An overview of cell association in heterogeneous network: Load balancing and interference management perspective

Chinipardaz, Maryam; Rasti, Mehdi; Nourhosseini, M.

doi:10.1109/istel.2014.7000895

Cited by 11 publications

(20 citation statements)

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“…Another approach in this group is the Reference Signal Received Power (RSRP)‐based method, in which the BS with the maximum received power of a reference signal is the one selected for association. As shown in Chinipardaz et al, the SINR method often represents the channel quality much better than the RSRP approach does. Also belonging to this group, the Reference Signal Received Quality (RSRQ)‐based scheme is a strategy in which each user is assigned to the BS that maximizes the RSRQ metric (that is, the RSRP divided by the aggregate received power).…”

Section: Related Workmentioning

confidence: 99%

“…User assignment in UDNs is different from that in conventional macro‐cell networks 32,36. Similarly, the problem in HetNets or multitier networks—in which “small,” lower power BSs (pico, femto), with usually unplanned deployment, are embedded within a conventional well‐planned macrocell deployment21—is also different from that of macrocell networks 26…”

Section: Related Workmentioning

confidence: 99%

“…Specifically, these are metrics based on the signal strength measured on a load‐independent reference pilot signal sent by each BS . One of these metrics is the aforementioned SINR, which leads to the SINR‐based user‐cell association scheme, usually considered as the reference method . In this strategy, each user is assigned to the BS for which such a user has the maximum (best) SINR.…”

Section: Related Workmentioning

confidence: 99%

“…Also belonging to this group, the Reference Signal Received Quality (RSRQ)‐based scheme is a strategy in which each user is assigned to the BS that maximizes the RSRQ metric (that is, the RSRP divided by the aggregate received power). Basically, the RSRQ‐based scheme is similar to the SINR‐based method . See 3GPP for further details about these approaches.…”

Section: Related Workmentioning

confidence: 99%

“…Regardless of the terminology used, user‐cell association in multicell networks usually consider 2 metrics : instantaneous achievable rate (at the physical layer) and cell load 24. Because the instantaneous bit rate is computed from the received signal‐to‐interference‐plus‐noise ratio (SINR), the simplest (and thus widely accepted) user assignment scheme is the “SINR‐based” method, usually considered as the reference method in industry . In the particular case of LTE, this conventional approach (CA) assigns users to eNBs based on the channel quality indicator (CQI) parameter, directly related to SINR .…”

Section: Introductionmentioning

confidence: 99%

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Near‐optimal user assignment in LTE mobile networks with evolutionary computing

Arco-Vega

Cuadra

Portilla-Figueras

et al. 2016

Trans. Emerging Tel. Tech.

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This paper tackles the problem of users assignment to evolved NodeBs in long‐term evolution mobile networks. We propose a model that aims at assigning users by minimizing a function called download time of the complete system, defined as the minimum time required for all the users in the system to complete their downloads. This strategy helps the network use its resources in a more balanced way, by assigning users from cells (which otherwise would be overloaded when using a simple signal‐to‐interference‐plus‐noise ratio‐based approach) to others with less load. The user‐cell association problem is, in general, a combinatorial NP hard problem. Although other approaches indirectly tackle this complexity by transforming the problem into another whose optimal solution is less difficult (but still with very high computational cost), we tackle directly our download time of the complete system minimization problem by finding an approximate solution provided by a specific implementation of an evolutionary algorithm, which is based on a long‐term evolution domain‐specific initial population along with fully tailored operators adapted to this assignment problem. A variety of experiments in different realistic scenarios point out that the proposed method outperforms the conventional signal‐to‐interference‐plus‐noise ratio‐based and load balancing approaches, specially in urban and dense urban environments (where the assignment is more critical in terms of capacity). We have also tested our approach in simple scenarios that mimic heterogeneous networks and ultra‐dense networks in which the number of users is similar to that of nodes, proving its beneficial properties. Our method is aimed at the design/dimensioning stage and not to the operation stage, and in this sense, it is an off‐line algorithm that could be useful to make better decisions on cost‐effective network dimensioning and upgrading.

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Section: Related Workmentioning

confidence: 99%