Inter-numerology Interference Research Articles

A three-level slicing algorithm in a multi-slice multi-numerology context

Fifth generation (5G) mobile networks feature a new concept called Bandwidth Part (BWP) that permits applying various multiple Orthogonal Frequency-Division Multiplexing (OFDM) subcarrier spacing, also coined numerologies, in the same band. With flexible numerology, the sought-after purpose is achieving lower latency for the Ultra Reliable Low Latency Communications (URLLC) service. However, the lucrative multiple numerology gains might not stand because of the ensuing Inter-Numerology Interference (INI) problems. In this paper, we assess the feasibility, as well as the profitability, of multi-numerology in a multi-slice setting. In such a context, it is paramount to study the radio resource allocation problem at the Radio Access Network (RAN) level. To that aim, we propose a three-level slicing algorithm that efficiently selects the BWP serving the URLLC users from a set of BWPs using different numerologies, designs a guard band among these BWPs to reduce INI without wasting radio resources, and attributes a bandwidth to each slice depending on multiple parameters such as the users’ Key Performance Indicators (KPIs), channel conditions, INI, and the incurred monetary cost. This work considers the three 5G services, namely the enhanced Mobile BroadBand (eMBB) service, the URLLC service and the massive Machine-Type Communications (mMTC) service. The proposed algorithm combines various tools from Game Theory, Deep-Q learning Networks as well as savvy heuristics. The performance evaluation demonstrates the high efficiency of our solution in terms of latency and throughput compared to well-known approaches in the State-of-the-Art.

Inter-Numerology Interference in Mixed Numerology OFDM Systems in Time-Varying Fading Channels With Phase Noise

Mixed numerology has been adopted in the orthogonal frequency division multiplexing-based physical layer of 5G new radio (NR) to serve diverse use cases and services. Since the subcarriers of different numerologies have different bandwidths and symbol durations, they interfere with each other despite being centered at different frequencies. We analyze the inter-numerology interference (INI) encountered by these systems in wideband time-varying channels in the presence of phase noise. We derive novel expressions for the fading-averaged INI power at each subcarrier as a function of the channel’s power delay profile. These lead to insightful, tight bounds for the bandwidth-averaged INI power, which bring out the combined impact of Doppler spread and phase noise. Our comprehensive approach applies to the entire family of numerologies of 5G NR, and accounts for partial subcarrier loading, guard bands, and non-line-of-sight and line-of-sight channels. Our results show that INI affects high-rate modulation and coding schemes (MCSs). To mitigate its impact, we propose a novel statistical square root power allocation scheme that exploits the variation in the average INI powers across the subcarriers. It achieves a lower block error rate than uniform power allocation, which is used in 5G NR.

Optimal radio resource management in 5G NR featuring network slicing

3GPP 5G New Radio (NR) has introduced several new features that the network slicing concept can leverage to guarantee the heterogeneous requirements in terms of throughput and delays of expected 5G network services. Mainly, these features are (i) Mixed-numerology to control the time slot duration, hence guaranteeing low latency requirements; (ii) Bandwidth Parts (BWP) to control the number of radio resources allocated to users, hence satisfying different throughput requirements. However, efficient radio resource management is already complex, and adding these new dimensions will further increase this complexity. In this paper, we first propose modeling radio resource management in 5G NR featuring network slicing through a Mixed Integer Linear Program (MILP). For our best knowledge, this is the first MILP modeling of the radio resource management featuring network slicing taking into account (i) Mixed-numerology, (ii) both latency and throughput requirements (iii) multiple slice attach per UE (iv) Inter-Numerology Interference (INI). After showing that solving the problem takes an exponential time, we considered a new approach to solve it in a polynomial time, which is highly required when scheduling radio resources. The new approach consists of formalizing this problem using a Deep Reinforcement Learning (DRL)-based solver. We evaluate the use of RL to solve the problem for different network configurations and compared its performance with the optimal solution obtained by solving the MILP problem.

Precoder Based Transceivers Design for Non-Orthogonal Multiple Access Mixed Numerologies

Orthogonal Frequency Division Multiplexing (OFDM) based mixed numerology scheme and Non-Orthogonal Multiple Access (NOMA) are the two important key features of 5G radio standard capable of handling diversified service requirements. However, the mixed numerology scheme suffers from an inherent problem called Inter Numerology Interference (INI) that arises due to non-orthogonality issue between any two numerologies, whereas NOMA suffers from potential Bit Error Rate (BER). In this research work, transmitter architecture based on windowing and pre-coding techniques to mitigate the INI and two receiver architectures based on the principle of Successive Interference Cancellation (SIC) and Combined Maximum Likelihood (CDML) decoding are proposed for optimal decoding of the information. The proposed system model is designed to operate under mixed numerology NOMA scenario. The BER performance analysis is done for both the receivers and compared with each other and the conclusive remarks are drawn.

Multi-scale mapping algorithm for INI cancellation and a novel mixed-numerologies OFDM transceiver

OFDM with mixed-numerologies enhances the system flexibility effectively to meet the demands of diversified application scenarios. However, the coexistence of waveforms with different numerologies leads to serious inter-numerology interference (INI), and the corresponding relationship between the number of guard subcarriers and the power of INI needs to be considered for scheduling subcarriers. In this paper, we propose a multi-scale mapping (MSM) and INI cancellation (MSM-INIC) algorithm as well as the corresponding de-MSM algorithm for mixed-numerologies OFDM system. Based on the proposed algorithms, we provide a novel transceiver in the scenario of multi-path fading channel, in which subcarrier scheduling does not need to consider whether the guard band is allocated. In the proposed transmitter, an additional MSM-INIC module is employed to pre-compensate signal distortion for downlink, and in the receiver, a de-MSM module is applied to de-map the received signals for recovering the original numerologies. Furthermore, we reveal the inherent property of the mapped signals, and propose a low computational complexity de-MSM algorithm accordingly. Simulation results verify the superiority of the proposed transceiver in BER performance as well as spectrum efficiency even without any guard band.

A QoS-aware and channel-aware Radio Resource Management framework for multi-numerology systems

To support the diversified 5G usage scenarios, the 5G New Radio (NR) physical layer is enriched with the flexible multi-numerology feature, where multiple frame structures with different sub-carrier spacings coexist in one frequency band. However, this flexibility brings with it a further challenge in Radio Resource Management (RRM), how to best allocate the available band spectrum among the different non-orthogonal numerologies. This work presents a novel Quality of Service (QoS)-aware and channel-aware RRM framework that manages diversified type of services, i.e., Best Effort (BE) services and Guaranteed Bit Rate (GBR) services with different priorities. The goal is to maximize the number of satisfied GBR services, according to the priority, and the throughput of the BE services. To provide a feasible solution, the proposed framework consists of two control levels. The first one appropriately splits the available bandwidth among the various numerologies, with the aim of achieving the above goal, trying to assign continuous portion of band to the same Numerology, in order to mitigate the Inter-Numerology Interference (INI) phenomenon. Moreover, it includes an advanced dropping strategy to support overload conditions. The second level distributes the Physical Resource Blocks (PRBs) to the User Equipments (UEs) belonging to the same numerology, by exploiting scheduling algorithms available in literature. Through an extensive simulation campaign, under different traffic load and channel conditions, we compare the proposed RRM framework with other reference schemes available in literature. The results show that the proposed framework outperforms them in any simulation setup.

A reinforcement learning agent for mixed-numerology interference-aware slice spectrum allocation with non-deterministic and deterministic traffic

5G RAN slicing is an essential tool to support the simultaneous coexistence of enhanced mobile broadband (eMBB) and ultra-reliable low-latency communications (URLLC) network slices on a shared mixed-numerology physical layer. Moreover, due to recent advance of the private network paradigm, RAN slicing assumes a central role to provide dedicated radio coverage to industry 4.0 applications as a standalone RAN. Unlike the stochastic traffic behavior characterizing URLLC slices in classical mobile networks, industrial networks support URLLC services with deterministic and periodic traffic patterns. Based on this alternative network characterization, we design a deep reinforcement learning (DRL) agent that simultaneously provides a spectrum allocation fulfilling the eMBB and URLLC service requirements and mitigates the inter-numerology interference (INI). Furthermore, by exploiting the information about the deterministic traffic patterns, we specialize the agent reward function to improve the spectrum allocation effectiveness for URLLC slices deployed in industrial environments. We assess the agent performance with respect to resource allocation schemes that are INI agnostic. Results reveal that the proposed solution outperforms the benchmark schemes in terms of service provisioning performance in both network scenarios (e.g. mobile and industrial) and showcase the benefit of INI mitigation.

Intelligent multi-branch allocation of spectrum slices for inter-numerology interference minimization

Network slicing and mixed-numerology access schemes cover a central role to enable the flexible multi-service connectivity that characterizes 5G radio access networks (RAN). However, the interference generated by the simultaneous multiplexing of radio slices having heterogeneous subcarrier spacing can hinder the isolation of the different slices sharing the RAN and their effectiveness in meeting the application requirements. To overcome these issues, we design a radio resource allocation scheme that accounts for the inter-numerology interference and maximizes the aggregate network throughput. To overcome the computationally complexity of the optimal formulation, we leverage deep reinforcement learning (DRL) to design an agent capable of approximating the optimal solution exploiting a model-free environment formulation. We propose a multi-branch agent architecture, based on Branching Dueling Q-networks (BDQ), which ensures the agent scalability as the number of spectrum resources and network slices increases. In addition, we augment the agent learning performance by including an action mapping procedure designed to enforce the selection of feasible actions. We compare the agent performance to several benchmarks schemes. Results show that the proposed solution provides a good approximation of the optimal allocation in most scenarios.

Interference Analysis and Power Allocation in the Presence of Mixed Numerologies

The flexibility in supporting heterogeneous services with vastly different technical requirements is one of the distinguishing characteristics of the fifth generation (5G) communication systems and beyond. One viable solution is to divide the system bandwidth into several bandwidth parts (BWPs), each having a distinct numerology optimized for a particular service. However, multiplexing of mixed numerologies over a unified physical infrastructure comes at the cost of induced interference. In this paper, we develop an analytical system model for inter-numerology interference (InterNI) analysis in orthogonal frequency-division multiplexing (OFDM) systems with and without filter processing in the presence of mixed numerologies. With the analytical model, the level of InterNI is quantified by the developed analytical metric, which is expressed as a function of several system parameters. This leads to an analysis and evaluation of these parameters for meeting a given distortion target. Moreover, a case study on power allocation utilizing the derived analysis is presented, where an optimization problem of maximizing the sum rate is formulated, and a solution is also provided. It is also demonstrated that a filtered-OFDM system better accommodates the coexistence of mixed numerologies. The proposed model provides an accurate analytical guidance for the multi-service design in 5G and beyond systems.

Analysis and Cancellation of Mixed-Numerologies Interference for Massive MIMO-OFDM UL

The extremely diverse service requirements is an important challenge for the future generations of wireless communication technologies. As promising solutions, orthogonal frequency division multiplexing (OFDM)-based massive multiple-input multiple-output (MIMO) and mixed numerologies transmission are proposed. This letter investigates the transmission of different users, sharing the same bandwidth while they are associated to different numerologies. A transceiver design which aims to manage the mixed numerology spectrum sharing (SS) transmission is first presented. Then, we analyze the inter-numerology interference (INI) and derive its corresponding theoretical expressions. Based on the derived closed-form expressions, a new INI cancellation scheme is introduced, which efficiently mitigates the INI and enhances the performance of massive MIMO-OFDM uplink systems.

PAPR Reduction Using Iterative Clipping/Filtering and ADMM Approaches for OFDM-Based Mixed-Numerology Systems

Mixed-numerology transmission is proposed to support a variety of communication scenarios with diverse requirements. However, as the orthogonal frequency division multiplexing (OFDM) remains as the basic waveform, the peak-to average power ratio (PAPR) problem is still cumbersome. In this paper, based on the iterative clipping and filtering (ICF) and optimization methods, we investigate the PAPR reduction in the mixed-numerology systems. We first illustrate that the direct extension of classical ICF brings about the accumulation of inter-numerology interference (INI) due to the repeated execution. By exploiting the clipping noise rather than the clipped signal, the noise-shaped ICF (NS-ICF) method is then proposed without increasing the INI. Next, we address the in-band distortion minimization problem subject to the PAPR constraint. By reformulation, the resulting model is separable in both the objective function and the constraints, and well suited for the alternating direction method of multipliers (ADMM) approach. The ADMM-based algorithms are then developed to split the original problem into several subproblems which can be easily solved with closed-form solutions. Furthermore, the applications of the proposed PAPR reduction methods combined with filtering and windowing techniques are also shown to be effective.

Guard Band Reduction for 5G and Beyond Multiple Numerologies

The existence of inter-numerology interference (INI) is a major drawback for the flexible multi-numerology frame structure proposed for the upcoming fifth generation New Radio (5G-NR). Insertion of a guard band (GB) between adjacent numerologies has been widely used in the literature as one of the effective ways to reduce the INI. However, the conventional way of implementing GBs is inefficient in terms of spectrum usage. In this letter, we exploit the inherent INI characteristics of the scalable multi-numerology structure to propose a more spectrally efficient way of implementing GBs. It is shown through simulations that the proposed GB insertion technique enhances the GB utilization up to 50% while achieving the same bit error rate performance as the conventionally implemented GB.

Inter-Numerology Interference Management With Adaptive Guards: A Cross-Layer Approach

The next-generation communication technologies are evolving towards increased flexibility in various aspects. Although orthogonal frequency division multiplexing (OFDM) remains as the waveform of the upcoming fifth-generation (5G) standard, the new radio provides flexibility in waveform parametrization (a.k.a. numerology) to address diverse requirements. However, managing the peaceful coexistence of mixed numerologies is challenging due to inter-numerology interference (INI). This paper proposes the utilization of adaptive guards in both time and frequency domains as a solution along with a multi-window operation in the physical (PHY) layer. The adaptive windowing operation needs a guard duration to reduce the unwanted emissions, and a guard band is required to handle the INI level on the adjacent band. The guards in both domains are jointly optimized with respect to the subcarrier spacing, use case (i.e., service requirement), and power offset between the numerologies. Also, the multi-window approach provides managing each side of the spectrum independently in case of an asymmetric interference scenario. Since the allowed interference level depends on the numerologies operating in the adjacent bands, the potential of adaptive guards is further increased and exploited with a medium access control (MAC) layer scheduling technique. The proposed INI-based scheduling algorithm decreases the need for guards by allocating the numerologies to the available bands, considering their subcarrier spacing, power level, and SIR requirements. Therefore, INI management is performed with a cross-layer (PHY and MAC) approach in this study. The results show that the precise design that accommodates such flexibility reduces the guards significantly and improves the spectral efficiency of mixed numerology systems.

PAPR Reduction With Mixed-Numerology OFDM

High peak-to-average power ratio (PAPR) is a critical problem in orthogonal frequency-division multiplexing (OFDM). The fifth-generation New Radio (5G NR) facilitates the utilization of multiple heterogeneous bandwidth parts (BWPs), which complicates the PAPR problem even further and introduces inter-numerology interference (INI) between the BWPs. This paper proposes two novel schemes to reduce the PAPR of mixed-numerology OFDM signals. The first scheme is an original enhanced iterative clipping-and-error-filtering (ICEF) approach that cancels efficiently the INI along with PAPR reduction. This allows to achieve efficient PAPR reduction while being compatible with well-known windowed overlap-and-add (WOLA) processing. The second scheme is based on fast-convolution (FC) processing, where PAPR reduction is embedded in the FC filtering carried out using overlapping processing blocks. This allows one to use any existing block-wise PAPR reduction method to reduce the composite signals' PAPR with arbitrary BWP waveforms, and it is thus especially well suited for processing mixed-numerology composite waveforms carrying multiple BWPs with different OFDM numerologies. The performance of the proposed algorithms is evaluated in the 5G NR context, and the essential performance advantages are demonstrated and quantified.

Reliability enhancement in multi-numerology-based 5G new radio using INI-aware scheduling

Multi-numerology waveform-based 5G new radio (NR) systems offer great flexibility for different requirements of users and services. However, there is a new type of problem that is defined as inter-numerology interference (INI) between multiple numerologies. This paper proposes novel scheduling and resource allocation techniques to enhance the overall reliability and also provide extra protection for ultra-reliable and low-latency communications (uRLLC) users and cell edge users against INI. Proposed methods are useful for Internet of Things (IoT) communications, and they do not cause additional spectral usage, computational complexity, and latency. Practical INI-aware schemes in this paper include fractional numerology domain (FND) scheduling, power difference-based (PDB) scheduling, and machine learning-based (MLB) scheduling algorithms. INI and signal-to-interference ratio (SIR) results for multi-numerology systems are obtained through computer simulations to show trade-offs between different scenarios and success of the proposed algorithms.

A Transceiver Design for Spectrum Sharing in Mixed Numerology Environments

We consider a mixed numerology spectrum sharing (SS) system where the users have different subcarrier spacing (SCS). Unlike in single numerology SS systems, mixed numerology SS systems suffer from inter-numerology interference (INI). We first derive the interference pattern and find that the variance of interference energy increases due to the difference in SCS. This increase in variance negatively affects decoding performance, since the interference energy is unbalanced between subcarriers. However, previous works on this issue did not take this interference pattern into account for interference management. In order to suppress the increase in variance of the interference energy, we propose a transceiver structure for large SCS users by using simple cyclic shift and frequency shift operations. The proposed transceiver disperses the effect of INI, reducing the variance in the interference energy. From the derived distribution characteristics of the interference energy in closed-form expressions, we show that the proposed transceiver achieves better decoding performance.

Inter-Numerology Interference Analysis and Cancellation for Massive MIMO-OFDM Downlink Systems

The extremely diverse service requirements is an important challenge for the upcoming fifth-generation (5G) wireless communication technologies. Orthogonal frequency division multiplexing (OFDM)-based massive multiple-input multiple-output (MIMO) and mixed numerologies transmission are proposed as solutions. This paper investigates the use of spatial multiplexing of users, sharing the same bandwidth, whose associated numerologies are different. We first introduce a precoding design that aims to manage the mixed numerologies spectrum sharing (SS) transmission. Then, we analyse the inter-numerology interference (INI) and derive the theoretical expressions of its radiation pattern in massive MIMO-OFDM downlink systems. We demonstrate that by using the proposed precoding scheme and considering two groups of users using two different numerologies, INI appears only in frequency selective channels. Besides, the transmission of users using numerology with large subcarrier spacing (SCS) is always with the best quality, only users using the numerology with small SCS suffer from INI. In that case, INI increases due to the difference in SCS, channel selectivity and power allocation. Based on the derived INI, in closed-form expressions, the precoding scheme is improved and a new INI cancellation scheme is introduced. Our analysis demonstrates that the INI theoretical model matches the simulation results, and the introduced INI cancellation based precoder efficiently mitigates the INI and enhances the performance of massive MIMO-OFDM downlink systems.

Inter-Numerology Interference for Beyond 5G

Fifth generation (5G) radio access technology (RAT) is designed to flexibly serve multiple services with extremely diverse requirements. One of the steps taken toward fulfilling this vision of 5G-RAT is the introduction of multi-numerology concept, where multiple frame structures with different subcarrier spacings coexist in one frequency band. Though efficient in providing the required flexibility, this approach introduces a new kind of interference into the system known as inter-numerology interference (INI). In this study, a novel cyclic prefix (CP) insertion technique (referred to as common CP) for multi-numerology system is mathematically analyzed in terms of the INI problem and its extensive comparison with the conventional CP configuration (referred to as individual CP) standardized for the 5G multi-numerology systems is presented. An in-depth discussion of various critical issues concerning multi-numerology system such as frequency domain multiplexing, time domain symbol alignment, and orthogonality between subcarriers of different numerologies is presented in the light of both, individual and common CP configurations. The analyses reveal that common CP has an advantage of restructuring the INI pattern in the system in a manner that paves the way for developing better techniques of avoiding or minimizing INI in the future generations.

Mixed Numerologies Interference Analysis and Inter-Numerology Interference Cancellation for Windowed OFDM Systems

Extremely diverse service requirements are one of the critical challenges for the upcoming fifth-generation (5G) radio access technologies. As a solution, mixed numerologies transmission is proposed as a new radio air interface by assigning different numerologies to different subbands. However, coexistence of multiple numerologies induces the inter-numerology interference (INI), which deteriorates the system performance. In this paper, a theoretical model for INI is established for windowed orthogonal frequency division multiplexing (W-OFDM) systems. The analytical expression of the INI power is derived as a function of the channel frequency response of interfering subcarrier, the spectral distance separating the aggressor and the victim subcarrier, and the overlapping windows generated by the interferer's transmitter windows and the victim's receiver window. Based on the derived INI power expression, a novel INI cancelation scheme is proposed by dividing the INI into a dominant deterministic part and an equivalent noise part. A soft-output ordered successive interference cancelation (OSIC) algorithm is proposed to cancel the dominant interference, and the residual interference power is utilized as effective noise variance for the calculation of log-likelihood ratios for bits. Numerical analysis shows that the INI theoretical model matches the simulated results, and the proposed interference cancelation algorithm effectively mitigates the INI and outperforms the state-of-the-art W-OFDM receiver algorithms.

A Flexibility Metric and Optimization Methods for Mixed Numerologies in 5G and Beyond

Mixed numerology-based frame structures will be a part of 5G systems in order to enhance overall flexibility and user satisfaction. However, inter-numerology interference, spectral efficiency reduction, complexity, and signaling overhead type of issues arise in such structures. It is needed to limit the number of numerologies used together. In this paper, a novel heuristic method is developed to find the efficient number of mixed numerologies. The proposed method aims to control overheads in systems using multi-numerology structures. Analysis of the trade-offs and relationships between different services and user requirements are also presented. The designed algorithm employs a new flexibility function and performance metric. Simulation results are shown for three different numerology sets, which include 5G numerologies.