User Centric Research Articles

Reducing Inter-CPU Coordination in User-Centric Distributed Massive MIMO Networks

This paper proposes a method for reducing inter-central processing unit (CPU) coordination in user-centric (UC) distributed massive multiple-input multiple-output (D-mMIMO) networks, also called cell-free networks. A two-stage heuristic approach is proposed to reduce the number of inter-coordinated user equipment (UE) (UEs connected to multiple CPUs) in each CPU. Firstly, each UE connects to a master access point (AP) and a primary CPU; then, AP selection is executed. Secondly, each CPU performs a fine-tuning algorithm to drop the worst inter-coordinated UEs. Simulation results reveal that one can reduce inter-CPU coordination while yielding slight degradation in spectral efficiency and improving energy efficiency.

Cognitive RF–FSO Fronthaul Assignment in Cell-Free and User-Centric mMIMO Networks

Cell-free massive MIMO (CF-mMIMO) network and its user-centric (UC) version are considered as promising techniques for the next generations of wireless networks. However, fronthaul and backhaul assignments are challenging issues in these networks. In this paper, energy efficiencies of uplink transmission for the CF- and UC-mMIMO networks are studied, wherein access points (APs) are connected to aggregation nodes (ANs) through radio frequency (RF) and/or free-space optic (FSO) fronthauls, and the ANs are connected to a central processing unit via fiber backhauls. The achievable data rates are derived by taking into account the effects of hardware non-ideality at the APs and ANs, FSO alignment and weather conditions. To have a robust and energy-efficient network, especially in the presence of FSO misalignment and adverse weather conditions, firstly, a cognitive RF--FSO fronthaul assignment algorithm is proposed at the cost of sharing the available RF bandwidth between the access and fronthaul links. Then, optimal power allocations at the users and APs are investigated, and two analytical approaches are proposed to solve the non-convex optimization problem. Through numerical results, we have discussed that the cognitive RF--FSO fronthaul assignment achieves higher energy efficiency compared to that of FSO-only, RF-only, or simultaneously using RF and FSO fronthauls.

A Review on Cell-Free Massive MIMO Systems

Cell-free massive multiple-input multiple-output (CF mMIMO) can be considered as a potential physical layer technology for future wireless networks since it can benefit from all the advantages of distributed antenna systems (DASs) and network MIMOs, such as macro-diversity gain, high channel capacity, and link reliability. CF mMIMO systems offer remarkable spatial degrees of freedom and array gains to mitigate the inherent inter-cell interference (ICI) of cellular networks. In such networks, several distributed access points (APs) together with precoding/detection processing can serve many users while sharing the same time-frequency resources. Each AP can be equipped with single or multiple antennas, and hence, can provide a consistently adequate service to all users regardless of their locations in the network. This paper presents a detailed overview of the current state-of-the-art on CF systems. First, it performs a literature review of the conventional CF and scalable user-centric (UC) CF mMIMO systems in terms of the limited capacity of the fronthaul links and the connection between APs and user equipments (UEs). As beyond networks will rely on higher frequency bands, it is of paramount importance to discuss the impact of beamforming techniques that are being investigated. Finally, some of the CF promising enabling technologies are presented to emphasize the main applications in these networks.

A Joint Multiservice Transmission Scheme for RSMA-Aided Cell-Free mMIMO System

In this letter, we propose a joint multi-service (MS) transmission scheme based on cell-free (CF) massive MIMO architecture. Based on the user-centric (UC) principle, we perform access point (AP) selection and form broadcast, multicast, and unicast messages at each AP according to the rate splitting multiple access (RSMA) technique, then we carry out the joint transmission of MS signals. In terms of system optimization, a novel three-step resource allocation algorithm is proposed to deal with the non-convex problem of achievable sum-rate maximization which downscales multidimensional variables to solve the one-dimensional problems of AP selection, intra-AP power ratio determination, and inter-AP power allocation in steps. We provide achievable sum-rate analyses backed by numerical results to show the superiority of the proposed scheme and optimization algorithm compared with the baselines.

Power Control in Cell-Free Massive MIMO Networks for UAVs URLLC Under the Finite Blocklength Regime

In this paper, we employ a user-centric (UC) cell-free massive MIMO (CFmMIMO) network for providing ultra reliable low latency communication (URLLC) when traditional ground users (GUs) coexist with unmanned aerial vehicles (UAVs). We study power control in both the downlink and the uplink when partial zero-forcing (PZF) transmit/receive beamforming and maximum ratio transmission/combining are utilized. We consider optimization problems where the objective is to maximize either the users’ sum URLLC rate or the minimum user’s URLLC rate. The URLLC rate function is both complicated and nonconvex rendering the considered optimization problems nonconvex. Thus, we propose two approximations for the complicated URLLC rate function and employ successive convex optimization (SCO) to tackle the considered optimization problems. Specifically, we propose the SCO with iterative concave lower bound approximation (SCO-ICBA) and the SCO with iterative interference approximation (SCO-IIA). We provide extensive simulations to evaluate SCO-ICBA and SCO-IIA and compare UC CFmMIMO deployment with traditional colocated massive MIMO (COmMIMO) systems. The obtained results reveal that employing the SCO-IIA scheme to optimize the minimum user’s rate for CFmMIMO with MRT in the downlink, and PZF reception in the uplink can provide the best URLLC rate performances.

Joint association and beamforming optimization in reconfigurable intelligent surface-enhanced user-centric networks

Fully coordinated Cell-Free (CF) networks can alleviate the Inter-Cell Interference (ICI) for the cell-edge users in cellular networks. Due to the complex topology of the association between the Access Points (APs) and the users in CF networks, it is challenging to deploy CF networks in practical scenarios. In order to make CF networks feasible, we introduce User-Centric (UC) networks enabling each user served by a limited number of APs. As a low-cost and energy-efficient technology, Reconfigurable Intelligent Surface (RIS) can be embedded in UC networks to further improve the system performance. First, we provide a brief survey on the prior works in UC networks for clear comprehension. Then, we formulate a Spectral Efficiency (SE) maximization problem for RIS-enhanced UC networks. For solving the non-convex problem, we divide it into three subproblems and propose a joint optimization framework for optimizing AP-user association, active beamforming of multiple antennas at the APs, and the passive beamforming of the RIS. Besides, a channel gain based association method coupled with the design of RIS is proposed to construct a dynamic and efficient association. The subproblems about optimizing active and passive beamforming are solved with the fractional programming. Simulation results show that the proposed joint optimization framework for RIS-enhanced UC networks can obtain good SE compared with other benchmark schemes.

A successful transmission probability based AP clustering scheme in user-centric cell-free networks

Cell-free network is a promising architecture with numerous merits in energy efficiency and macro diversity, which is easy and flexible to integrate with other communication technologies. However, its current network topology where access points (APs) are connected to a central processing unit (CPU) to jointly serve the users, causes huge burden to the fronthaul network. To deal with this problem, in this paper, we first combine thoughts in user-centric (UC) network where users are served by selected subset of APs. Then, we propose a successful transmission probability (STP) based AP clustering scheme to reduce the fronthaul capacity requirement (FCR). By using stochastic geometry and proper approximation methods, the approximated STP calculation expression is derived. Numerical simulations demonstrate that the obtained STP expression can provide a tight approximation compared to Monte Carlo simulation results under different system parameters while keeping the computation tractable. Furthermore, the relationship between the FCR and the STP threshold is formulated as a clustering optimization problem, which gives insights on clustering design in UC-CF network systems. We show by simulation results that the proposed scheme requires less fronthaul capacity than the original CF approach while ensuring the STP performance.

Downlink Resource Allocation With Pilot Length Optimization for User-Centric Cell-Free MIMO Networks

Pilot length is a crucial parameter in cell-free multiple-input multiple-output (MIMO) networks, which could significantly affect the downlink throughput. In this letter, we jointly optimize the user scheduling, power allocation and pilot length for user-centric (UC) cell-free MIMO networks. We consider the frequency band is uniformly split into several sub-channels. As the number of users scheduled on one sub-channel is commonly small, orthogonal pilot sequences are adopted among users on each sub-channel to avoid pilot contamination. To achieve user fairness, we aim to maximize the minimum ergodic user rate in the downlink transmission, yielding a challenging mix-integer non-convex optimization. Inspired by the weighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> -norm approximation in compressive sensing together with successive convex approximation (SCA), an iterative scheme is then proposed to solve the problem. Simulation results demonstrate that the proposed scheme outperforms its state-of-the-art counterpart, which is a three-step algorithm including user grouping, sub-channel allocation, and power optimization.

Energy Efficiency and Resource Allocation Optimization with MIMONOMA and Backhaul Beam-forming in User-centric Ultra-dense Networks

Background: Non-orthogonal multiple access (NOMA) is viewed as the key multiple access technology for 5G and beyond networks, attracting the attention of academics and industries. NOMA and the multiple input multiple output (MIMO-NOMA) technology can improve a system’s throughput, latency, and energy efficiency (EE) in future-generation communication networks. Objective: The objective of this paper is to achieve maximum EE by applying the Max-min Power Control Algorithm (MMPCA) through sub-channel optimization, resource allocation (RA) optimization, access point selection (APS), and user association. The EE results obtained with and without using MMPCA are compared to the RA optimization from a conventional water-filling algorithm (WFA). Method: This paper formulates a framework for user-centric (UC) joint resource allocation, such as backhaul connection via beam-forming and Access point (AP) to user connection via MIMO-NOMA. The user without interference is decoded using the NOMA principle. The MMPCA was also used to optimize cooperative power allocation, sub-channel allocation, and efficient user association. The RA for EE is framed as a mixed non-convex and non-linear function using successive convex approximation and sum ratio decoupling converted into convex and linear. A bisection method was used to achieve optimal RA, user association, and sub-channel assignment. Results and Conclusion: The simulation shows energy efficiency (EE) improvement. Similarly, it is observed that MMPCA outperforms the WFA.

Proportional-Fair Resource Allocation for User-Centric Networks

Considering the computational complexity and fronthaul capacity requirements of network-wide centralized optimization for Cell-free (CF) massive multiple input multiple output (MIMO) networks, we focus on user-centric (UC) networks wherein each user is served by a subset of access points (APs) rather than all the APs. We study a proportional-fair resource allocation (RA) problem, including slot resource allocation and precoding design in UC networks over consecutive time-slots. We formulate the proportional-fair (PF) resource allocation problem as a series of weighted sum-rate maximization problems and develop a two-stage heuristic RA scheme to solve the problem. The scheme adopts a user grouping process to decompose resource allocation problem into intra-group orthogonal resource allocation sub-problems. A modularity-based user grouping algorithm upon a constructed weighted digraph is proposed. Then, relying on the grouping results, a parallel distributed PFRA algorithm consisting of user selection process and a Signal to Leakage plus Noise Ratio (SLNR)-based precoding design is proposed to fulfil intra-group resource allocation. Rather than global channel state information (CSI) collected from the whole network, local CSI obtained cooperatively within each AP subset is utilized in the proposed resource allocation scheme. Numerical results confirm that the proposed RA scheme outperforms in throughput while maintaining comparable fairness among users.

Deep Contextual Bandits for Fast Neighbor-Aided Initial Access in mmWave Cell-Free Networks

Access points (APs) in millimeter-wave (mmWave) and sub-THz-based user-centric (UC) networks will have sleep mode functionality. As a result of this, it becomes challenging to solve the initial access (IA) problem when the sleeping APs are activated to start serving users. In this paper, a novel deep contextual bandit (DCB) learning method is proposed to provide instant IA using information from the neighboring active APs. In the proposed approach, beam selection information from the neighboring active APs is used as an input to neural networks that act as a function approximator for the bandit algorithm. Simulations are carried out with realistic channel models generated using the Wireless Insight ray-tracing tool. The results show that the system can respond to dynamic throughput demands with negligible latency compared to the standard baseline 5G IA scheme. The proposed fast beam selection scheme can enable the network to use energy-saving sleep modes without compromising the quality of service due to inefficient IA

Resource Allocation in User-Centric Optical Wireless Cellular Networks Based on Blind Interference Alignment

Visible light communications (VLC) have been recently proposed to enhance the capacity of next generation of wireless services. Moreover, VLC networks usually comprise a large number of overlapping optical access points (APs). Moreover, each of these APs provides a small and confined area of coverage in order to generate satisfactory illumination. In this work, a user-centric (UC) clustering formation based on the K-means algorithm is proposed to manage the inter-cell interference (ICI) and enhance the performance of VLC networks. Moreover, assuming that each user is equipped with a reconfigurable photodetector, the use of blind interference alignment (BIA) in each UC cluster is considered. Notice that the data rate demands are not the same for all the users. We formulate an optimization problem to maximize the utility of the network resources allocated to the users based on their demands. After that, a centralized algorithm is proposed to obtain an optimal solution through exhaustive search, which is subject to high complexity. To reduce the complexity of this optimization problem, the problem is divided into sub-problems based on the number of constructed UC clusters. Then, a distributed algorithm via Lagrangian multipliers is proposed within each UC cluster with the aim of providing a near optimal solution to the centralized algorithm. Simulation results demonstrate that the proposed resource allocation algorithms provide higher performance than a uniform resource allocation scheme among users.

SpiderNet: Spectrally Efficient and Energy Efficient Data Aided Demand Driven Elastic Architecture for 6G

Legacy base station (BS) centric cellular architecture is marked by tight interlock between spectral efficiency (SE) and energy efficiency (EE). That means, unless new degrees of freedom and intelligent dynamic adaptability is added, any significant gain in SE must come at the cost of EE. Even the most predominant approaches for capacity enhancement such as network densification yield gains in SE at the cost of increased energy consumption. Moreover, in future mobile networks, the key challenge is not only the rampantly growing volume of traffic, but also the spatiotemporal variability of the traffic. These observations call for a paradigm shift in the way cellular networks are designed and operated. Therefore, in this work, we propose a new cellular architecture called SpiderNet: Spectrally Efficient and Energy Efficient Data Aided Demand Driven Elastic Architecture for 6G Wireless Networks. The key idea behind SpiderNet is to introduce additional degrees of freedom to relax the tight coupling between the SE and EE and database-aided intelligence to enable dynamic adaptive operation for simultaneous enhancement of both SE and EE. This goal is achieved by shifting the pivot of operation from the rigid always ON BS centric cells to user centric (UC) on demand cells. The SpiderNet architecture consists of a layer of low-density large footprint and database aided control BS underlayed by high-density switchable data BS. This database enables artificial intelligence (AI) powered proactive dynamic orchestration of UC cells to maximize not only SE and EE, but quality of experience (QoE) as well. We also identify the challenges that arise in the practical realization of SpiderNet and propose solutions. Finally, we present a case study that compares SpiderNet performance with legacy HetNets. The results show that compared to current BS centric cellular architecture, SpiderNet can substantially enhance both SE and EE without compromising QoE.

On the optimal energy efficiency and spectral efficiency trade-off of CF massive MIMO SWIPT system

Energy efficiency is a key requirement for future network design, and user-centric (UC) cell-free (CF) massive multi-input multi-output (MIMO) networks can achieve over ten times the energy efficiency. Based on this, this paper studies a CF MIMO simultaneous wireless information and power transmission system and proposes a UC access point (AP) selection method and a trade-off performance optimization scheme for spectral efficiency and energy efficiency. In this system, users have both energy recovery and information transmission functions. According to the difference between the interference in the energy harvesting and information transmission process, a flexible AP selection scheme is designed. Blindly pursuing high spectral efficiency will result in waste of resources. This paper proposes an evaluation index that takes into account both energy efficiency and spectral efficiency, analyses the trade-off between energy efficiency and spectral efficiency, and jointly optimizes the AP selection scheme and the uplink (UL) and downlink (DL) time switching ratio to maximize the trade-off performance. Then, the non-convex problem is converted to a geometric planning problem to solve. The simulation results show that by implementing a suitable AP selection scheme and UL and DL time allocation, the information processing scheme on the AP side has a slight loss in spectral efficiency, but the energy efficiency is close to the performance of global processing on the central processing unit.

Heterogeneous User-Centric Cluster Migration Improves the Connectivity-Handover Trade-Off in Vehicular Networks

User-centric (UC) clustering has recently emerged as a promising paradigm for enhancing the connectivity of mobile users by grouping an appropriate number of access points (APs), thus paving the way for seamlessly connected vehicular networks. However, for a high-velocity vehicular user, UC clustering may lead to overly frequent handovers (HOs), which increases the risk of throughput-reduction, call dropping and energy wastage. To mitigate this problem, we aim for reducing the HO overhead imposed on the heterogeneous UC (HUC) cluster migration process of vehicular networks. Specifically, we first conceive a novel hybrid HUC cluster migration strategy that adaptively switches between horizontal and vertical HOs for supporting both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. Then, a dynamic decision-making problem is formulated for balancing the benefits of HUC cluster migration and the total HO overhead, subject to realistic HUC clustering constraints. In the face of unknown vehicular mobility, we propose a sequential HUC cluster migration solution based on max-bipartite matching theory imposing a low complexity. As a design alternative, we also propose a holistic solution relying on model-free deep reinforcement learning (DRL). Finally, our numerical results reveal the superiority of the proposed cluster migration design in terms of striking a compelling trade-off between the per-user average data rate (PAR) and the number of HOs in different scenarios.

A Resource-Efficient Approach on User Association in 5G Networks Using Downlink and Uplink Decoupling

A user-centered network model can significantly optimize connectivity issues between a user and the corresponding base station (BS). This article shall evaluate the user-centric (UC) model targeted for Fifth Generation telecommunication systems and will attempt to optimize communication between users and BSs. The authors suggest a resource-aware mechanism that targets improving coverage through the network decoupling into two separate and independent uplink and downlink networks. The mechanism shall fully respect each user's initially requested throughput demands and aims to solve the network user BS association problem with efficient resource management techniques. Simulations revealed that the mechanism perfectly preserves quality of service (QoS) and offers increased data rates in favor of ultimate user coverage, in both scenarios. Additionally, Frequency Range 2 offers an increased amount of resources, both increased data rates and higher amounts of devices that are covered by the overall network.

Graph-based multi-user scheduling for indoor cooperative visible light transmission.

Recent progress in the indoor visible light communication (VLC) has shown promising signs of alleviating an increasing strain on the radio frequency spectrum and enhancing transmission capacity. Nevertheless, the indoor VLC usually suffers from inter-channel interference (ICI) due to the dense light-emitting diode (LED) deployment. The ICI is considered as a key factor affecting signal to interference and noise ratio (SINR) and spectral efficiency. To address this challenge, an efficient multi-user scheduling framework that employs interference coordination and cooperative transmission is investigated based on the graph theory. To effectively mitigate ICI and maximize benefit of the cooperative transmission, the cell-centric (CC) and user-centric (UC) clustering are introduced for cooperative transmission. For the CC clustering, the multi-user scheduling problem under the proportional fairness criterion is formulated to maximize spectral efficiency while ensuring user fairness. Such a problem is solved by linear programming and greedy algorithms after transforming it into a maximum weighted independent set problem with the aid of a modified interference graph. For the UC clustering, the multi-user scheduling problem under the max-min criterion is formulated and solved by a proposed heuristic approach based on the bipartite graph theory. Numerical results show that the proposed graph-based scheduling is capable of providing up to 7.7 dB gain in SINR over the non-cooperative transmission. The bipartite graph scheduling offers high spectral efficiency and service fairness index. In the worst case with an occlusion probability of 1, a small SINR penalty of up to 1 dB is observed with the greedy algorithm. It is shown that the graph-based scheduling is robust to receiver rotation and occlusion in terms of spectral efficiency, SINR, and user fairness.

Efficient Angle-Domain Processing for FDD-Based Cell-Free Massive MIMO Systems

Cell-free massive MIMO communications is an emerging network technology for 5G wireless communications wherein distributed multi-antenna access points (APs) serve many users simultaneously. Most prior work on cell-free massive MIMO systems assume time-division duplexing mode, although frequency-division duplexing (FDD) systems dominate current wireless standards. The key challenges in FDD massive MIMO systems are channel-state information (CSI) acquisition and feedback overhead. To address these challenges, we exploit the so-called angle reciprocity of multipath components in the uplink and downlink, so that the required CSI acquisition overhead scales only with the number of served users, and not the number of AP antennas nor APs. We propose a low complexity multipath component estimation technique and present linear angle-of-arrival (AoA)-based beamforming/combining schemes for FDD-based cell-free massive MIMO systems. We analyze the performance of these schemes by deriving closed-form expressions for the mean-square-error of the estimated multipath components, as well as expressions for the uplink and downlink spectral efficiency. Using semi-definite programming, we solve a max-min power allocation problem that maximizes the minimum user rate under per-user power constraints. Furthermore, we present a user-centric (UC) AP selection scheme in which each user chooses a subset of APs to improve the overall energy efficiency of the system. Simulation results demonstrate that the proposed multipath component estimation technique outperforms conventional subspace-based and gradient-descent based techniques. We also show that the proposed beamforming and combining techniques along with the proposed power control scheme substantially enhance the spectral and energy efficiencies with an adequate number of antennas at the APs.

Channel Hardening in Cell-Free and User-Centric Massive MIMO Networks With Spatially Correlated Ricean Fading

The irruption of the cell-free (CF) massive multiple-input multiple-output (MIMO) network topology has meant taking one step further the concept of massive MIMO as a means to provide uniform service in large coverage areas. A key property of massive MIMO networks is channel hardening, by which the channel becomes deterministic when the number of antennas grows large enough relative to the number of serviced users, easing the signal processing and boosting the performance of simple precoders. However, in CF massive MIMO, the fulfillment of this condition depends on several aspects that are not considered in classical massive MIMO systems. In this work, we address the presence of channel hardening in both CF massive MIMO and the recently appeared user-centric (UC) approach, under a spatially correlated Ricean fading channel using distributed and cooperative precoding and combining schemes and different power control strategies for both the downlink (DL) and uplink (UL) segments. We show that the line-of-sight (LOS) component, spatially correlated antennas and UC schemes have an impact on how the channel hardens. In addition, we examine the existent gap between the estimated achievable rate and the true network performance when channel hardening is compromised. Exact closed-form expressions for both the hardening metric and achievable DL/UL rates are given as well.

User-Centric 5G Cellular Networks: Resource Allocation and Comparison With the Cell-Free Massive MIMO Approach

Recently, the so-called cell-free (CF) massive multiple-input multiple-output (MIMO) architecture has been introduced, wherein a very large number of distributed access points (APs) simultaneously and jointly serve a much smaller number of mobile stations (MSs). The paper extends the CF approach to the case in which both the APs and the MSs are equipped with multiple antennas, proposing a beamfoming scheme that, relying on the zero-forcing strategy, does not require channel estimation at the MSs. We contrast the originally proposed formulation of CF massive MIMO with a user-centric (UC) approach wherein each MS is served only by a limited number of APs. Exploiting the framework of successive lower-bound maximization, the paper also proposes and analyzes power allocation strategies aimed at either sum-rate maximization or minimum-rate maximization, both for the uplink and downlink. Results show that the UC approach, which requires smaller backhaul overhead and is more scalable that the CF deployment, also achieves generally better performance than the CF approach for the vast majority of the users, especially on the uplink.