In the fifth-generation (5G) mobile communication system, various service requirements of different communication environments are expected to be satisfied. As a new evolution network structure, heterogeneous network (HetNet) has been studied in recent years. Compared with homogeneous networks, HetNets can increase the opportunity in the spatial resource reuse and improve users’ quality of service by developing small cells into the coverage of macrocells. Since there is mutual interference among different users and the limited spectrum resource in HetNets, however, efficient resource allocation (RA) algorithms are vitally important to reduce the mutual interference and achieve spectrum sharing. In this article, we provide a comprehensive survey on RA in HetNets for 5G communications. Specifically, we first introduce the definition and different network scenarios of HetNets. Second, RA models are discussed. Then, we present a classification to analyze current RA algorithms for the existing works. Finally, some challenging issues and future research trends are discussed. Accordingly, we provide two potential structures for 6G communications to solve the RA problems of the next-generation HetNets, such as a learning-based RA structure and a control-based RA structure. The goal of this article is to provide important information on HetNets, which could be used to guide the development of more efficient techniques in this research area.

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A Survey on Resource Allocation for 5G Heterogeneous Networks: Current Research, Future Trends, and Challenges

A method and an apparatus for transmitting broadcast signals thereof are disclosed. The method for transmitting broadcast signals includes encoding DP data according to a code rate, wherein the encoding further includes LDPC encoding the DP data according to the code rate, bit interleaving the LDPC encoded DP data, mapping the bit interleaved DP data onto constellations, MIMO (Multi Input Multi Output) encoding the mapped DP data, and time interleaving the MIMO encoded DP data; building at least one signal frame by arranging the encoded DP data; and modulating data in the built signal frame by OFDM method and transmitting the broadcast signals having the modulated data, wherein the step of modulating includes inserting CPs in the built signal frame based on a CP set which includes information about locations of CPs, wherein the CP set is defined based on FFT size.

Apparatus for transmitting broadcast signals, apparatus for receiving broadcast signals, method for transmitting broadcast signals and method for receiving broadcast signals

The development of the fifth generation (5G) wireless networks is gaining momentum to connect almost all aspects of life through the network with much higher speed, very low latency and ubiquitous connectivity. Due to its crucial role in our lives, the network must secure its users, components, and services. The security threat landscape of 5G has grown enormously due to the unprecedented increase in types of services and in the number of devices. Therefore, security solutions if not developed yet must be envisioned already to cope with diverse threats on various services, novel technologies, and increased user information accessible by the network. This paper outlines the 5G network threat landscape, the security vulnerabilities in the new technological concepts that will be adopted by 5G, and provides either solutions to those threats or future directions to cope with those security challenges. We also provide a brief outline of the post-5G cellular technologies and their security vulnerabilities which is referred to as future generations (XG) in this paper. In brief, this paper highlights the present and future security challenges in wireless networks, mainly in 5G, and future directions to secure wireless networks beyond 5G.

/pdf/security-for-5g-and-beyond-4cj06e4muu.pdf

Security for 5G and Beyond

Near-Capacity Multi-Functional MIMO Systems

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

A non-uniform signal constellation can be used to obtain shaping gain. One typical way to design non-uniform constellation is make the output signal follow a Gaussian distribution by using equally like signals with unequal spacing. However, for binary turbo coded modulation (BTCM) with M-QAM system, this non-uniform constellation design criteria can be modified based on different impacts of various turbo encoded hits sequence on BER performance. In this paper, author points out that shaping gain for M-QAM with BTCM can be achieved integrated with optimization of mapping rule, performance of the proposed scheme outperforms over BTCM with uniform constellation, especially under low and moderate SNR region. Meanwhile, the effects of code rate, order of QAM constellation on shaping gain are investigated. Numeral results are provided to support the proposed scheme and analysis. The proposed scheme can be applied to high rate data service with hybrid ARQ, such as HSDPA system.

Shaping gain by non-uniform QAM constellation with binary turbo coded modulation

The tremendous performance gain of heterogeneous networks (HetNets) is at the cost of complicated resource allocation. Considering information security, the resource allocation for HetNets becomes much more challenging and this is the focus of this paper. In this paper, the eavesdropper is hidden from the macro base stations. To relax the unpractical assumption on the channel state information on eavesdropper, a localization based algorithm is first given. Then a joint resource allocation algorithm is proposed in our work, which simultaneously considers physical layer security, cross-tier interference and joint optimization of power and subcarriers under fairness requirements. It is revealed in our work that the considered optimization problem can be efficiently solved relying on convex optimization theory and the Lagrangian dual decomposition method is exploited to solve the considered problem effectively. Moreover, in each iteration the closed-form optimal resource allocation solutions can be obtained based on the Karush-Kuhn-Tucker (KKT) conditions. Finally, the simulation results are given to show the performance advantages of the proposed algorithm.

Resource allocation for physical layer security in heterogeneous network with hidden eavesdropper

This paper presents an all digital timing recovery scheme for high speed modem. Compared to the conventional schemes, which use a VCO to drive A/D sampling clock, the new scheme based on interpolation filter is easy to simulate and implement. In the case which oversampling rate is slightly larger than 2, the new scheme can also give precise timing recovery. So this scheme is very suitable for high symbol rate situation. Firstly, the theory of the asynchronous symbol timing recovery is presented. Then, an implementation scheme of all digital timing recovery is proposed. As a key component, interpolation filter and timing controller are analyzed. Finally, based on the Xilinx Virtex II series FPGA xc2v1000-5, an all digital QPSK timing recovery scheme is implemented. Simulation and hardware test results show that the new scheme can efficiently be used when the symbol rate is up to 45 Msps.

High speed all digital symbol timing recovery based on FPGA

Multichannel audio signal is more difficult to be compressed than mono and stereo ones. A novel multichannel audio signal compression method based on tensor representation and decomposition is proposed in this paper. The multichannel audio is represented with 3-order tensor space and is decomposed into core tensor with three factor matrices in the way of channel, time and frequency. Only the truncated core tensor is transmitted which will be multiplied by the pre-trained factor matrices to reconstruct the original tensor space. Objective and subjective experiments have been done to show a very noticeable compression capability with an acceptable output quality. The novelty of the proposed compression method is that it enables both high compression capability and backward compatibility with limited signal distortion to the hearing.

http://ieeexplore.ieee.org/iel7/6245522/6825249/06825261.pdf

A novel multichannel audio signal compression method based on tensor representation and decomposition

The bit-interleaved coded irregular modulation (BICIM) was initially proposed for convolutional code and turbo code cases, which applies different signal constellations and mappings within one codeword. We firstly extend this idea to irregular LDPC codes and present an optimized mapping rule for LDPC coded irregular modulation according to the code degree distribution. Furthermore, we apply this optimized LDPC coded irregular modulation to chase combing (CC) HARQ and propose two enhanced HARQ schemes. The improved schemes can efficiently reduce the reliability variances of bits after soft combining. Simulation results show that they can not only achieve better BER performance but also provide a throughput performance enhancement.

Kuang Jingming

Papers

Shaping gain by non-uniform QAM constellation with binary turbo coded modulation

Resource allocation for physical layer security in heterogeneous network with hidden eavesdropper

High speed all digital symbol timing recovery based on FPGA

A novel multichannel audio signal compression method based on tensor representation and decomposition

Enhanced HARQ Schemes Based on LDPC Coded Irregular Modulation