Abstract-This paper investigates the capacity and energy consumption metrics of small-cell networks that are enabled with sleep mode (SL) functionality. A novel method is introduced to systematically and accurately identify the potential SL cells that can maximize the spectrum reuse efficiency without the need for an exhaustive search. The performance of the proposed technique is assessed and compared with the always-on approach and an optimal benchmark. The results show that the proposed method significantly outperforms the always-on system and approaches the performance of the optimal benchmark with notably reduced computational burden.

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The rapid increase of wireless applications and services coupled with the arrival of 5G and Internet of Things will not only exacerbate demand for further capacity at the downlink (DL) but also crucially at the uplink (UL). One of the most potential enablers to simultaneously optimize both links is the DL/UL decoupling (DUDe) technique which does so by exploiting the possibility of associating each user to a different base station (BS) in each link direction. Moreover, the increasing desire to incorporate millimeter-wave (mmWave) communications in future networks further enriches the possibilities to achieve higher capacities. To this end and in contrast to existing works which use dual association based on minimum path-loss (Min-PL), this paper investigates the merits of adopting capacity-based multi-association in ultra-high frequency (UHF) and mmWave hybrid networks, where mobile users may simultaneously connect to multiple UHF small cells (SCells), millimeter-wave SCells and/or UHF macro cells (MCells). It will be shown that, our joint association and resource management approach can provide higher data rates and energy efficiency than many benchmark techniques. To achieve an insight into the performance of the proposed design, the results present a comprehensive comparison based on single and multi-connectivity as well as coupled and decoupled association for a variety of important metrics.

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