System-level assessment of a C-RAN based on generalized space–frequency index modulation for 5G new radio and beyond

Index modulation (IM) has been attracting considerable research efforts in recent years as it is considered a promising technology that can enhance spectral and energy efficiency and help cope with the rising demand of mobile traffic in future wireless networks. In this paper, we propose a cloud radio access network (C-RAN) suitable for fifth-generation (5G) and beyond systems, where the base stations (BSs) and access points (APs) transmit multidimensional IM symbols, which we refer to as precoding-aided transmitter-side generalized space–frequency IM (PT-GSFIM). The adopted PT-GSFIM approach is an alternative multiuser multiple-input multiple-output (MU-MIMO) scheme that avoids multiuser interference (MUI) while exploiting the inherent diversity in frequency-selective channels. To validate the potential gains of the proposed PT-GSFIM-based C-RAN, a thorough system-level assessment is presented for three different three-dimensional scenarios taken from standardized 5G New Radio (5G NR), using two different numerologies and frequency ranges. Throughput performance results indicate that the 28 GHz band in spite of its higher bandwidth and higher achieved throughput presents lower spectral efficiency (SE). The 3.5 GHz band having lower bandwidth and lower achieved throughput attains higher SE. Overall, the results indicate that a C-RAN based on the proposed PT-GSFIM scheme clearly outperforms both generalized spatial modulation (GSM) and conventional MU-MIMO, exploiting its additional inherent frequency diversity.info:eu-repo/semantics/publishedVersio

System-level assessment of low complexity hybrid precoding designs for massive MIMO downlink transmissions in beyond 5G networks

The fast growth experienced by the telecommunications field during the last few decades has been motivating the academy and the industry to invest in the design, testing and deployment of new evolutions of wireless communication systems. Terahertz (THz) communication represents one of the possible technologies to explore in order to achieve the desired achievable rates above 100 Gbps and the extremely low latency required in many envisioned applications. Despite the potentialities, it requires proper system design, since working in the THz band brings a set of challenges, such as the reflection and scattering losses through the transmission path, the high dependency with distance and the severe hardware constraints. One key approach for overcoming some of these challenges relies on the use of massive/ultramassive antenna arrays combined with hybrid precoders based on fully connected phase-shifter architectures or partially connected architectures, such as arrays of subarrays (AoSAs) or dynamic AoSAs (DAoSAs). Through this strategy, it is possible to obtain very high-performance gains while drastically simplifying the practical implementation and reducing the overall power consumption of the system when compared to a fully digital approach. Although these types of solutions have been previously proposed to address some of the limitations of mmWave/THz communications, a lack between link-level and system-level analysis is commonly verified. In this paper, we present a thorough system-level assessment of a cloud radio access network (C-RAN) for beyond 5G (B5G) systems where the access points (APs) operate in the mmWave/THz bands, supporting multi-user MIMO (MU-MIMO) transmission with massive/ultra-massive antenna arrays combined with low-complexity hybrid precoding architectures. Results showed that the C-RAN deployments in two indoor office scenarios for the THz were capable of achieving good throughput and coverage performances, with only a small compromise in terms of gains when adopting reduced complexity hybrid precoders. Furthermore, we observed that the indoor-mixed office scenario can provide higher throughput and coverage performances independently of the cluster size when compared to the indoor-open office scenario.info:eu-repo/semantics/publishedVersio

Self-organised multi-objective network clustering for coordinated communications in future wireless networks

The fifth generation (5G) cellular system is being developed with a vision of 1000 times more capacity than the fourth generation (4G) systems to cope with ever increasing mobile data traffic. Interference mitigation plays an important role in improving the much needed overall capacity especially in highly interference-limited dense deployment scenarios envisioned for 5G. Coordinated multi-point (CoMP) is identified as a promising interference mitigation technique where multiple base stations (BS) can cooperate for joint transmission/reception by exchanging user/control data and perform joint signal processing to mitigate inter-cell interference and even exploit it as a useful signal. CoMP is already a key feature of long term evolution-advanced (LTE-A) and envisioned as an essential function for 5G. However, CoMP cannot be realized for the whole network due to its computational complexity, synchronization requirement between coordinating BSs and high backhaul capacity requirement. BSs need to be clustered into smaller groups and CoMP can be activated within these smaller clusters. This PhD thesis aims to investigate optimum dynamic CoMP clustering solutions in 5G and beyond wireless networks with massive small cell (SC) deployment. Truly self-organised CoMP clustering algorithms are investigated, aiming to improve much needed spectral efficiency and other network objectives especially load balancing in future wireless networks. Low complexity, scalable, stable and efficient CoMP clustering algorithms are designed to jointly optimize spectral efficiency, load balancing and limited backhaul availability. Firstly, we provide a self organizing, load aware, user-centric CoMP clustering algorithm in a control and data plane separation architecture (CDSA) proposed for 5G to maximize spectral efficiency and improve load balancing. We introduce a novel re-clustering algorithm for user equipment (UE) served by highly loaded cells and show that unsatisfied UEs due to high load can be significantly reduced with minimal impact on spectral efficiency. Clustering with load balancing algorithm exploits the capacity gain from increase in cluster size and also the traffic shift from highly loaded cells to lightly loaded neighbours. Secondly, we develop a novel, low complexity, stable, network-centric clustering model to jointly optimize load balancing and spectral efficiency objectives and tackle the complexity and scalability issues of user-centric clustering. We show that our clustering model provide high spectral efficiency in low-load scenario and better load distribution in high-load scenario resulting in lower number of unsatisfied users while keeping spectral efficiency at comparably high levels. Unsatisfied UEs due to high load are reduced by $68.5\%$ with our algorithm when compared to greedy clustering model. In this context, the unique contribution of this work that it is the first attempt to fill the gap in literature for multi-objective, network-centric CoMP clustering, jointly optimizing load balancing and spectral efficiency. Thirdly, we design a novel multi-objective CoMP clustering algorithm to include backhaul-load awareness and tackle one of the biggest challenges for the realization of CoMP in future networks i.e. the demand for high backhaul bandwidth and very low latency. We fill the gap in literature as the first attempt to design a clustering algorithm to jointly optimize backhaul/radio access load and spectral efficiency and analyze the trade-off between them. We employ 2 novel coalitional game theoretic clustering methods, 1-a novel merge/split/transfer coalitional game theoretic clustering algorithm to form backhaul and load aware BS clusters where spectral efficiency is still kept at high level, 2-a novel user transfer game model to move users between clusters to improve load balancing further. Stability and complexity analysis is provided and simulation results are presented to show the performance of the proposed method under different backhaul availability scenarios. We show that average system throughout is increased by 49.9% with our backhaul-load aware model in high load scenario when compared to a greedy model. Finally, we provide an operator's perspective on deployment of CoMP. Firstly, we present the main motivation and benefits of CoMP from an operator's viewpoint. Next, we present operational requirements for CoMP implementation and discuss practical considerations and challenges of such deployment. Possible solutions for these experienced challenges are reviewed. We then present initial results from a UL CoMP trial and discuss changes in key network performance indicators (KPI) during the trial. Additionally, we propose further improvements to the trialed CoMP scheme for better potential gains and give our perspective on how CoMP will fit into the future wireless networks

Hybrid ray-tracing/FDTD method for human exposure evaluation of a massive MIMO technology in an industrial indoor environment

This paper presents a numerical approach for massive multiple-input multiple-output (MIMO) human exposure assessment. It combines ray-tracing for the estimation of the wireless channel and the finite-difference time-domain method to simulate the exposure of a realistic human phantom. We apply it to estimate the exposure in a model of an industrial indoor environment with a single massive MIMO base station (BS). The exposure scenarios include line-of-sight and non-line-of-sight propagation with the BS using equal gain transmission precoding at 3.5 GHz. Calculated channel parameters are discussed in comparison with the data available in the literature. The exposure in the phantom's head is evaluated in terms of the peak-spatial specific absorption rate averaged over a 10-g cube and referenced to the free-space time-averaged Poynting vector magnitude at the same location