In this paper, we propose a Reference Signal (RS) framework potentially for next generation wireless systems, which targets to systematical RS construction with nonorthogonal Space-Time Code (STC) designs. In particular, we embed parametrized non-orthogonal STCs into the RS design, by exploiting the properties of so-called ABBA STCs or its diagonal variants, where the diagonality is imposed by use of appropriate precoding and postcoding. The transformations of the ABBA coding vectors can be resolved at the receiver. The transformed matrices are used to enhance channel estimation quality with the aid of inherent power gain, self-interference mitigation, and diversity gain. The proposed framework can be flexibly integrated to existing RS sequences, defined in 5G New Radio (NR). In effect, this leads to parameterizable code space and controllable self-interference. The proposed RS construction can be easily extended to support$2^{M} \times 2^{M}$STC systems for$M \geq 2$, and can be used to track the channel response with up to$2^{M}$spatial links in parallel.

The large untapped spectrum in sub-THz allows for ultra-high throughput communication to realize many seemingly impossible applications in 6G. Phase noise (PN) is one key hardware impairment, which is accentuated as we increase the frequency and bandwidth. Furthermore, the modest output power of the power amplifier demands limits on peak to average power ratio (PAPR) signal design. In this work, we design a PN-robust, low PAPR single-carrier (SC) waveform by geometrically shaping the constellation and adapting the pulse shaping filter pair under practical PN modelling and adjacent channel leakage ratio (ACLR) constraints for a given excess bandwidth. We optimize the waveforms under conventional and state-of-the-art PN-aware demappers. Moreover, we introduce a neural-network (NN) demapper enhancing transceiver adaptability.We formulate the waveform optimization problem in its augmented Lagrangian form and use a back-propagation-inspired technique to obtain a design that is numerically robust to PN, while adhering to PAPR and ACLR constraints. The results substantiate the efficacy of the method, yielding up to 2.5 dB in the requiredEb/N0under stronger PN along with a PAPR reduction of 0.5 dB. Moreover, PAPR reductions up to 1.2 dB are possible with competitive BLER and SE performance in both low and high PN conditions.

The large untapped spectrum in the sub-THz allows for ultra-high throughput communication to realize many seemingly impossible applications in 6G. One of the challenges in radio communications in sub-THz is the hardware impairments. Specifically, phase noise is one key hardware impairment, which is accentuated as we increase the frequency and bandwidth. Furthermore, the moderate output power of the sub-THz power amplifier demands limits on peak to average power ratio (PAPR) signal design. Single carrier frequency domain equalization (SC-FDE) has been identified as a suitable candidate for sub-THz, although some challenges such as phase noise and PAPR still remain to be tackled. In this work, we design a phase noise robust, modest PAPR SC waveform by geometrically shaping the constellation under practical conditions. We formulate the waveform optimization problem in its augmented Lagrangian form and use a back-propagation-inspired technique to obtain a constellation design that is numerically robust to phase noise, while maintaining a relatively low PAPR compared to the conventional waveforms.

Turbo-AI is an iterative Machine Learning (ML) based channel estimator, processed through frequency, time and spatial domains consecutively with low complexity. Within the context of 5th Generation (5G) and Beyond 5G (B5G) wireless communications, we embed Turbo-AI into Demodulation Reference Signal (DMRS) processing in this paper. The extension is not technically obvious, comparing to our previous Turbo-AI paper series, because the main challenges come from not only estimating the channel response exactly for the DMRS pilot resource elements, but also precisely interpolating the channel response for the data resource elements, especially when the user mobility or equivalently the sparsity of the pilot structure significantly increases. Throughout this paper, we propose a universal interpolation approach, named Firecracker Algorithm, which is jointly introduced to DMRS-Turbo-AI. Numerical results show that DMRS-Turbo-AI can deliver high quality channel estimation for the users with high mobility, and demonstrate that Firecracker Algorithm is even capable of tracking the channel of a user, who moves at a supersonic speed.

Targeting to potential evolution of future wireless systems, precise channel estimation is regarded as one of the fundamental prerequisites. In this paper, we focus on Machine Learning (ML) based channel estimation with Turbo-AI, which is an iterative training approach, and can monotonically reduce the post-processing noise variance of Gaussian inputs after each iteration, by updating the Neural Network (NN) models with re-training. After discussing about the initial results of Turbo-AI as Part I in our introductory paper, we will now deploy the same principle in a multicarrier system from a more practical view point. Multi-dimensional Turbo-AI will estimate the channel in an iterative manner through frequency, time and spatial domain cooperatively. Since the complexity of traditional channel estimation method will be extremely high due to the multi-dimensional data structure, Turbo-AI can be regarded as a complementary solution to balance the performance and complexity. Throughout this paper, we exploit 5G compliant link level simulator to show that genie-aided upper bound can be approached by Turbo-AI. Challenges of realizing Turbo-AI based channel estimation in practical systems for future wireless communication towards Beyond 5G (B5G) are focused on.

Channel estimation belongs to one of the potential applications, that can exploit Artificial Intelligence (AI) and Machine Learning (ML) to enhance Physical Layer (PHY) performance in the context of 5th Generation (5G) and Beyond 5G (B5G) wireless communication systems. In this paper, we focus on the ML-based channel estimation for 2-Dimensional (2D) antenna arrays. Due to the extremely high computational requirement for 2D massive arrays with Conventional Training, we exploit the 2D Kronecker covariance model to perform Subspace Training for the vertical and horizontal spatial domains independently, which achieves a complexity cost saving factor O(M4N4)/O(MN4+ NM4) for an M × N 2D-array. Furthermore, we propose an iterative training approach, referred to as Turbo-AI. Along with Subspace Training, the new approach can monotonically reduce the effective variance of additive noise of the observation, by updating the Neural Network (NN) models with re-training. Furthermore, we propose a concept, named Universal Training. It allows to use one NN for a wide range of Signal-to-Noise-Ratio (SNR) operation points and spatial angles, which can greatly simplify Turbo-AI usage. Numerical results exhibit that Turbo-AI can tightly approach the genie-aided channel estimation bound, especially at low SNR.

Multi-User Multiple-Input Multiple-Output (MU-MIMO) and Non-Orthogonal Multiple Access (NOMA) belong to the key physical layer technologies under the context of 5th Generation (5G) New Radio (NR) communication. Both MU- MIMO and NOMA exhibit capability to significantly enhance the spectral efficiency from different aspects. In this paper, we investigate and compare MU-MIMO and NOMA in link level with respect to some practical issues, e.g. imperfect channel estimation, imperfect sectorization. Numerical results reveal that the coexistence of MU-MIMO and NOMA provide the degrees of freedom to allow the system to selectively focus on spectral efficiency per user or the number of supported users, and effectively be associated with scheduled traffic and contention-based traffic, respectively.

Vehicular networking belongs to one of the most attractive techniques under the context of 5th Generation (5G) and Beyond 5G (B5G) wireless communication system. It opens diverse new concepts and applications, e.g. Vehicular-to-Vehicular (V2V), Vehicular-to-Infrastructure (V2I). One of the key components to support autonomous driving is to allow vehicles to share information about their awareness of the environment, i.e. perception layer of neighbor vehicles. In this paper, we focus on low overhead, low latency MAC layer design, enabling perception layer sharing in V2V environment, and PHY layer design, enabling Multi-User Detection (MUD) with V2V environment by means of Non-Orthogonal Multiple Access (NOMA), which is one of the key physical layer technologies of 5G New Radio (NR). Numerical results of link level simulation exhibit that our solution generates and exchanges the NOMA user signature flexibly, i.e. supports both synchronous and asynchronous traffic with low bit error rate. Furthermore, it enables small packet communication with low computational complexity, thus low latency.

In this paper, let's exhibit the exploitation of Gaussian Approximation (GA) based detection algorithm for Non-Orthogonal Coded Access (NOCA). NOCA belongs to one candidate solution of Non-Orthogonal Multiple Access (NOMA), recently discussed for future wireless communications towards 5th Generation (5G) New Radio (NR) standard. It is well known that GA based detection algorithm was very elegantly introduced in Interleave Division Multiple Access (IDMA). As being an iterative detection approach, the computational requirement belongs to one of the major focuses of GA based detection algorithms. Thus, in this paper, the efficiency in sense of complexity, suitable use cases of GA based detection will be investigated with respect to NOCA and IDMA by link level simulations. Numerical results reveals that both IDMA and NOCA are suitable for sporadic traffic. Especially, NOCA exhibits high detection efficiency for earlier iterations, and IDMA exhibits the capability of overloading and performance convergence under very low Signal-to-Noise Ratio (SNR).

In this paper, a wireless cellular network based indoor localization approach is presented. After receiving the positioning request, initialized from a mobile device during the training stage, the surrounding Base Stations (BSs), which are typically equipped with massive antenna array elements, can measure the Angle of Arrival (AoA) of the positioning request signal. Being unsimilar to the conventional AoA localization approaches, the BSs will not communicate with each other and localize the mobile device cooperatively. Instead, each BS simply encodes its own longitude, latitude and AoA information within a 80-bit small packet. Further, before delivering the data to the mobile device, downlink Interleave Division Multiple Access (IDMA) is exploited. Hence, during the measurement stage, the mobile device receives the superposition of the data packet from multiple BSs. Downlink IDMA manages to detect the superimposed data robustly even under the asynchronous environment. Numerical results exhibit that 90% of the realizations can yield a positioning error smaller than one meter, under very low Signal-to-Noise Ratio (SNR).

In this paper, we present a low-cost receive algorithm for massive Multiple-Input Multiple-Output (MIMO) system, which exploits 2-dimensional superimposed pilots in time and frequency domain. By introducing the well designed superimposed pilots, the pilot contamination can be systematically suppressed. Even within a small time-frequency allocation, transforming the 2-dimensional superimposed pilots to an equivalent one-dimensional orthogonal pattern, the provided processing gain can be adequate to achieve precise channel estimation on the basis of linear operations. In this paper, we provide the proof-of-concept with link layer simulation for multicarrier system. Additionally, the 2-dimensional trellis-based channel estimation, as a high-cost solution, will be compared to the proposed solution, in order to quantify the reasonability of the trade-off.

In this paper, we propose an efficient data exchange mechanism with a dedicated detection approach for Device-to-Device (D2D) communications. By involving in a third device as Relay Node (RN), both devices of interest can communicate with each other. Within an inband relay mode, Interleave Division Multiple Access (IDMA) is exploited to enable the third device to detect and decode the data of both devices. Afterwards, the third device broadcasts the superposition of both signals. In order to pick out the useful signal component out of the superimposed signals, each device deploys trellis-based iterative joint data detection and channel estimation. Especially, the iterative processing enables the successful D2D communications under very low Signal-to-Noise Ratio (SNR), which can significantly mitigate the interference for the Long Term Evolution (LTE) and LTE-Advanced network.

As being a complementary technique for Machine Type Communications (MTC) in future 5th Generation (5G) wireless communications, the control mechanism and control signaling should preferably be able to operate at possibly low Signal-to-Noise Ratio (SNR) without interfering the existing cellular network. In this paper, we propose an enhanced mechanism for the control signaling, targeting to low-power communications. It enables the detection of sporadically transmitted data packet of a user at relatively low SNR. Further, we focus on the low-power channel estimation and data detection as an additional proof-of-concept within a multicarrier system. The superimposed pilots are exploited 2-dimensionally in time and frequency to jointly detect the data and estimate the channel.

In this paper, we investigate channel estimation for the massive Multiple-Input Multiple-Output (MIMO) systems, by deploying superimposed pilots. The pilots are the a priori known spreading sequences, repeated cyclically and superimposed to the signal layer. They reserve a marginal fraction of the total transmit energy. Thus, the initial channel estimates can be initially obtained by separating the users with respect to the corresponding code-division orthogonality with robustness against asynchronicity. Further, during a second stage, iterative channel estimation is considered, by integrating a trellis-based channel estimator, which can significantly enhance the channel estimation during the iterations, by reusing the superimposed pilots. Notice that the so-called turbo-effect can be clearly observed, in which both data detection and channel estimation are jointly improved between consecutive iterations.

In this paper, we compare two potential multiple access schemes, namely Non-Orthogonal Multiple Access (NOMA) and Interleave Division Multiple Access (IDMA) in the case of equal rate communication without link adaptation. Especially, we focus on their performance in uplink with a real channel estimator, by exploiting superimposed pilots. The utilized superimposed pilots are the cyclically repeated spreading sequence, a priori known by the transmitter and receiver, and are assigned a marginal fraction of the total transmit power. Exploiting the orthogonality of codes, the superimposed pilots help to generate an initial channel estimation. Furthermore, iterative channel estimation is considered by introducing a trellis-based channel estimator, which can significantly enhance the channel estimation during the iterations by reusing the superimposed pilots. The behaviors of Bit Error Rate (BER) and channel estimation Mean Square Error (MSE) will be investigated for NOMA and IDMA system.

In this paper, we investigate secure communications by introducing superimposed pilots for multiple antenna system. The superimposed pilots enable the trellis-based joint channel tracking and data detection for the user of interest. Further, by adjusting the power ratio between the data symbol and superimposed pilot symbol, the secure capacity region can be established. The user of interest can appropriately select the Forward Error Correction (FEC) code rate, to prevent any possible eavesdropping. In this paper, we present the achievable secure capacity region for multiple antenna system, and verify it via Monte Carlo simulation as well.

In this paper we investigate multiple access schemes and multi-carrier waveforms in the context of future 5th Generation (5G) wireless communication systems. We compare classical Frequency Division Multiple Access (FDMA) to Interleave-Division Multiple Access (IDMA) on top of two different multicarrier waveforms: Orthogonal Frequency Division Multiplexing (OFDM) and a new approach called Universal Filtered Multi-Carrier (UFMC). A relaxation of timing and frequency alignment requirements is taken into account for supporting applications like Machine Type Communications (MTC) and the Internet of Things (IoT). This paper contains a first uplink comparison scenario where traffic with Relaxed Synchronicity (RS) is embedded into synchronous traffic. Two main users of interest are either using IDMA or FDMA on top of either OFDM or UFMC modulation. Simulation results give first suitability indications for 5G for the combination of waveform and multiple access scheme. The numerical results reveal that IDMA brings in significant enhancement for low rate users, and UFMC introduces additional protection to high-rate users. Both schemes can be combined well.

In this paper we compare three candidate multicarrier waveforms for the air interface of 5G: filtered CP-OFDM - the choice for 4G, FBMC - heavily discussed in recent years, and Universal Filtered Multi-Carrier (UFMC) - a new contender making its appearance recently. We judge their time-frequency efficiency when transmitting very small bursts (e.g. for machine to machine communications) and under very tight response time requirements (e.g. for vehicle to vehicle communications). While FBMC is very efficient when transmitting long sequences, it suffers when having to transmit short bursts/frames. Due to the cyclic prefix and wide frequency guards, OFDM is rather inefficient. UFMC proofs to be the best choice, here, outperforming OFDM by about 10% in any case and FBMC in case of very short packets while performing similar for long sequences.

In order to reduce pilot overhead for Multiple-Input Multiple-Output (MIMO) communications, we present a trellis-based joint channel tracking and data detection approach that allows reliable communications at fast fading rates. The trellis tracking algorithm predicts multiple channel coefficients in parallel while simultaneously performing a posteriori Probability (APP) detection of the transmitted bits. Different variants are compared based on different pilot training strategies, using discrete pilots, superimposed pilots, or no pilots at all by employing Differential Phase Shift Keying (DPSK) across multiple antenna channels.

In this paper, we present a downlink multiuser Multiple-Input Multiple-Output (MIMO) joint detection scheme with secure physical layer. Within a data block, an arbitrary user's Quadrature Amplitude Modulation (QAM) modulated signal is protected by the pseudo-randomly defined scrambling codes, in order to prevent the other users from eavesdropping the whole Forward Error Correction (FEC) block. We can adjust the density of scrambling codes in the data block, to balance the trade-off between the improved physical layer security and the slight loss of sum rate. Finally, we analyze the Bit Error Ratio (BER) performance and provide the corresponding union bound. We also present the achievable rate of the proposed scheme by Monte Carlo simulation.

A low-complexity detection algorithm is proposed for Multiple-Input Multiple-Output (MIMO) systems with many transmit and receive antennas. We involve QR Decomposition (QRD) to triangularize the effective MIMO channel matrix so that several MIMO sub-systems can be individually established, which allow detecting several (possibly overlapping) groups of data streams separately. This method is referred to as MIMO Overlapped Subspace Detection (OSD). Further, we propose an OSD based non-iterative (i.e., without feedback from an outer channel decoder) Interference Cancellation (IC) approach where some streams are cancelled out as reliable interference. Link layer simulations for an 8×8 configuration show that this algorithm not only inherits the feature of OSD, yielding a scalable performance/complexity trade-off, but can also closely approach the MIMO ergodic capacity.

This paper presents a low complexity detection algorithm for Multiple-Input Multiple-Output (MIMO) systems. The novel subspace approach involves QR Decomposition (QRD) or Cholesky decomposition to triangularize the effective channel matrix so that several (possibly overlapping) groups of data streams can be detected separately. Link layer simulations show that the ergodic MIMO capacity can be closely approached by exploiting an Overlapped Subspace Detection (OSD) strategy. The OSD algorithm offers a scalable performance/complexity trade-off between Zero-Forcing (ZF) and Maximum a posteriori Probability (APP) detection. The proposed algorithm can be straightforwardly applied to large MIMO systems, as prevalent with recent advances in the field such as network MIMO and Coordinated Multi-Point (CoMP) transmission and reception.

As being a promising technique to allow the coexistence of independent wireless sub-systems and to improve the spectral efficiency, Decentralized Dynamic Spectrum Allocation (DDSA) within a Cognitive Radio (CR) system is recently intensively investigated. It is well known that the major challenges come from the decentralization of wireless subsystems, wireless channel monitoring and spectrum sensing. This paper presents three DDSA algorithms from the viewpoint of physical layer, by exploiting the Virtual Antenna Array (VAA) for the uplink transmission in a horizontal CR system. System level simulator is deployed to evaluate the DDSA algorithms.

The recent research interest moves to cellular network with small cell deployment to reach high spectral efficiency, regarding the concepts as femtocell, Network MIMO, relay network, etc. Frequency planning becomes more important to support these technical advances. In this paper, a novel frequency planning scheme is presented. It allows exploiting the joint transmission in downlink, and enables the receiver to deploy interference cancellation. Throughout the investigation, a multilink simulator is developed to evaluate the basic performance for the isolated cellular setup. Finally, a simplified system simulator is taken into account to assess the system performance.

This paper presents an efficient multiuser Multiple Input Multiple Output (MU-MIMO) detection algorithm. It introduces Cholesky Decomposition (CLD) and deploys Successive Interference Cancellation (SIC) detection. This study reveals that the proposed algorithm delivers equivalent performance to the QR Decomposition (QRD) based detection algorithm, with adequately lower complexity. As well, the CLD based detection algorithm can be straightforwardly adapted to other QRD based algorithm variants to reduce the computational effort.

This paper presents a low-complexity multiuser MIMO detection algorithm. The MIMO receiver exploits Radial space-Division Multiple Access (RDMA) principle, by paring and scheduling the MIMO multiusers with distinguishable radial space-divisions to the receiver. By exploiting the Channel State Information (CSI) and eigenvalue analysis, the MIMO receiver can selectively deploy the Linear Minimum Mean Square Error (L-MMSE) detection or Successive Interference Cancelation (SIC) for the MIMO multiusers. This reduces the complexity for the multiuser detection, by possibly maintaining the performance. The investigation is provided for the Orthogonal Frequency Division Multiplex (OFDM) system. Throughout this paper, an IEEE 802.16e, Worldwide Interoperability Microwave Access (WiMAX), link level simulator is exploited for the uplink performance evaluation.

Worldwide Interoperability Microwave Access (WiMAX) is an important member of the IEEE 802.16 family. It offers solutions for Beyond-3G and future 4G systems. The deployment of Adaptive Antenna Systems (AAS) enables beamforming and Multiple-Input Multiple-Output (MIMO) techniques. In this Paper, mechanisms are investigated for switching between single user beamforming and multiuser MIMO within the WiMAX Time Division Duplex (TDD) mode, with respect to eigenvalue analysis. Throughout this paper, an IEEE 802.16e link level simulator is exploited for the performance evaluation.

This paper deals with the high speed downlink packet access (HSDPA) in UMTS/FDD system. The transmit diversity and beamforming techniques are focused on, namely space time transmit diversity (STTD), closed loop mode 1 transmit diversity, conventional beamforming and closed loop beamforming with 2 transmit antennas. The link level system performance of the various methods is investigated and compared for the fading environment. At low user speeds, closed loop transmit diversity turns out to be superior. STTD has the lowest system complexity and achieves an acceptable performance at high SNR. Both beamforming schemes are the good solutions for high speed mobile terminal at low SNR.