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

In this chapter we deal with the following nonlinear fractional programming problem: 

$$P:\mathop{{\max }}\limits_{{x \in s}} q(x) = (f(x) + \alpha )/((x) + \beta )$$

where f, g: R n → R, α, β ∈ R, S ⊆ R n . To simplify things, and without restricting the generality of the problem, it is usually assumed that, g(x) + β + 0 for all x ∈ S,S is non-empty and that the objective function has a finite optimal value.

Nonlinear Fractional Programming

The focus of wireless research is increasingly shifting toward 6G as 5G deployments get underway. At this juncture, it is essential to establish a vision of future communications to provide guidance for that research. In this paper, we attempt to paint a broad picture of communication needs and technologies in the timeframe of 6G. The future of connectivity is in the creation of digital twin worlds that are a true representation of the physical and biological worlds at every spatial and time instant, unifying our experience across these physical, biological and digital worlds. New themes are likely to emerge that will shape 6G system requirements and technologies, such as: (i) new man-machine interfaces created by a collection of multiple local devices acting in unison; (ii) ubiquitous universal computing distributed among multiple local devices and the cloud; (iii) multi-sensory data fusion to create multi-verse maps and new mixed-reality experiences; and (iv) precision sensing and actuation to control the physical world. With rapid advances in artificial intelligence, it has the potential to become the foundation for the 6G air interface and network, making data, compute and energy the new resources to be exploited for achieving superior performance. In addition, in this paper we discuss the other major technology transformations that are likely to define 6G: (i) cognitive spectrum sharing methods and new spectrum bands; (ii) the integration of localization and sensing capabilities into the system definition, (iii) the achievement of extreme performance requirements on latency and reliability; (iv) new network architecture paradigms involving sub-networks and RAN-Core convergence; and (v) new security and privacy schemes.

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Communications in the 6G Era

Massive MIMO is a promising technique for increasing the spectral efficiency (SE) of cellular networks, by deploying antenna arrays with hundreds or thousands of active elements at the base stations and performing coherent transceiver processing. A common rule-of-thumb is that these systems should have an order of magnitude more antennas $M$ than scheduled users $K$ because the users’ channels are likely to be near-orthogonal when $M/K > 10$ . However, it has not been proved that this rule-of-thumb actually maximizes the SE. In this paper, we analyze how the optimal number of scheduled users $K^\star$ depends on $M$ and other system parameters. To this end, new SE expressions are derived to enable efficient system-level analysis with power control, arbitrary pilot reuse, and random user locations. The value of $K^\star$ in the large- $M$ regime is derived in closed form, while simulations are used to show what happens at finite $M$ , in different interference scenarios, with different pilot reuse factors, and for different processing schemes. Up to half the coherence block should be dedicated to pilots and the optimal $M/K$ is less than 10 in many cases of practical relevance. Interestingly, $K^\star$ depends strongly on the processing scheme and hence it is unfair to compare different schemes using the same $K$ .

Massive MIMO for Maximal Spectral Efficiency: How Many Users and Pilots Should Be Allocated?

Random Matrix Methods for Wireless Communications: Estimation

This paper investigates the performance of massive multiple-input multiple-output (MIMO) cellular systems with pilot contamination. We derive a closed-form approximation of the area spectral efficiency (ASE) for uplink multicell multiuser massive MIMO systems by using a uniformly distributed user location model. Considering a practical power consumption model, we obtain the area energy efficiency (AEE) in a cell. The theoretical results of ASE and AEE can be used to investigate some system parameters of massive MIMO cellular systems, such as the number of antennas at the base station, the number of users, and the number of symbols over which the channel remains constant. We also study the optimal pilot-to-data power ratio for both ASE maximization and AEE maximization.

Area Spectral Efficiency and Area Energy Efficiency of Massive MIMO Cellular Systems

In this paper, the spectral efficiency of network assisted full-duplex communications (NAFD) in cell-free (CF) massive multiple-input multiple-output (MIMO) network with imperfect channel state information is investigated under spatial correlated channels. Based on large dimensional random matrix theory, the deterministic equivalents for the uplink sum-rate with minimum-mean-square-error receiver as well as the downlink sum-rate with zero-forcing and regularized zero-forcing beamforming are presented. Numerical results show that under various environmental settings, the deterministic equivalents are accurate in both a large-scale system and system with a finite number of antennas. It is also shown that with the downlink-to-uplink interference cancellation, the uplink spectral efficiency of CF massive MIMO with NAFD could be improved. The spectral efficiencies of NAFD with different duplex configurations such as in-band full-duplex, and half-duplex are compared. With the same total numbers of transmit and receive antennas, NAFD with half-duplex remote antenna units offers a higher spectral efficiency. To alleviate the uplink-to-downlink interference, a novel genetic algorithm based user scheduling strategy (GAS) is proposed. Simulation results show that the achievable downlink sum-rate by using the GAS is greatly improved compared to that by using the random user scheduling.

Performance of Network-Assisted Full-Duplex for Cell-Free Massive MIMO

In this paper, the downlink spectral efficiency of multicell multiuser distributed massive MIMO systems with linear beamforming is studied in the presence of pilot contamination. According to the levels of effective channel gain information at user side, we provide the lower bound and upper bound on user ergodic achievable downlink rate. Due to the different access distance from each user to different remote antenna units, the entries of user channel vectors are no longer identically distributed in distributed massive MIMO systems, which makes the spectral efficiency analysis challenging. Using the properties of Gamma distributions together with the approximate methods for nonisotropic vectors, we derive tractable, but accurate closed-form expressions for the rate bounds with maximum ratio transmission (MRT) and zero-forcing (ZF) beamforming in distributed massive MIMO systems. Based on these expressions, user ultimate achievable rates are also given when the ratio of the total number of transmit antennas to the number of users goes to infinity. It is shown that MRT and ZF beamforming achieve the same ultimate rate no matter what levels of effective channel gain information at user side. Numerical results show that ZF achieves better performance gain and faster convergence speed than MRT. When the coherence interval is large, the downlink beamforming training scheme is more preferable for the distributed massive MIMO systems.

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Downlink Spectral Efficiency of Distributed Massive MIMO Systems With Linear Beamforming Under Pilot Contamination

This letter proposes a deep learning-based pilot design scheme to minimize the sum mean square error (MSE) of channel estimation for multi-user distributed massive multiple-input multiple-output (MIMO) systems. The pilot signal of each user is expressed as a weighted superposition of orthonormal pilot sequence basis, where the power assigned to each pilot sequence is the corresponding weight. A multi-layer fully connected deep neural network (DNN) is designed to optimize the power allocated to each pilot sequence to minimize the sum MSE, which takes the channel large-scale fading coefficients as input and outputs the pilot power allocation vector. The loss function of the DNN is defined as the sum MSE, and we leverage the unsupervised learning strategy to train the DNN. Simulation results show that the proposed scheme achieves better sum MSE performance than other methods with low complexity.

Deep Learning-Based Pilot Design for Multi-User Distributed Massive MIMO Systems

In this paper, considering the practical channel impairments known as pilot contamination and channel aging induced by both Doppler shift and phase noise, we analyze the uplink spectral efficiency of multi-cell multi-user distributed massive multi-input multi-output (MIMO) systems with linear receivers. A joint channel model is first provided, which allows studying the impacts of these impairments on the achievable rate performance of distributed massive MIMO systems simultaneously. Based on this model, using the properties of Gamma distributions together with the approximate methods for non-isotropic vectors, we derive tractable but accurate closed-form expressions for the uplink achievable rate with maximum ratio combining (MRC) and zero-forcing (ZF) receivers in distributed massive MIMO systems. User ultimate achievable rate is also given when the ratio of total number of transmit antennas to the number of users is very large. It is shown that MRC and ZF receivers achieve the same ultimate achievable rate in the presence of the practical channel impairments. Numerical results show that channel aging has a greater impact on ZF receiver and the impact of the Doppler shift is more detrimental than that of the phase noise.

Jiamin Li

Papers

Area Spectral Efficiency and Area Energy Efficiency of Massive MIMO Cellular Systems

Performance of Network-Assisted Full-Duplex for Cell-Free Massive MIMO

Downlink Spectral Efficiency of Distributed Massive MIMO Systems With Linear Beamforming Under Pilot Contamination

Deep Learning-Based Pilot Design for Multi-User Distributed Massive MIMO Systems

Uplink Spectral Efficiency Analysis of Distributed Massive MIMO With Channel Impairments