Downlink Resource Allocation in Multiuser Cell-Free MIMO Networks With User-Centric Clustering

Ammar, Hussein A.; Adve, Raviraj; Shahbazpanahi, Shahram; Boudreau, Gary; Srinivas, Kothapalli Venkata

doi:10.1109/twc.2021.3104456

Cited by 59 publications

(51 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• Centralized [29]: we use a similar approach to the CUdistributed system but with some differences that include the use of the weighted sum rate objective function, i.e., SINR-based. The implication of this is the need for a centralized unit to gather all the information about the network and run the algorithm, which means that the scheme is not as scalable as the distributed schemes.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…We assume that the users are grouped such that the pilot sequences inside each group are orthogonal, while the same pilot sequences are used across groups. Specifically, as in [29], we use the hierarchical agglomerative clustering (HAC) algorithm to cluster the users into subsets each containing a number of users less than or equal to τ p , the length of the pilot sequence. The available orthogonal pilot sequences are randomly assigned to the users inside each subset.…”

Section: B Channel Estimationmentioning

confidence: 99%

See 1 more Smart Citation

Distributed Resource Allocation Optimization for User-Centric Cell-Free MIMO Networks

Ammar¹,

Adve²,

Shahbazpanahi³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

We develop two distributed downlink resource allocation algorithms for user-centric, cell-free, spatially-distributed, multiple-input multiple-output (MIMO) networks. In such networks, each user is served by a subset of nearby transmitters that we call distributed units or DUs. The operation of the DUs in a region is controlled by a central unit (CU). Our first scheme is implemented at the DUs, while the second is implemented at the CUs controlling these DUs. We define a hybrid quality of service metric that enables distributed optimization of system resources in a proportional fair manner. Specifically, each of our algorithms performs user scheduling, beamforming, and power control while accounting for channel estimation errors. Importantly, our algorithm does not require information exchange amongst DUs (CUs) for the DU-distributed (CU-distributed) system, while also smoothly converging. Our results show that our CU-distributed system provides 1.3-to 1.8-fold network throughput compared to the DU-distributed system, with minor increases in complexity and front-haul load -and substantial gains over benchmark schemes like local zero-forcing. We also analyze the trade-offs provided by the CU-distributed system, hence highlighting the significance of deploying multiple CUs in user-centric cell-free networks.

show abstract

Section: Numerical Resultsmentioning

confidence: 99%

Section: B Channel Estimationmentioning

confidence: 99%

Distributed Resource Allocation Optimization for User-Centric Cell-Free MIMO Networks

Ammar¹,

Adve²,

Shahbazpanahi³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Based on the maximum long-term average received power, a Voronoi-based user-centric clustering approach is proposed in dense cell-less networks [18]. In [19], user-centric clustering is implemented in a multiuser cell-free network with the objective of optimizing power allocation, user scheduling and beamforming. In the proposed work, a user is associated with a RRH if the RRH provides an average channel power above a predefined threshold.…”

Section: Related Workmentioning

confidence: 99%

Joint User-Centric Clustering and Multi-cell Radio Resource Management in Coordinated Multipoint Joint Transmission

Shami

Grace

Burr

et al. 2022

Wireless Pers Commun

View full text Add to dashboard Cite

Coordinated multipoint joint transmission (JT-CoMP) is a promising solution to address inter-cell interference in dense future wireless networks due its strength in converting interfering signals into useful signals, thereby enhancing capacity especially at the cell edge. However, allowing all user equipments (UEs) to operate using the JT-CoMP mode reduces the availability of radio resources. This paper develops an efficient algorithm that can identify which UEs will benefit from operating in a JT-CoMP mode and how to efficiently allocate radio resources from multiple base stations. Joint user-centric JT-CoMP clustering and multi-cell resource management is used in two steps where user-centric clusters are constructed as a first step and according to the clustering results obtained, resources are assigned. This paper also provides a new user-centric clustering approach that allows a user to utilize the JT-CoMP technique only if JT-CoMP boosts its rate above a certain threshold level. A multi-cell resource allocation scheme that can address the resource mismatching problem between cooperative BSs that happens due to load imbalance is proposed. Simulation results show that the proposed user-centric clustering algorithm outperforms the traditional power level difference scheme in terms of the system’s overall throughput as well as the throughput of cell-edge users. Also, results show that the performance of JT-CoMP is mainly affected by the user-centric approach and the amount of physical radio resources assigned to CoMP UEs.

show abstract

“…The data rate in such case is the summation of the individual data rates of the DUs. Generally, this mode provides smaller data rates than the coherent mode [19] because the user requires successive interference cancellation (SIC) to decode the data streams from the serving DUs; this makes the effective received signal power to be the sum of the signal powers received from the DUs [64]. Notably, the strict phase-synchronization requirement is not necessary in such mode, making the implementation easier on the transmitter side but at the expense of a far more complex receiver that implements SIC.…”

Section: A Transmission Modementioning

confidence: 99%

“…The main limitation is the capacity of the individual transmitters; in particular, serving users with distant transmitters occupies precious power and bandwidth resources but, due to high path loss, contributes little useful power at the user. Even with strict power policies that can limit the number of served users, the signaling needed to perform resource allocation would still not be scalable [1,19,20]. In this regard, it is better to define serving shows a wide interest in this topic that is a candidate architecture for future networks.…”

mentioning

confidence: 99%

User-centric Cell-free Massive MIMO Networks: A Survey of Opportunities, Challenges and Solutions

Ammar¹,

Adve²,

Shahbazpanahi³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Densification of network base stations is indispensable to achieve the stringent Quality of Service (QoS) requirements of future mobile networks. However, with a dense deployment of transmitters, interference management becomes an arduous task. To solve this issue, exploring radically new network architectures with intelligent coordination and cooperation capabilities is crucial. This survey paper investigates the emerging user-centric cell-free massive multiple-input multiple-output (MIMO) network architecture that sets a foundation for future mobile networks. Such networks use a dense deployment of distributed units (DUs) to serve users; the crucial difference from the traditional cellular paradigm is that a specific serving cluster of DUs is defined for each user. This framework provides macro diversity, power efficiency, interference management, and robust connectivity. Most importantly, the user-centric approach eliminates cell edges, thus contributing to uniform coverage and performance for users across the network area. We present here a guide to the key challenges facing the deployment of this network scheme and contemplate the solutions being proposed for the main bottlenecks facing cell-free communications. Specifically, we survey the literature targeting the fronthaul, then we scan the details of the channel estimation required, resource allocation, delay, and scalability issues. Furthermore, we highlight some technologies that can provide a management platform for this scheme such as distributed software-defined network (SDN) and self-organizing network (SON). Our article serves as a check point that delineates the current status and indicates future directions for this area in a comprehensive manner.

show abstract

Downlink Resource Allocation in Multiuser Cell-Free MIMO Networks With User-Centric Clustering

Cited by 59 publications

References 43 publications

Distributed Resource Allocation Optimization for User-Centric Cell-Free MIMO Networks

Distributed Resource Allocation Optimization for User-Centric Cell-Free MIMO Networks

Joint User-Centric Clustering and Multi-cell Radio Resource Management in Coordinated Multipoint Joint Transmission

User-centric Cell-free Massive MIMO Networks: A Survey of Opportunities, Challenges and Solutions

Contact Info

Product

Resources

About