Five Facets of 6G: Research Challenges and Opportunities

Whilst the fifth-generation (5G) systems are being rolled out across the globe, researchers have turned their attention to the exploration of radical next-generation solutions. At this early evolutionary stage we survey five main research facets of this field, namely {\em Facet~1: next-generation architectures, spectrum and services, Facet~2: next-generation networking, Facet~3: Internet of Things (IoT), Facet~4: wireless positioning and sensing, as well as Facet~5: applications of deep learning in 6G networks.} In this paper, we have provided a critical appraisal of the literature of promising techniques ranging from the associated architectures, networking, applications as well as designs. We have portrayed a plethora of heterogeneous architectures relying on cooperative hybrid networks supported by diverse access and transmission mechanisms. The vulnerabilities of these techniques are also addressed and carefully considered for highlighting the most of promising future research directions. Additionally, we have listed a rich suite of learning-driven optimization techniques. We conclude by observing the evolutionary paradigm-shift that has taken place from pure single-component bandwidth-efficiency, power-efficiency or delay-optimization towards multi-component designs, as exemplified by the twin-component ultra-reliable low-latency mode of the 5G system. We advocate a further evolutionary step towards multi-component Pareto optimization, which requires the exploration of the entire Pareto front of all optiomal solutions, where none of the components of the objective function may be improved without degrading at least one of the other components

Radio frequency-chain selection for energy and spectral efficiency maximization in hybrid beamforming under hardware imperfections

The next-generation wireless communications require reduced energy consumption, increased data rates and better signal coverage. The millimetre-wave frequency spectrum above 30 GHz can help fulfil the performance requirements of the next-generation mobile broadband systems. Multiple-input multiple-output technology can provide performance gains to help mitigate the increased path loss experienced at millimetre-wave frequencies compared with microwave bands. Emerging hybrid beamforming architectures can reduce the energy consumption and hardware complexity with the use of fewer radio-frequency (RF) chains. Energy efficiency is identified as a key fifth-generation metric and will have a major impact on the hybrid beamforming system design. In terms of transceiver power consumption, deactivating parts of the beamformer structure to reduce power typically leads to significant loss of spectral efficiency. Our aim is to achieve the highest energy efficiency for the millimetre-wave communications system while mitigating the resulting loss in spectral efficiency. To achieve this, we propose an optimal selection framework which activates specific RF chains that amplify the digitally beamformed signals with the analogue beamforming network. Practical precoding is considered by including the effects of user interference, noise and hardware impairments in the system modelling

Radio-frequency chain selection for energy and spectral efficiency maximization in hybrid beamforming under hardware imperfections

The next-generation wireless communications require reduced energy consumption, increased data rates and better signal coverage. The millimetre-wave frequency spectrum above 30 GHz can help fulfil the performance requirements of the next-generation mobile broadband systems. Multiple-input multiple-output technology can provide performance gains to help mitigate the increased path loss experienced at millimetre-wave frequencies compared with microwave bands. Emerging hybrid beamforming architectures can reduce the energy consumption and hardware complexity with the use of fewer radio-frequency (RF) chains. Energy efficiency is identified as a key fifth-generation metric and will have a major impact on the hybrid beamforming system design. In terms of transceiver power consumption, deactivating parts of the beamformer structure to reduce power typically leads to significant loss of spectral efficiency. Our aim is to achieve the highest energy efficiency for the millimetre-wave communications system while mitigating the resulting loss in spectral efficiency. To achieve this, we propose an optimal selection framework which activates specific RF chains that amplify the digitally beamformed signals with the analogue beamforming network. Practical precoding is considered by including the effects of user interference, noise and hardware impairments in the system modelling

Performance analysis for SDMA mmWave systems: using an approximate closed-form solution of downlink sum-rate

In this paper, we analyze the performance of downlink transmission in a space division multiple access (SDMA) millimeter-wave (mmWave) system with beam selection, where antenna arrays are considered at a base station (BS) and users. Under the assumption of limited scattering environment, we propose a simplified approach to analyze the downlink performance of the SDMA system. In this scheme, the throughput for each BS beam is approximated by a binary random variable for tractable analysis and an approximate closed-form solution for the downlink sum rate is derived. It is shown that the predicted throughputs by the proposed method reasonably agree with simulation results

Topology Control, Scheduling, and Spectrum Sensing in 5G Networks

The proliferation of intelligent wireless devices is remarkable. To address phenomenal traffic growth, a key objective of next-generation wireless networks such as 5G is to provide significantly larger bandwidth. To this end, the millimeter wave (mmWave) band (20 GHz -300 GHz) has been identified as a promising candidate for 5G and WiFi networks to support user data rates of multi-gigabits per second. However, path loss at mmWave is significantly higher than today\u27s cellular bands. Fortunately, this higher path loss can be compensated through the antenna beamforming technique-a transmitter focuses a signal towards a specific direction to achieve high signal gain at the receiver. In the beamforming mmWave network, two fundamental challenges are network topology control and user association and scheduling. This dissertation proposes solutions to address these two challenges. We also study a spectrum sensing scheme which is important for spectrum sharing in next-generation wireless networks. Due to beamforming, the network topology control in mmWave networks, i.e., how to determine the number of beams for each base station and the beam coverage, is a great challenge. We present a novel framework to solve this problem, termed Beamforming Oriented tOpology coNtrol (BOON). The objective is to reduce total downlink transmit power of base stations in order to provide coverage of all users with a minimum quality of service. BOON smartly groups nearby user equipment into clusters to dramatically reduce interference between beams and base stations so that we can significantly reduce transmit power from the base station. We have found that on average BOON uses only 10%, 32%, and 25% transmit power of three state-of-the-art schemes in the literature. Another fundamental problem in the mmWave network is the user association and traffic scheduling, i.e., associating users to base stations, and scheduling transmission of user traffic over time slots. This is because base station has a limited power budget and users have very diverse traffic, and also require some minimum quality of service. User association is challenging because it generally does not rely on the user distance to surrounding base stations but depends on if a user is covered by a beam. We develop a novel framework for user association and scheduling in multi-base station mmWave networks, termed the clustering Based dOwnlink user assOciation Scheduling, beamforming with power allocaTion (BOOST). The objective is to reduce the downlink network transmission time of all users\u27 traffic. On average, BOOST reduces the transmission time by 37%, 30%, and 26% compared with the three state-of-the-art user scheduling schemes in the literature. At last, we present a wavelet transform based spectrum sensing scheme that can simultaneously sense multiple subbands, even without knowing how the subbands are divided, i.e., their boundaries. It can adaptively detect all active subband signals and, thus, discover the residual spectrum that can be used by unlicensed devices