Recent years have seen the rapid development of long-range wide area network (LoRaWAN) operating in region-specific sub-GHz frequency bands (e.g., 868 MHz in Europe and 915 MHz in North America). To achieve global deployment, LoRaWAN has been extended to operate in the globally available 2.4 GHz unlicensed band. However, this shift exposes LoRaWAN to significant interference from coexisting Wi-Fi networks, which share the same band and typically transmit at much higher power levels. To address this problem, this paper presents InMAC, an interference-aware medium access control (MAC) protocol designed to improve coexistence between LoRaWAN and Wi-Fi networks. To the best of our knowledge, InMAC is the first MAC protocol specifically tailored to mitigate Wi-Fi interference for 2.4 GHz LoRaWAN. InMAC enhances LoRaWAN communication by probabilistically exploiting the silent time in Wi-Fi traffic, leveraging a Wi-Fi traffic profiling mechanism at LoRaWAN gateways and a packet length adaptation strategy at end devices. In addition to mitigating external interference from Wi-Fi, InMAC also tackles internal interference caused by signal collisions among LoRaWAN end devices. It incorporates a novel channel access mechanism based on Channel Activity Detection, a carrier-sensing technique adapted specifically for LoRaWAN. Experimental results demonstrate that InMAC reduces both external Wi-Fi interference and internal LoRaWAN collisions, achieving up to a 111% throughput boost over existing approaches.

Falls remain a major cause of injury and death among older adults, which shows the need for reliable and non-intrusive monitoring solutions in healthcare environments. In this paper, we propose a novel Ambient Internet of Things (IoT) backscatter sensing system that utilizes a dense array of passive tags and a minimal number of reader antennas for cost-effective fall detection and localization. To fully exploit the spatial and temporal characteristics of ambient backscatter sensing data, we design a hierarchical multi-task spatio-temporal graph attention network (HM-STGAT), which jointly models the spatial relationships among tags and antennas as well as the temporal dynamics of human activities. The proposed unified framework simultaneously detects fall events and accurately estimates fall locations. We validate the proposed approach through a real-world experiment to collect a diverse dataset of fall and non-fall scenarios. Experimental results demonstrate that the proposed method achieves state-of-the-art performance in both fall detection accuracy and localization precision, highlighting its potential for practical deployment in healthcare monitoring applications.

In this paper, we propose a radio frequency identification (RFID)–based integrated sensing and communication (ISAC) system that uses the variations in received signal strength (RSS) from a passive tag grid for both robust communication and healthcare monitoring. Our approach utilizes the same RSS fluctuations for dual purposes: human activity triggered key generation and fall detection. Specifically, we conducted a hardware experiment to collect real-time RSS data from the tag grid and proposed a dynamic threshold adjustment-based physical layer key generation algorithm that guarantees robust and secure communication activated by human motion. For the healthcare sensing, we developed a non-wearable-based fall detection system by detecting the sudden RSS variations. We also generated heatmaps to visualize real-time alerts and fall location. By unifying these two functions, our system not only demonstrates the practicality and efficiency of using RFID as an ISAC platform, but also overcomes the traditional trade-off between communication and sensing.

This paper proposes a spectrum-efficient non-orthogonal affine frequency division multiplexing (AFDM) waveform for reliable high-mobility communications in the upcoming sixth-generation (6G) mobile systems. Our core idea is to introduce a compression factor to enable controllable subcarrier overlapping in chirp-based AFDM modulation. To mitigate inter-carrier interference (ICI), we introduce linear precoding at the transmitter and an iterative detection scheme at the receiver. Simulation results demonstrate that these techniques can effectively reduce interference and maintain robust bit error rate (BER) performance even under aggressive compression factors and high-mobility channel conditions. The proposed non-orthogonal AFDM waveform offers a promising solution for next-generation wireless networks, balancing spectrum efficiency and Doppler resilience in highly dynamic environments.

Future wireless networks targeted for improving spectral and energy efficiency, are expected to simultaneously provide sensing functionality and support low-power communications. This paper proposes a novel net-zero integrated sensing and communication (ISAC) model for backscatter systems, including an access point (AP), a net-zero device, and a user receiver. We fully utilize the backscatter mechanism for sensing and communication without additional power consumption and signal processing in the hardware device, which reduces the system complexity and makes it feasible for practical applications. To further optimize the system performance, we design a novel signal frame structure for the ISAC model that effectively mitigates communication interference at the transmitter, tag, and receiver. Additionally, we employ distributed antennas for sensing which can be placed flexibly to capture a wider range of signals from diverse angles and distances, thereby improving the accuracy of sensing. We derive theoretical expressions for the symbol error rate (SER) and tag’s location detection probability, and provide a detailed analysis of how the system parameters, such as transmit power and tag’s reflection coefficient, affect the system performance.

While symbol-level (SL) precoders have been shown to improve transmission performance by treating multi-user interference (MUI) as a useful resource, the SL precoders only employ uniform modulation for all downlink users, and the incurred complexity increases linearly with the block length. In this letter, we investigate the possibility of mixed-modulations interference exploitation (IE) for satellite communications, at a sub-block level. By exploiting the specific detection regions of constellation points of different modulations, a novel sub-block level mixed-modulation (BL-MIE) design is proposed, guaranteeing that MUI is always constructive in each sub-block duration, regardless of the users’ heterogeneous modulation schemes. Compared to the classic SL design, it is proved that the BL-MIE provides complexity reduction on the order of square root of the sub-block length, i.e., ${\mathcal{O}}(\sqrt{n})$, with n denoting the number of symbols per sub-block. Hence, it well strikes the balance between the performance and complexity. Simulation demonstrates that the proposed designs significantly outperform the benchmarks in terms of power consumption and throughput performance.

Traditional communications focus on regular and orthogonal signal waveforms for simplified signal processing and improved spectral efficiency. In contrast, the next-generation communications would aim for irregular and nonorthogonal signal waveforms to introduce new capabilities. This work proposes a spectrally efficient irregular Sinc (irSinc) shaping technique, revisiting the traditional Sinc back to 1924, with the aim of enhancing performance in Industrial Internet of Things (IIoT). In time-critical IIoT applications, low-latency and time-jitter tolerance are two critical factors that significantly impact the performance and reliability. Recognizing the inevitability of latency and jitter in practice, this work aims to propose a waveform technique to mitigate these effects via reducing latency and enhancing the system robustness under time jitter effects. The utilization of irSinc yields a signal with increased spectral efficiency without sacrificing error performance. Integrating the irSinc in a two-stage framework, a single-carrier nonorthogonal frequency shaping (SC-NOFS) waveform is developed, showcasing perfect compatibility with fifth generation (5G) standards, enabling the direct integration of irSinc in existing industrial Internet of things (IoT) setups. Through 5G standard signal configuration, our signal achieves faster data transmission within the same spectral bandwidth. Hardware experiments validate an 18% saving in timing resources, leading to either reduced latency or enhanced jitter tolerance.

Traditional physical layer secure beamforming is achieved via precoding before signal transmission using channel state information (CSI). However, imperfect CSI will compromise the performance with imperfect beamforming and potential information leakage. In addition, multiple RF chains and antennas are needed to support the narrow beam generation, which complicates hardware implementation and is not suitable for resource-constrained Internet-of-Things (IoT) devices. Moreover, with the advancement of hardware and artificial intelligence (AI), low-cost and intelligent eavesdropping to wireless communications is becoming increasingly detrimental. In this paper, we propose a multi-carrier based multi-band waveform-defined security (WDS) framework, independent from CSI and RF chains, to defend against AI eavesdropping. Ideally, the continuous variations of sub-band structures lead to an infinite number of spectral features, which can potentially prevent brute-force eavesdropping. Sub-band spectral pattern information is efficiently constructed at legitimate users via a proposed chaotic sequence generator. A novel security metric, termed signal classification accuracy (SCA), is used to evaluate the security robustness under AI eavesdropping. Communication error probability and complexity are also investigated to show the reliability and practical capability of the proposed framework. Finally, compared to traditional secure beamforming techniques, the proposed multi-band WDS framework reduces power consumption by up to six times.

Communication security could be enhanced at the physical layer but at the cost of complex algorithms and redundant hardware, which would render traditional physical-layer security (PLS) techniques unsuitable for use with resource-constrained communication systems. This work investigates a waveform-defined security (WDS) framework, which differs fundamentally from traditional PLS techniques used in today’s systems. The framework is not dependent on channel conditions, such as signal power advantage and channel state information (CSI). Therefore, the framework is more reliable than channel-dependent beamforming and artificial noise (AN) techniques. In addition, the framework is more than just increasing the cost of eavesdropping. By intentionally tuning waveform patterns to weaken signal feature diversity and enhance feature similarity, eavesdroppers will not be able to identify correctly the signal formats. The wrong classification of signal formats would result in subsequent detection errors even when an eavesdropper uses brute-force detection techniques. To get a robust WDS framework, three impact factors, namely, the training data feature, oversampling factor, and bandwidth compression factor (BCF) offset, are investigated. An optimal WDS waveform pattern is obtained at the end after a joint study of the three factors. To ensure a valid eavesdropping model, artificial intelligence (AI)-dependent signal classifiers are designed followed by optimal performance achievable signal detectors. To show the compatibility in available communication systems, the WDS framework is successfully integrated in IEEE 802.11a with nearly no adding computational complexity. Finally, a low-cost software-defined radio (SDR) experiment is designed to verify the feasibility of the WDS framework in resource-constrained communications

Spectral efficiency improvement is a key focus in most wireless communication systems and achieved by various means such as using large antenna arrays and/or advanced modulation schemes and signal formats. This work proposes to further improve spectral efficiency through combining non-orthogonal spectrally efficient frequency division multiplexing (SEFDM) systems with index modulation (IM), which can efficiently make use of the indices of activated subcarriers as communication information. Recent research has verified that IM may be used with SEFDM to alleviate inter-carrier interference (ICI) and improve error performance. This work proposes new SEFDM signal formats based on novel activation pattern designs, which limit the locations of activated subcarriers and enable a variable number of activated subcarriers in each SEFDM subblock. SEFDM-IM system designs are developed by jointly considering activation patterns, modulation schemes and signal waveform formats, with a set of solutions evaluated under different spectral efficiency scenarios. Detailed modelling of coded systems and simulation studies reveal that the proposed designs not only lead to better bit error rate (BER) but also lower peak-to-average power ratio (PAPR) and reduced computational complexity relative to other reported index-modulated systems.

Wireless signals are commonly used for communications. Emerging applications are giving new functions to wireless signals, in which wireless sensing is the most attractive one. Channel state information (CSI) is not only the parameter for channel equalization in communications but also the indicator for wireless sensing. However, due to the broadcast nature of wireless signals, eavesdroppers can easily capture legitimate user signals and violate user privacy by measuring CSI. Moreover, the advancement of hardware simplifies illegal eavesdropping since smart devices can track over-the-air signals through walls. Therefore, this work considers a waveform-defined privacy (WDP) solution that can hide CSI phase information and therefore protect user privacy. Besides, the proposed waveform solution achieves better performance due to the use of a unique modulation mechanism. Additionally, by tuning a waveform parameter, the waveform can also enhance communication security.

Internet of things (IoT) offers advanced and intelligent services for our life. However, smart IoT devices also bring various security vulnerabilities. Traditionally, attacks are solved by conventional authentication and authorization schemes, requiring extensive time and computational resources. In addition, it is possible to exploit artificial intelligence (AI) to provide countermeasures while enabling lightweight authentication. In this paper, we explore a solution on modelling a spoofing detection system based on machine learning and we propose a deep learning method using Auto-Extractor/Classifier Neural Network. Our scheme operates on the physical layer without causing computational overhead. Therefore, the lightweight authentication can be achieved and spoofing attacks are well- controlled in IoT scenarios.

In this paper, we investigate power-efficient constructive interference (CI) exploitation in multi-cell coordination systems. By only sharing channel state information (CSI) among the coordinated base stations (BS)s, we propose a CI-based coordinated beamforming (CBF) scheme to judiciously exploit multiuser interference as a beneficial element rather than strictly mitigating it, while simultaneously suppressing inter-cell interference as a destructive element. Then taking imperfect channel state information (CSI) into consideration, we minimize the total transmission power consumption with multiple users' probabilistic signal-to-interference-and-noise ratio (SINR) requirements, where the users' SINR requirements are guaranteed in a statistical manner. Finally, under the presence of CSI error, simulation results demonstrate that the proposed CI-based CBF scheme consumes much lower transmission power compared to the classical CBF benchmarks, where both intra-cell multi-user and inter-cell interference need to be strictly cancelled as destructive elements. Last but not least, the incurred overhead and computational complexity of the proposed scheme are analytically analyzed, confirming its practicality as a new dimension on multi-cell coordination.

This paper proposes a frequency-domain multiple user access scheme termed non-orthogonal frequency division multiple access (NoFDMA), which maintains the same data rate per user while allowing more users to access via non-orthogonal user overlapping in a given spectral band. User side signal processing follows existing standards with minor modifications. Receiver side operation can jointly process signals from all the users. Computational complexity is investigated for NoFDMA, which shows slightly increased operations than the typical orthogonal frequency division multiple access (OFDMA). Nevertheless, effective spectral efficiency of NoFDMA, considering both raw spectral efficiency and computational complexity, is higher than that of OFDMA. The scalability of the multiple access scheme is flexible via tuning the user overlapping ratio. Simulation reveals that the number of accessed users is doubled using the NoFDMA strategy when compared with the traditional OFDMA scheme over the same spectral resource utilization.

Non-cooperative communications, where a receiver can automatically distinguish and classify transmitted signal formats prior to detection, are desirable for low-cost and low-latency systems. This work focuses on the deep learning enabled blind classification of multi-carrier signals covering their orthogonal and non-orthogonal varieties. We define Type-I signals with large feature diversity and Type-II signals with strong feature similarity. We evaluate time-domain and frequency-domain convolutional neural network (CNN) models with wireless channel/hardware impairments. Experimental systems are designed and tested, using software defined radio (SDR) devices, operated for different signal formats in line-of-sight and non-line-of-sight communication link scenarios. Testing, using four different time-domain CNN models, showed the pre-trained CNN models to have limited efficiency and utility due to the mismatch between the analytical/simulation and practical/real-world environments. Transfer learning, which is an approach to fine-tune learnt signal features, is applied based on measured over-the-air time-domain signal samples. Experimental results indicate that transfer learning based CNN can efficiently distinguish different signal formats for Type-I in both line-of-sight and non-line-of-sight scenarios relative to the non-transfer-learning approaches. Type-II signals are not identified correctly in the experiment even with the transfer learning assistance leading to potential applications in secure communications.

To enable user diversity and multiplexing gains, a fully digital precoding multiple-input-multiple-output (MIMO) architecture is typically applied. However, a large number of radio frequency (RF) chains make the system unrealistic to low-cost communications. Therefore, a practical three-stage hybrid analog-digital precoding architecture, occupying fewer RF chains, is proposed aiming for a nonorthogonal Internet of Things (IoT) signal in low-cost multiuser MIMO systems. The nonorthogonal waveform can flexibly save spectral resources for massive devices connections or improve data rate without consuming extra spectral resources. The hybrid precoding is divided into three stages, including analog domain, digital domain, and waveform domain. A codebook-based beam selection simplifies the analog-domain beamforming via phase-only tuning. Digital-domain precoding can fine-tune the codebook shaped beam and resolve multiuser interference in terms of both signal amplitude and phase. In the end, the waveform-domain precoding manages the self-created intercarrier interference (ICI) of the nonorthogonal signal. This article designs over-the-air signal transmission experiments for fully digital and hybrid precoding systems on software-defined radio (SDR) devices. Results reveal that waveform precoding accuracy can be enhanced by hybrid precoding. Compared to a transmitter with the same RF chain resources, hybrid precoding significantly outperforms fully digital precoding by up to 15.6 dB error vector magnitude (EVM) gain. A fully digital system with the same number of antennas clearly requires more RF chains and, therefore, is low power, space-efficient, and cost-efficient. Therefore, the proposed three-stage hybrid precoding is a quite suitable solution to nonorthogonal IoT applications.

Massive connectivity is one of the main research directions for beyond 5G. The cellular based narrowband IoT (NB-IoT), enabled by the orthogonal frequency division multiplexing (OFDM) signal, is an important technique. To evolve into the beyond 5G era, non-orthogonal concepts are preferred to re-shape the NB-IoT to provide higher spectral efficiency, wider coverage and lower power consumed services. This work investigates a non-orthogonal waveform in next generation IoT (NG-IoT) scenarios. Previous work has verified the advantages of zero forcing (ZF) precoding in interference mitigation but with some limitations. This work proposes a singular value reconstruction (SVR) precoding method, which can improve the precoding reliability and greatly reduce noise sensitivity. Simulations show significant spectral efficiency gain when compared with the previous work. An experiment platform is then configured in an over-the-air multiuser multiple input multiple output (MIMO) scenario to verify the practical feasibility of the precoding algorithm.

The non-orthogonal IoT signal, following the bandwidth compression spectrally efficient frequency division multiplexing (SEFDM) characteristics, can bring benefits in enhanced massive device connections, signal coverage extension and data rate increase, but at the cost of computational complexity. Resource-constrained IoT devices have limited memory storage and complex signal processing is not allowed. Machine learning can simplify signal detection by training a general data-driven signal detection model. However, fully connected neural networks would introduce processing latency and extra power consumption. Therefore, the motivation of this work is to investigate different neural network compression schemes for system simplification. Three compression strategies are studied including topology compression, weight compression and quantization compression. These methods show efficient neural network compression with trade-offs between computational complexity and bit error rate (BER) performance. Practical neural network prototyping is evaluated as well on a software defined radio (SDR) platform. Results show that the practical weight compression neural network can achieve similar performance as the fully connected neural network but with great resource saving.

Single carrier frequency division multiple access (SC-FDMA) has been comprehensively investigated and standardized in 4th generation (4G) and 5th generation (5G) mobile systems. Its significant advantage is low peak-to-average power ratio (PAPR), which makes it suitable for uplink channel communications. However, over a long time period, SC-FDMA has not made breakthrough especially in data rate enhancement, which may not catch up with the next generation evolution in communications. This work proposes to use a non-orthogonal waveform in SC-FDMA to promote a new concept, termed single carrier spectrally efficient frequency division multiple access (SC-SEFDMA). This non-orthogonal single carrier access technique maintains essentially similar complexity as SC-FDMA but advantageously can either achieve higher data rate for the same amount of power consumption or the same data rate with less power consumption.

This paper investigates duobinary signals and their applications in non-orthogonal multi-carrier systems to enhance spectral efficiency. In duobinary transmission schemes, the signal spectrum is reshaped by introducing controlled correlation, which can be eliminated at the duobinary decoder. For the first time, we propose the idea of combining duobinary transmission technique and the fast orthogonal frequency division multiplexing (Fast-OFDM) system with three subcarriers, with experimental results presented. The proposed system is capable of achieving three times the data rate of single-carrier ASK scheme with the same bandwidth. Results show that bit error rate (BER) performance of the proposed duobinary-based Fast-OFDM system is slightly worse than the ASK system. In addition, we also tested various 3-PSK constellation patterns designed for the duobinary signal to achieve performance improvement.

Narrowband Internet of Things (NB-IoT) was introduced by 3GPP in low power wide area network to support low power and wide coverage applications. Since it follows long term evolution standard, its signal quality is guaranteed and its deployment is straightforward via reusing existing infrastructures. Current NB-IoT supports low data rate services via using low order modulation formats for the purpose of power saving. However, with the increase of data rate driven applications, next generation NB-IoT would require data rate enhancement techniques without consuming extra battery power. In this paper, a downlink framework, using a nonorthogonal signal waveform for next generation enhanced NB-IoT (eNB-IoT), is proposed and experimentally tested in both single-antenna and multiantenna systems. In the single-antenna scenario, waveform precoding is used to pre-equalize the self-created inter carrier interference distorted signal waveform. For the multiantenna multiuser scenario, both waveform and antenna space precoding have to be used. Measured results show that in both single-antenna and multiantenna systems, the proposed signal waveform in eNB-IoT can increase data rate by ~11% compared with NB-IoT occupying the same spectral resource in similar receiver computational complexity.

Narrowband Internet of Things (NB-IoT) is widely used in low power wide area network (LPWAN) applications due to its long distance coverage and low power consumption. According to the 3GPP NB-IoT standard, the maximum modulation format that NB-IoT can support is QPSK, which limits the data rate sensitive IoT applications. To overcome this limitation either higher order modulation formats such as 8PSK or advanced non-orthogonal signal waveforms can be used. In this work, we propose an enhanced NB-IoT framework, eNB-IoT, which applies a non-orthogonal spectrally efficient frequency division multiplexing (SEFDM) signal waveform beyond the typical orthogonal frequency division multiplexing (OFDM). Since the uplink signal recovery is at base station (BS), in this work we use an efficient and sophisticated minimum Euclidean norm search detector, termed sphere decoding (SD) detector. Simulation results indicate that to achieve the same data rate improvement (i.e. 50% improvement) and BER performance, eNB-IoT, employing the non-orthogonal signal waveform, requires 3 dB less transmission power than typical NB-IoT using the dense modulation approach at BER=10-5. The saved power can either extend the battery life of IoT devices or extend the signal transmission distance. This work also proposes an overlapped SD (OSD) detector to simplify the BS signal processing. Its parallel architecture, employing multiple small size SD kernels, can speed up the signal detection. Results show that by using the OSD detector, eNB-IoT still outperforms NB-IoT but with one order of magnitude complexity reduction.

Narrowband Internet of Things (NB-IoT) is a cellular based IoT technique, which can send messages at a long distance using repetitive transmission and single tone frequency hopping. However, retuning of the RF frontend for each narrowband hop could cause frequency offset. Since each tone is shaped by a sinc pulse, when combining tones into a complete signal at the receiver, the side lobe of the sinc pulse would result in significant signal interference in the frequency offset condition. In this work, we propose a half-sinc (HS) waveform for uplink channels via cutting half band using the Hilbert transform to intentionally reserve a frequency offset protection gap. It is verified that the half-sinc waveform can tolerate up to 100% frequency offset via simulation and can practically remove the half side signal band in a software defined IoT platform.

Spectral efficiency can be improved in multicarrier systems through the employment of non-orthogonal overlapping sub-carriers, termed spectrally efficient frequency division multiplexing (SEFDM), but with self-created interference. Previous work has focused on signal detection development. The trade-off between performance and complexity is challenging. This work investigates a self interference cancellation scheme for SEFDM to make use of ICI information at the transmitter and simplify the design of receiver. Repetition codes are used in the system where the same symbol with opposite signs are modulated onto adjacent sub-carriers. Therefore, ICI caused by adjacent sub-carriers would be cancelled mutually. However, the spectral efficiency is reduced. In order to maintain the same spectral efficiency and mutual interference cancellation benefits, the optimal combination of various modulation formats and bandwidth compression factors have to be studied jointly to derive maximum achievable spectral efficiency. Both simulation and experiment are reported and results validate the performance of the proposed self interference cancellation scheme.

Narrowband Internet of Things (NB-IoT) is a low power wide area network (LPWAN) technique introduced in 3GPP release 13. The narrowband transmission scheme enables high capacity, wide coverage, and low power consumption communications. With the increasing demand for services over the air, wireless spectrum is becoming scarce and new techniques are required to boost the number of connected devices within a limited spectral resource to meet the service requirements. This paper provides a compressed signal waveform solution, termed fast-orthogonal frequency division multiplexing (FastOFDM), to double potentially the number of connected devices by compressing occupied bandwidth of each device without compromising data rate and bit error rate performance. Simulation is first evaluated for the Fast-OFDM with comparisons to singlecarrier-frequency division multiple access (SC-FDMA). Results indicate the same performance for both systems in additive white Gaussian noise channel. Experimental measurements are also presented to show the bandwidth saving benefits of Fast-OFDM. It is shown that in a line-of-sight scenario, Fast-OFDM has similar performance as SC-FDMA but with 50% bandwidth saving. This research paves the way for extended coverage, enhanced capacity and improved data rate of NB-IoT in fifth generation new radio networks.

This work investigates several experimental validations for the bandwidth compressed multicarrier signal termed spectrally efficient frequency division multiplexing (SEFDM). The signal compresses bandwidth, therefore improved spectral efficiency, by packing sub-carriers closer. Unlike typical orthogonal frequency division multiplexing (OFDM) signals, SEFDM violates the orthogonality criterion, therefore self-created inter carrier interference (ICI) is introduced. In this work, to ameliorate the effect of interference, a method based on sub-carrier pulse shaping, targeting massive machine-type communication (mMTC), is developed and tested experimentally. Practical over-the-air testing of the proposal is operated on commercially developed software defined radio platforms. Results show that in the condition of coexistence scenario SEFDM can significantly reduce interference when used with existing long term evolution (LTE) signals leading to improved quality of service. The throughput of LTE signals is therefore improved from 49.92 Mbps to 63.21 Mbps. Additionally, the proposed pulse shaping Nyquist-SEFDM performs well in scenarios where the spectrum is limited and in fact it outperforms pulse shaped OFDM significantly, both in terms of bandwidth saving and throughput, which is boosted from 4.35 Mbps to 43.36 Mbps.

Much work has been done on a non-orthogonal signal termed spectrally efficient frequency division multiplexing (SEFDM). Due to its self-created inter carrier interference (ICI), signal detection is complicated. A linear detector named iterative detection (ID) detector shows better bit error rate (BER) performance and complexity trade-off than other linear detectors. Therefore, this work shows the first time hardware modelling of the ID detector at the register transfer level (RTL) stage. The impact of bit precision on the system performance is studied at the beginning. Then, an RTL model is designed with results showing competitive fixed-point performance which are comparable to Matlab floating-point results. Verification work is operated in a co-simulation environment through comparison between fixed-point Matlab results and ISim (a hardware modelling software from Xilinx Inc.) simulation results. Their results are consistent indicating the hardware model is correct.

In the spectrally efficient frequency division multiplexing (SEFDM) non-orthogonal multicarrier signal, higher spectral efficiency can be achieved at the expense of self-created inter carrier interference (ICI). The effective interference, which is contributed by all sub-carriers, has to be minimized and this results in a receiver of significant complexity. In order to mitigate the interference and simplify the receiver design, in this work, a precoding technique, based on eigenvalue decomposition of the sub-carrier correlation matrix, is utilised. Briefly, the technique is based on modifying the data sent on individual sub-carriers according to the signal quality of each, which is based on the sub-carrier to interference ratio (ScIR) of such sub-carrier as estimated from eigenvalue decomposition. A full system model is presented in this paper and simulations show that the precoding of SEFDM results in either better bit error rate (BER) performance compared to that of an orthogonal frequency division multiplexing (OFDM) system of the same spectral efficiency or in higher effective bit rate relative to an OFDM system with the same BER performance. Modelling is done in simple Gaussian noise channels and in a static frequency selective channel and for different modulation formats. Results show that for the same bandwidth a 128QAM precoded SEFDM system outperforms a 16QAM OFDM one by offering 75% bit rate increase. Furthermore, Turbo coding assisted BER performance comparisons are investigated in this work. Using 64QAM modulated symbols, the precoded SEFDM outperforms the typical OFDM by several dBs.

A non-orthogonal multicarrier signal, termed spectrally efficient frequency division multiplexing (SEFDM), is investigated in this work. It improves spectral efficiency by compressing sub-carrier spacing below the symbol rate at the cost of self-created inter carrier interference (ICI). Sphere decoding (SD) is an efficient method to recover signals approaching maximum likelihood (ML) performance. However, the complexity of the SD approaches that of ML with the increase of system size. Studies in this work show that for a small number of sub-carriers, SEFDM signals with low order modulation formats outperform spectral efficiency equivalent OFDM signals modulated by higher order modulation symbols. A key achievement is that 16QAM SEFDM signal outperforms 64QAM OFDM signal at high Eb/N0of the same spectral efficiency. In order to maintain the performance benefit and reduce the complexity of SD for large size SEFDM signals, multi-sphere decoding for a multi-block architecture is applied. For a large number of sub-carriers, the multi-sphere architecture works well for SEFDM signals modulated by 4QAM symbols. Whilst for 16QAM symbols, the performance is related to the number of sub-carriers. This work offers an efficient detection solution for SEFDM signals and reveals challenges. It paves the way for future study of signal detection of large size interfered multicarrier signals.

We comprehensively summarize experimental validations 1 of bandwidth compressed multicarrier waveforms for future 5th generation (5G) applications. The proposed waveforms are derived from an existing non-orthogonal multicarrier concept termed spectrally efficient frequency division multiplexing (SEFDM) where sub-carriers are non-orthogonally packed at frequencies below the symbol rate. This improves the spectral efficiency at the cost of self-created inter carrier interference (ICI). In this work, experiments are reported and testing is carried out in three scenarios including long term evolution (LTE)-like wireless link; millimeter wave radio-over-fiber (RoF) link and optical fiber link. In the first scenario, for a given 25 MHz bandwidth, the SEFDM testbed can provide 70 Mbit/s gross data rate while only 50 Mbit/s can be achieved for an OFDM system occupying the same bandwidth. For the millimeter wave experiment, occupying a 1.125 GHz bandwidth, the gross bit rate for OFDM is 2.25 Gbit/s and with 40% bandwidth compression, 3.75 Gbit/s can be achieved for SEFDM. Two experimental optical fiber links are described in this work; a 10 Gbit/s direct detection optical SEFDM system and a 24 Gbit/s coherent detection SEFDM system. The LTE-like signals and millimeter wave technologies are well suited to provide last mile communications to end users as both can support mobility in wireless environments. The lightwave signals delivered by optical fibers would offer higher data rates and support long-haul communications. The reported techniques, used individually or combined, would be of interest to future wireless system designers, where bandwidth saving is of importance, such as in 5G networks, aiming to provide high capacity and high mobility, simultaneously while saving spectrum.

A newly proposed optical-spectrally efficient frequency division multiplexing (O-SEFDM) system reduces the required communication spectrum by employing non-orthogonal and overlapping sub-carriers. This results in higher spectral efficiency relative to an equivalent optical-orthogonal frequency division multiplexing (O-OFDM) delivering the same data rate. O-SEFDM technique can save spectrums in both the electrical and optical domains. However, due to the loss of orthogonality, detection of O-SEFDM signals becomes more complicated. In this work, we employ a hybrid soft Iterative Detection (ID) together with fixed sphere decoder (FSD), concurrently optimizing performance and complexity. We show that for Bandwidth Compression Factor (BCF) of up to 25 percent, we can achieve the same performance as O-OFDM. This verifies Mazo's rates of transmission 25 percent faster than the Nyquist rate. We report a 4QAM system occupying approximately the same bandwidth as that of an 8QAM with 1.6 dB improved error performance for the same transmission rate. The same system shows only minor power penalty (1 dB) relative to its 4QAM OFDM bit rate equivalent but with the advantage of 30% bandwidth saving. This study reports1optical and electrical bandwidth saving with minor error performance degradation and paves the way for practical.