Improved Two-Dimensional Double Successive Projection Algorithm for Massive MIMO Detection

In a massive MIMO system, a large number of receiving antennas at the base station can simultaneously serve multiple users. Linear detectors can achieve optimal performance but require large dimensional matrix inversion, which requires a large number of arithmetic operations. Several low complexity solutions are reported in the literature. In this work, we have presented an improved two-dimensional double successive projection (I2D-DSP) algorithm for massive MIMO detection. Simulation results show that the proposed detector performs better than the conventional 2D-DSP algorithm at a lower complexity. The performance under channel correlation also improves with the I2D-DSP scheme. We further developed a soft information generation algorithm to reduce the number of magnitude comparisons. The proposed soft symbol generation method uses real domain operation and can reduce almost 90% flops and magnitude comparisons

A tutorial on the characterisation and modelling of low layer functional splits for flexible radio access networks in 5G and beyond

The centralization of baseband (BB) functions in a radio access network (RAN) towards data processing centres is receiving increasing interest as it enables the exploitation of resource pooling and statistical multiplexing gains among multiple cells, facilitates the introduction of collaborative techniques for different functions (e.g., interference coordination), and more efficiently handles the complex requirements of advanced features of the fifth generation (5G) new radio (NR) physical layer, such as the use of massive multiple input multiple output (MIMO). However, deciding the functional split (i.e., which BB functions are kept close to the radio units and which BB functions are centralized) embraces a trade-off between the centralization benefits and the fronthaul costs for carrying data between distributed antennas and data processing centres. Substantial research efforts have been made in standardization fora, research projects and studies to resolve this trade-off, which becomes more complicated when the choice of functional splits is dynamically achieved depending on the current conditions in the RAN. This paper presents a comprehensive tutorial on the characterisation, modelling and assessment of functional splits in a flexible RAN to establish a solid basis for the future development of algorithmic solutions of dynamic functional split optimisation in 5G and beyond systems. First, the paper explores the functional split approaches considered by different industrial fora, analysing their equivalences and differences in terminology. Second, the paper presents a harmonized analysis of the different BB functions at the physical layer and associated algorithmic solutions presented in the literature, assessing both the computational complexity and the associated performance. Based on this analysis, the paper presents a model for assessing the computational requirements and fronthaul bandwidth requirements of different functional splits. Last, the model is used to derive illustrative results that identify the major trade-offs that arise when selecting a functional split and the key elements that impact the requirements.This work has been partially funded by Huawei Technologies. Work by X. Gelabert and B. Klaiqi is partially funded by the European Union's Horizon Europe research and innovation programme (HORIZON-MSCA-2021-DN-0) under the Marie Skłodowska-Curie grant agreement No 101073265. Work by J. Perez-Romero and O. Sallent is also partially funded by the Smart Networks and Services Joint Undertaking (SNS JU) under the European Union’s Horizon Europe research and innovation programme under Grant Agreements No. 101096034 (VERGE project) and No. 101097083 (BeGREEN project) and by the Spanish Ministry of Science and Innovation MCIN/AEI/10.13039/501100011033 under ARTIST project (ref. PID2020-115104RB-I00). This last project has also funded the work by D. Campoy.Peer ReviewedPostprint (author's final draft

Large-Scale-Fading Decoding in Cellular Massive MIMO Systems with Spatially Correlated Channels

Massive multiple-input--multiple-output (MIMO) systems can suffer from coherent intercell interference due to the phenomenon of pilot contamination. This paper investigates a two-layer decoding method that mitigates both coherent and non-coherent interference in multi-cell Massive MIMO. To this end, each base station (BS) first estimates the channels to intra-cell users using either minimum mean-squared error (MMSE) or element-wise MMSE (EW-MMSE) estimation based on uplink pilots. The estimates are used for local decoding on each BS followed by a second decoding layer where the BSs cooperate to mitigate inter-cell interference. An uplink achievable spectral efficiency (SE) expression is computed for arbitrary two-layer decoding schemes. A closed-form expression is then obtained for correlated Rayleigh fading, maximum-ratio combining, and the proposed large-scale fading decoding (LSFD) in the second layer. We also formulate a sum SE maximization problem with both the data power and LSFD vectors as optimization variables. Since this is an NP-hard problem, we develop a low-complexity algorithm based on the weighted MMSE approach to obtain a local optimum. The numerical results show that both data power control and LSFD improves the sum SE performance over single-layer decoding multi-cell Massive MIMO systems.Comment: 17 pages; 10 figures; Accepted for publication in IEEE Transactions on Communication

Energy Efficiency Optimization of 5G Radio Frequency Chain Systems

With the massive multi-input multi-output (MIMO) antennas technology adopted for the fifth generation (5G) wireless communication systems, a large number of radio frequency (RF) chains have to be employed for RF circuits. However, a large number of RF chains not only increase the cost of RF circuits but also consume additional energy in 5G wireless communication systems. In this paper we investigate energy and cost efficiency optimization solutions for 5G wireless communication systems with a large number of antennas and RF chains. An energy efficiency optimization problem is formulated for 5G wireless communication systems using massive MIMO antennas and millimeter wave technology. Considering the nonconcave feature of the objective function, a suboptimal iterative algorithm, i.e., the energy efficient hybrid precoding (EEHP) algorithm is developed for maximizing the energy efficiency of 5G wireless communication systems. To reduce the cost of RF circuits, the energy efficient hybrid precoding with the minimum number of RF chains (EEHP-MRFC) algorithm is also proposed. Moreover, the critical number of antennas searching (CNAS) and user equipment number optimization (UENO) algorithms are further developed to optimize the energy efficiency of 5G wireless communication systems by the number of transmit antennas and UEs. Compared with the maximum energy efficiency of conventional zero-forcing (ZF) precoding algorithm, numerical results indicate that the maximum energy efficiency of the proposed EEHP and EEHP-MRFC algorithms are improved by 220% and 171%, respectively.Comment: 16 pages, 7 figure

The Four-C Framework for High Capacity Ultra-Low Latency in 5G Networks: A Review

Network latency will be a critical performance metric for the Fifth Generation (5G) networks expected to be fully rolled out in 2020 through the IMT-2020 project. The multi-user multiple-input multiple-output (MU-MIMO) technology is a key enabler for the 5G massive connectivity criterion, especially from the massive densification perspective. Naturally, it appears that 5G MU-MIMO will face a daunting task to achieve an end-to-end 1 ms ultra-low latency budget if traditional network set-ups criteria are strictly adhered to. Moreover, 5G latency will have added dimensions of scalability and flexibility compared to prior existing deployed technologies. The scalability dimension caters for meeting rapid demand as new applications evolve. While flexibility complements the scalability dimension by investigating novel non-stacked protocol architecture. The goal of this review paper is to deploy ultra-low latency reduction framework for 5G communications considering flexibility and scalability. The Four (4) C framework consisting of cost, complexity, cross-layer and computing is hereby analyzed and discussed. The Four (4) C framework discusses several emerging new technologies of software defined network (SDN), network function virtualization (NFV) and fog networking. This review paper will contribute significantly towards the future implementation of flexible and high capacity ultra-low latency 5G communications

Efficient implementation of channel estimation algorithm for beamforming

Abstract. The future 5G mobile network technology is expected to offer significantly better performance than its predecessors. Improved data rates in conjunction with low latency is believed to enable technological revolutions such as self-driving cars. To achieve faster data rates, MIMO systems can be utilized. These systems enable the use of spatial filtering technique known as beamforming. Beamforming that is based on the preacquired channel matrix is computationally very demanding causing challenges in achieving low latency. By acquiring the channel matrix as efficiently as possible, we can facilitate this challenge. In this thesis we examined the implementation of channel estimation algorithm for beamforming with a digital signal processor specialized in vector computation. We present implementations for different antenna configurations based on three different approaches. The results show that the best performance is achieved by applying the algorithm according to the limitations given by the system and the processor architecture. Although the exploitation of the parallel architecture was proved to be challenging, the implementation of the algorithm would have benefitted from the greater amount of parallelism. The current parallel resources will be a challenge especially in the future as the size of antenna configurations is expected to grow.Keilanmuodostuksen tarvitseman kanavaestimointialgoritmin tehokas toteutus. Tiivistelmä. Tulevan viidennen sukupolven mobiiliverkkoteknologian odotetaan tarjoavan merkittävästi edeltäjäänsä parempaa suorituskykyä. Tämän suorituskyvyn tarjoamat suuret datanopeudet yhdistettynä pieneen latenssiin uskotaan mahdollistavan esimerkiksi itsestään ajavat autot. Suurempien datanopeuksien saavuttamiseksi voidaan hyödyntää monitiekanavassa käytettävää MIMO-systeemiä, joka mahdollistaa keilanmuodostuksena tunnetun spatiaalisen suodatusmenetelmän käytön. Etukäteen hankittuun kanavatilatietoon perustuva keilanmuodostus on laskennallisesti erittäin kallista. Tämä aiheuttaa haasteita verkon pienen latenssivaatimuksen saavuttamisessa. Tässä työssä tutkittiin keilanmuodostukselle tarkoitetun kanavaestimointialgoritmin tehokasta toteutusta hyödyntäen vektorilaskentaan erikoistunutta prosessoriarkkitehtuuria. Työssä esitellään kolmea eri lähestymistapaa hyödyntävät toteutukset eri kokoisille antennikonfiguraatioille. Tuloksista nähdään, että paras suorituskyky saavutetaan sovittamalla algoritmi järjestelmän ja arkkitehtuurin asettamien rajoitusten mukaisesti. Vaikka rinnakkaisarkkitehtuurin hyödyntäminen asetti omat haasteensa, olisi algoritmin toteutus hyötynyt suuremmasta rinnakkaisuuden määrästä. Nykyinen rinnakkaisuuden määrä tulee olemaan haaste erityisesti tulevaisuudessa, sillä antennikonfiguraatioiden koon odotetaan kasvavan