A logistic regression approach to location classification in OFDMA-based FFR systems

Thampi, Ajay; Armour, Simon; Fan, Zhong; Kaleshi, Dritan

doi:10.1109/wowmom.2013.6583376

Cited by 3 publications

(4 citation statements)

References 15 publications

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“…We call such a system CFFR-LR. In [34], we have shown that using two features, namely, received power and SINR from the MS, the classification accuracy is much better than using a single feature such as SINR [6,[35][36][37]. We have also shown that the optimum hypothesis is a logistic/sigmoid function, given by (48), based on an affine model.…”

Section: Static Clustering With Adaptive Ffrmentioning

confidence: 98%

“…Steps (39), (40), (41) and (42) can be obtained from (33), (34), (35) and (32), respectively. By substituting (42) in (29), we get…”

Section: Capacity Of Lmmsementioning

confidence: 99%

“…In this paper, however, we consider a machine-learning approach of logistic regression for location classification. We extend an algorithm that we proposed in [34] for a single cell to a cluster scale. We call such a system CFFR-LR.…”

Section: Static Clustering With Adaptive Ffrmentioning

confidence: 99%

“…In (48), z (k) is given by (49), where w 2 is the SINR reported by user k and θ 0 , θ 1 and θ 2 are the optimum parameters determined through the training process. More details on this can be found in [34].…”

Section: Static Clustering With Adaptive Ffrmentioning

confidence: 99%

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Mitigating inter-cluster interference on the uplink for a three-cell clustered cooperative network

Thampi

Armour

Fan

et al. 2016

J Wireless Com Network

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In this paper, we propose a practical and scalable solution to mitigating interference on the uplink through static clustering and adaptive fractional frequency reuse (CFFR). The focus is on a three-cell clustered network due to its low complexity. Moreover, we have previously shown that a performance comparable to that of global coordination is achievable using a cluster size of three. In this paper, for a clustered planar Wyner network, we derive analytical capacity equations for zero forcing (ZF) and linear minimum mean squared error (LMMSE)-based receivers. The theoretical results show that inter-cluster interference is the major performance bottleneck and that the smallest interference from the neighbouring clusters is sufficient to significantly lower the system performance. We then switch our study to a more realistic network setting and augment our CFFR technique by adopting an entirely distributed architecture and by implementing a location classification algorithm based on logistic regression. We then show through simulations that CFFR performs significantly better than the widely studied dynamic clustering (DC) technique. Since the inter-cluster interference intensity of CFFR is much lower than DC, the per-cell sum rate performance is 1.5× better, especially at high loads. We also show that the CFFR algorithm is a lot less complex than DC in terms of running time.

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