It is shown that, particularly for terrestrial cellular telephony, the interference-suppression feature of CDMA (code division multiple access) can result in a many-fold increase in capacity over analog and even over competing digital techniques. A single-cell system, such as a hubbed satellite network, is addressed, and the basic expression for capacity is developed. The corresponding expressions for a multiple-cell system are derived. and the distribution on the number of users supportable per cell is determined. It is concluded that properly augmented and power-controlled multiple-cell CDMA promises a quantum increase in current cellular capacity. >

On the capacity of a cellular CDMA system

Sphere Packings, Lattices and Groups

The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

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Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

The emerging massive/large-scale multiple-input multiple-output (LS-MIMO) systems that rely on very large antenna arrays have become a hot topic of wireless communications. Compared to multi-antenna aided systems being built at the time of this writing, such as the long-term evolution (LTE) based fourth generation (4G) mobile communication system which allows for up to eight antenna elements at the base station (BS), the LS-MIMO system entails an unprecedented number of antennas, say 100 or more, at the BS. The huge leap in the number of BS antennas opens the door to a new research field in communication theory, propagation and electronics, where random matrix theory begins to play a dominant role. Interestingly, LS-MIMOs also constitute a perfect example of one of the key philosophical principles of the Hegelian Dialectics, namely, that “quantitative change leads to qualitative change.” In this treatise, we provide a recital on the historic heritages and novel challenges facing LS-MIMOs from a detection perspective. First, we highlight the fundamentals of MIMO detection, including the nature of co-channel interference (CCI), the generality of the MIMO detection problem, the received signal models of both linear memoryless MIMO channels and dispersive MIMO channels exhibiting memory, as well as the complex-valued versus real-valued MIMO system models. Then, an extensive review of the representative MIMO detection methods conceived during the past 50 years (1965–2015) is presented, and relevant insights as well as lessons are inferred for the sake of designing complexity-scalable MIMO detection algorithms that are potentially applicable to LS-MIMO systems. Furthermore, we divide the LS-MIMO systems into two types, and elaborate on the distinct detection strategies suitable for each of them. The type-I LS-MIMO corresponds to the case where the number of active users is much smaller than the number of BS antennas, which is currently the mainstream definition of LS-MIMO. The type-II LS-MIMO corresponds to the case where the number of active users is comparable to the number of BS antennas. Finally, we discuss the applicability of existing MIMO detection algorithms in LS-MIMO systems, and review some of the recent advances in LS-MIMO detection.

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Fifty Years of MIMO Detection: The Road to Large-Scale MIMOs

A new class of low-complexity, yet energy-efficient Multiple-Input Multiple-Output (MIMO) transmission techniques, namely, the family of Spatial Modulation (SM) aided MIMOs (SM-MIMO), has emerged. These systems are capable of exploiting the spatial dimensions (i.e., the antenna indices) as an additional dimension invoked for transmitting information, apart from the traditional Amplitude and Phase Modulation (APM). SM is capable of efficiently operating in diverse MIMO configurations in the context of future communication systems. It constitutes a promising transmission candidate for large-scale MIMO design and for the indoor optical wireless communication while relying on a single-Radio Frequency (RF) chain. Moreover, SM may be also viewed as an entirely new hybrid modulation scheme, which is still in its infancy. This paper aims for providing a general survey of the SM design framework as well as of its intrinsic limits. In particular, we focus our attention on the associated transceiver design, on spatial constellation optimization, on link adaptation techniques, on distributed/cooperative protocol design issues, and on their meritorious variants.

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Design Guidelines for Spatial Modulation

About the Authors. OtherWiley IEEE Press Books on Related Topics. Preface. Acknowledgments. 1 Problem Formulation, Objectives and Benefits. 1.1 TheWireless Channel and the Concept of Diversity. 1.2 Diversity and Multiplexing Trade-offs in Multi-functional MIMO Systems. 1.3 Coherent versus Non-coherent Detection for STBCs Using Co-located and Cooperative Antenna Elements. 1.4 Historical Perspective and State-of-the-Art Contributions. 1.5 Iterative Detection Schemes and their Convergence Analysis. 1.6 Outline and Novel Aspects of the Monograph. Part I Coherent Versus Differential Turbo Detection of Sphere-packing-aided Single-user MIMO Systems. List of Symbols in Part I. 2 Space-Time Block Code Design using Sphere Packing. 2.1 Introduction. 2.2 Design Criteria for Space-Time Signals. 2.3 Design Criteria for Time-correlated Fading Channels. 2.4 Orthogonal Space-Time Code Design using SP. 2.5 STBC-SP Performance. 2.6 Chapter Conclusions. 2.7 Chapter Summary. 3 Turbo Detection of Channel-coded STBC-SP Schemes. 3.1 Introduction. 3.2 System Overview. 3.3 Iterative Demapping. 3.4 Binary EXIT Chart Analysis. 3.5 Performance of Turbo-detected Bit-based STBC-SP Schemes. 3.6 Chapter Conclusions. 3.7 Chapter Summary. 4 Turbo Detection of Channel-coded DSTBC-SP Schemes. 4.1 Introduction. 4.2 Differential STBC using SP Modulation. 4.3 Bit-based RSC-coded Turbo-detected DSTBC-SP Scheme. 4.4 Chapter Conclusions. 4.5 Chapter Summary. 5 Three-stage Turbo-detected STBC-SP Schemes. 5.1 Introduction. 5.2 System Overview. 5.3 EXIT Chart Analysis. 5.4 Maximum Achievable Bandwidth Efficiency. 5.5 Performance of Three-stageTurbo-detected STBC-SP Schemes. 5.6 Chapter Conclusions. 5.7 Chapter Summary. 6 Symbol-based Channel-coded STBC-SP Schemes. 6.1 Introduction. 6.2 System Overview. 6.3 Symbol-based Iterative Decoding. 6.4 Non-binary EXIT Chart Analysis. 6.5 Performance of Bit-based and Symbol-based LDPC-coded STBC-SP Schemes. 6.6 Chapter Conclusions. 6.7 Chapter Summary. Part II Coherent Versus Differential Turbo Detection of Single-user and Cooperative MIMOs. List of Symbols in Part II. 7 Linear Dispersion Codes: An EXIT Chart Perspective. 7.1 Introduction and Outline. 7.2 Linear Dispersion Codes. 7.3 Link Between STBCs and LDCs. 7.4 EXIT-chart-based Design of LDCs. 7.5 EXIT-chart-based Design of IR-PLDCs. 7.6 Conclusion. 8 Differential Space-Time Block Codes: A Universal Approach. 8.1 Introduction and Outline. 8.2 System Model. 8.3 DOSTBCs. 8.4 DLDCs. 8.5 RSC-coded Precoder-aided DOSTBCs. 8.6 IRCC-coded Precoder-aided DLDCs. 8.7 Conclusion. 9 Cooperative Space-Time Block Codes. 9.1 Introduction and Outline. 9.2 Twin-layer CLDCs. 9.3 IRCC-coded Precoder-aided CLDCs. 9.4 Conclusion. Part III Differential Turbo Detection of Multi-functional MIMO-aided Multi-user and Cooperative Systems. List of Symbols in Part III. 10 Differential Space-Time Spreading. 10.1 Introduction. 10.2 DPSK. 10.3 DSTS Designusing Two Transmit Antennas. 10.4 DSTS Design Using Four Transmit Antennas. 10.5 Chapter Conclusions. 10.6 Chapter Summary. 11 Iterative Detection of Channel-coded DSTS Schemes. 11.1 Introduction. 11.2 Iterative Detection of RSC-coded DSTS Schemes. 11.3 Iterative Detection of RSC-coded and Unity-rate Precoded Four-antenna-aided DSTS-SP System. 11.4 Chapter Conclusions. 11.5 Chapter Summary. 12 Adaptive DSTS-assisted Iteratively Detected SP Modulation. 12.1 Introduction. 12.2 System Overview. 12.3 Adaptive DSTS-assisted SP Modulation. 12.4 VSF-based Adaptive Rate DSTS. 12.5 Variable-code-rate Iteratively Detected DSTS-SP System. 12.6 Results and Discussion. 12.7 Chapter Conclusion and Summary. 13 Layered Steered Space-Time Codes. 13.1 Introduction. 13.2 LSSTCs. 13.3 Capacity of LSSTCs. 13.4 Iterative Detection and EXIT Chart Analysis. 13.5 Results and Discussion. 13.6 Chapter Conclusions. 13.7 Chapter Summary. 14 DL LSSTS-aided Generalized MC DS-CDMA. 14.1 Introduction. 14.2 LSSTS-aided Generalized MCDS-CDMA. 14.3 Increasing the Number of Users by Employing TD and FD Spreading. 14.4 Iterative Detection and EXIT Chart Analysis. 14.5 Results and Discussion. 14.6 Chapter Conclusions. 14.7 Chapter Summary. 15 Distributed Turbo Coding. 15.1 Introduction. 15.2 Background of Cooperative Communications. 15.3 DTC. 15.4 Results and Discussion. 15.5 Chapter Conclusions. 15.6 Chapter Summary. 16 Conclusions and Future Research. 16.1 Summary and Conclusions. 16.2 Future Research Ideas. 16.3 Closing Remarks. A Gray Mapping and AGM Schemes for SP Modulation of Size L =16. B EXIT Charts of Various Bit-based Turbo-detected STBC-SP Schemes. C EXIT Charts of Various Bit-based Turbo-detected DSTBC-SP Schemes. D LDCs' / for QPSK Modulation. E DLDCs' / for 2PAM Modulation. F CLDCs' / 1 and / 2 for BPSK Modulation. G Weighting Coefficient Vectors e and a. H Gray Mapping and AGM Schemes for SP Modulation of Size L =16. Glossary. Bibliography. Index. Author Index.

Near-Capacity Multi-Functional MIMO Systems: Sphere-Packing, Iterative Detection and Cooperation

Deep learning has been applied in physical-layer communications systems in recent years and has demonstrated fascinating results that were comparable or even better than human expert systems. In this paper, a novel convolutional neural networks (CNNs)-based autoencoder communication system is proposed, which can work intelligently with arbitrary block length, can support different throughput and can operate under AWGN and Rayleigh fading channels as well as deviations from AWGN environments. The proposed generalized communication system is comprised of carefully designed convolutional neural layers and, hence, inherits CNN’s breakthrough characteristics, such as generalization, feature learning, classification, and fast training convergence. On the other hand, the end-to-end architecture jointly performs the tasks of encoding/decoding and modulation/demodulation. Finally, we provide the numerous simulation results of the learned system in order to illustrate its generalization capability under various system conditions.

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A CNN-Based End-to-End Learning Framework Toward Intelligent Communication Systems

This article proposes a novel orthogonal frequency-division multiplexing (OFDM) autoencoder featuring convolutional neural networks (CNNs)-based channel estimation for marine communications with complex and fast-changing environments. We demonstrate that the proposed OFDM autoencoder system can be generalized to work under various channel environments, different throughputs, while outperforming the traditional OFDM counterparts, especially when working at high throughputs. In addition, since OFDM systems require accurate channel estimations to function properly, this treatise also proposes a new channel estimation algorithm for OFDM systems that combine the power of deep learning (DL) with the philosophy of super-resolution reconstruction, which uses dense convolutional neural networks (Dense-Nets) to reconstruct low-resolution pilot information images into high-resolution full-channel impulse responses (CIRs). The Dense-Net structure has the characteristics of dense connections and feature multiplexing. The simulation results show that under slow fading, the proposed channel estimator (CE) can estimate the CIRs perfectly. Under fast fading, the proposed CE outperforms the existing learning-based algorithms with fewer neural network parameters. Therefore, the proposed novel autoencoder scheme and the powerful CE are potentially attractive approaches for the Internet of Vessels (IoV).

A Novel OFDM Autoencoder Featuring CNN-Based Channel Estimation for Internet of Vessels

In this treatise, we propose a novel family of asynchronous cooperative linear dispersion codes (ACLDCs), which is capable of maintaining full diversity in cooperative scenarios, even in the presence of asynchronous reception. The linear dispersion structure is employed to accommodate the dynamic topology of cooperative networks, as well as to achieve higher throughput than conventional space-time codes based on orthogonal designs. By introducing guard intervals and block encoding/decoding techniques, the interference signals caused by asynchronous reception can be exploited rather than discarded.

Asynchronous Cooperative MIMO Systems Using a Linear Dispersion Structure

In this paper, we propose novel serial concatenated irregular convolutional-coded (IRCC) irregular precoded linear dispersion codes (IR-PLDCs), which are capable of operating near the multiple-input-multiple output (MIMO) channel's capacity The irregular structure facilitates the proposed system's near-capacity operation across a wide range of SNRs, while maintaining a vanishing bit error ratio (BER) Each coding block of the proposed scheme and all the iterative decoding parameters are designed with near-capacity operation in mind, using extrinsic information transfer charts By applying the irregular design principle to both the inner and outer codes, the proposed IRCC-aided IR-PLDC scheme becomes capable of operating as close as 09 dB to the MIMO channel's capacity for SNRs in excess of a certain threshold

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Nan Wu

Papers

Near-Capacity Multi-Functional MIMO Systems: Sphere-Packing, Iterative Detection and Cooperation

A CNN-Based End-to-End Learning Framework Toward Intelligent Communication Systems

A Novel OFDM Autoencoder Featuring CNN-Based Channel Estimation for Internet of Vessels

Asynchronous Cooperative MIMO Systems Using a Linear Dispersion Structure

Near-Capacity Irregular-Convolutional-Coding-Aided Irregular Precoded Linear Dispersion Codes