encoded and decoded in NC; algebraic techniques are applied to the detected message to collect the various transmissions. The proposed scheme's theoretical average error probability was defined using a lower bound technique, and the results of Monte Carlo simulation (MCS) validated the result. The MCS results achieved exhibit a significant improvement of 8 dB at 6 b/s/Hz and 12 dB at 8 b/s/Hz over the conventional QSM aided SIMO-MBM scheme. The media-based modulation (MBM) technique can achieve significant throughput, increase spectrum efficiency, and improve bit-error-rate performance (BER). In this thesis, the use of MBM in single-input multiple-output systems is examined using radio frequency (RF) mirrors and Golden code (GC-SIMO). The goal is to lower the system's hardware complexity by maximizing the linear relationship between RF mirrors and spectral efficiency in MBM in order to achieve a high data rate with less hardware complexity. The GC scheme's encoder uses orthogonal pairs of the super-symbol, each transmitted via a separate RF mirror at a different time slot to achieve full rate full diversity. In the results of MCS obtained, at a BER of 10−5, the GC-SIMO-MBM exhibits a significant performance of approximately 7dB and 6.5 dB SNR gain for 4 b/s/Hz and 6 b/s/Hz, respectively, compared to GC-SIMO. The proposed scheme's derived theoretical average error probability is validated by the results of the Monte Carlo simulation

Optimizing the stochastic deployment of small base stations in an interleave division multiple access-based heterogeneous cellular networks

The use of small base stations (SBSs) to improve the throughput of cellular networks gave rise to the advent of heterogeneous cellular networks (HCNs). Still, the interleave division multiple access (IDMA) performance in sleep mode active HCNs has not been studied in the existing literature. This research examines the 24-h throughput, spectral efficiency (SE), and energy efficiency (EE) of an IDMA-based HCN and compares the result with orthogonal frequency division multiple access (OFDMA). An energy-spectral-efficiency (ESE) model of a two-tier HCN was developed. A weighted sum modified particle swarm optimization (PSO) algorithm simultaneously maximized the SE and EE of the IDMA-based HCN. The result obtained showed that the IDMA performs at least 68% better than the OFDMA on the throughput metric. The result also showed that the particle swarm optimization algorithm produced the Pareto optimal front at moderate traffic levels for all varied network parameters of SINR threshold, SBS density, and sleep mode technique. The IDMA-based HCN can improve the throughput, SE, and EE via sleep mode techniques. Still, the combination of network parameters that simultaneously maximize the SE and EE is interference limited. In sleep mode, the performance of the HCN is better if the SBSs can adapt to spatial and temporal variations in network traffic.publishedVersio

Two-phase Unsourced Random Access in Massive MIMO: Performance Analysis and Approximate Message Passing Decoder

In this paper, we design a novel two-phase unsourced random access (URA) scheme in massive multiple input multiple output (MIMO). In the first phase, we collect a sequence of information bits to jointly acquire the user channel state information (CSI) and the associated information bits. In the second phase, the residual information bits of all the users are partitioned into sub-blocks with a very short length to exhibit a higher spectral efficiency and a lower computational complexity than the existing transmission schemes in massive MIMO URA. By using the acquired CSI in the first phase, the sub-block recovery in the second phase is cast as a compressed sensing (CS) problem. From the perspective of the statistical physics, we provide a theoretical framework for our proposed URA scheme to analyze the induced problem based on the replica method. The analytical results show that the performance metrics of our URA scheme can be linked to the system parameters by a single-valued free entropy function. An AMP-based recovery algorithm is designed to achieve the performance indicated by the proposed theoretical framework. Simulations verify that our scheme outperforms the most recent counterparts.Comment: 16pages,7 figure

2022, nr 2, JTIT

kwartalni

Machine Learning for Intelligent IoT Networks with Edge Computing

The intelligent Internet of Things (IoT) network is envisioned to be the internet of intelligent things. In this paradigm, billions of end devices with internet connectivity will provide interactive intelligence and revolutionise the current wireless communications. In the intelligent IoT networks, the unprecedented volume and variety of data is generated, making centralized cloud computing ine cient or even infeasible due to network congestion, resource-limited IoT devices, ultra-low latency applications and spectrum scarcity. Edge computing has been proposed to overcome these issues by pushing centralized communication and computation resource physically and logically closer to data providers and end users. However, compared with a cloud server, an edge server only provides nite computation and spectrum resource, making proper data processing and e cient resource allocation necessary. Machine learning techniques have been developed to solve the dynamic and complex problems and big data analysis in IoT networks. Speci - cally, Reinforcement Learning (RL) has been widely explored to address the dynamic decision making problems, which motivates the research on machine learning enabled computation o oading and resource management. In this thesis, several original contributions are presented to nd the solutions and address the challenges. First, e cient spectrum and power allocation are investigated for computation o oading in wireless powered IoT networks. The IoT users o oad all the collected data to the central server for better data processing experience. Then a matching theory-based e cient channel allocation algorithm and a RL-based power allocation mechanism are proposed. Second, the joint optimization problem of computation o oading and resource allocation is investigated for the IoT edge computing networks via machine learning techniques. The IoT users choose to o oad the intensive computation tasks to the edge server while keep simple task execution locally. In this case, a centralized user clustering algorithm is rst proposed as a pre-step to group the IoT users into di erent clusters according to user priorities for achieving spectrum allocation. Then the joint computation o oading, computation resource and power allocation for each IoT user is formulated as an RL framework and solved by proposing a deep Q-network based computation o oading algorithm. At last, to solve the simultaneous multiuser computation o oading problem, a stochastic game is exploited to formulate the joint problem of computation o oading mechanism of multiple sel sh users and resource (including spectrum, computation and radio access technologies resources) allocation into a non-cooperative multiuser computation o oading game. Therefore, a multi-agent RL framework is developed to solve the formulated game by proposing an independent learners based multi-agent Q-learning algorithm