arXiv subjects

Cicek Cavdar

Publications and source records attributed to Cicek Cavdar.

77 records · Page 5Linked to original sources

arXiv2017

Grant-free Radio Access IoT Networks: Scalability Analysis in Coexistence Scenarios

IoT networks with grant-free radio access, like SigFox and LoRa, offer low-cost durable communications over unlicensed band. These networks are becoming more and more popular due to the ever-increasing need for ultra durable, in terms of battery lifetime, IoT networks. Most studies evaluate the system performance assuming single radio access technology deployment. In this paper, we study the impact of coexisting competing radio access technologies on the system performance. Considering $\mathpzc K$ technologies, defined by time and frequency activity factors, bandwidth, and power, which share a set of radio resources, we derive closed-form expressions for the successful transmission probability, expected battery lifetime, and experienced delay as a function of distance to the serving access point. Our analytical model, which is validated by simulation results, provides a tool to evaluate the coexistence scenarios and analyze how introduction of a new coexisting technology may degrade the system performance in terms of success probability and battery lifetime. We further investigate solutions in which this destructive effect could be compensated, e.g., by densifying the network to a certain extent and utilizing joint reception.

cs.IT↗

arXiv2015

Energy Efficiency of Massive MIMO: Coping with Daily Load Variation

Massive MIMO is a promising technique to meet the exponential growth of mobile traffic demand. However, contrary to the current systems, energy consumption of next generation networks is required to be load adaptive as the network load varies significantly throughout the day. In this paper, we propose a load adaptive multi-cell massive MIMO system where each base station (BS) adapts the number of antennas to the daily load profile (DLP) in order to maximize the downlink energy efficiency (EE). In order to incorporate the DLP, the load at each BS is modeled as an M/G/m/m state dependent queue under the assumption that the network is dimensioned to serve a maximum number of users at the peak load. The EE maximization problem is formulated in a game theoretic framework where the number of antennas to be used by a BS is determined through best response iteration. This load adaptive system achieves around 24% higher EE and saves around 40% energy compared to a baseline system where the BSs always run with the fixed number of antennas that is most energy efficient at the peak load and that can be switched off when there is no traffic.

cs.NI↗

arXiv2015

Energy-Efficient Load-Adaptive Massive MIMO

Massive MIMO is a promising technique to meet the exponential growth of global mobile data traffic demand. However, contrary to the current systems, energy consumption of next generation networks is required to be load adaptive as the network load varies significantly throughout the day. In this paper, we propose a load adaptive massive MIMO system that varies the number of antennas following the daily load profile (DLP) in order to maximize the downlink energy efficiency (EE). A multi-cell system is considered where each base station (BS) is equipped with a large number of antennas to serve many single antenna users. In order to incorporate DLP, each BS is modeled as an M/G/m/m state dependent queue under the assumption that the network is dimensioned to serve a maximum number of users at the peak load. For a given number of users in a cell, the optimum number of active antennas maximizing EE is derived. The EE maximization problem is formulated in a game theoretic framework where the number of antennas to be used by a BS is determined through best response iteration. This load adaptive system achieves overall 19% higher EE compared to a baseline system where the BSs always run with the fixed number of antennas that is most energy efficient at peak load and that can be switched-off when there is no traffic

cs.NI↗

arXiv2015

Energy saving market for mobile operators

Ensuring seamless coverage accounts for the lion's share of the energy consumed in a mobile network. Overlapping coverage of three to five mobile network operators (MNOs) results in enormous amount of energy waste which is avoidable. The traffic demands of the mobile networks vary significantly throughout the day. As the offered load for all networks are not same at a given time and the differences in energy consumption at different loads are significant, multi-MNO capacity/coverage sharing can dramatically reduce energy consumption of mobile networks and provide the MNOs a cost effective means to cope with the exponential growth of traffic. In this paper, we propose an energy saving market for a multi-MNO network scenario. As the competing MNOs are not comfortable with information sharing, we propose a double auction clearinghouse market mechanism where MNOs sell and buy capacity in order to minimize energy consumption. In our setting, each MNO proposes its bids and asks simultaneously for buying and selling multi-unit capacities respectively to an independent auctioneer, i.e., clearinghouse and ends up either as a buyer or as a seller in each round. We show that the mechanism allows the MNOs to save significant percentage of energy cost throughout a wide range of network load. Different than other energy saving features such as cell sleep or antenna muting which can not be enabled at heavy traffic load, dynamic capacity sharing allows MNOs to handle traffic bursts with energy saving opportunity.

cs.NI↗

arXiv2014

Dimensioning of PA for massive MIMO system with load adaptive number of antennas

This paper takes into consideration the non-ideal efficiency characteristics of realistic power amplifiers (PAs) along with the daily traffic profile in order to investigate the impact of PA dimensioning on the energy efficiency (EE) of load adaptive massive MIMO system. A multicellular system has been considered where each base station (BS) is equipped with a large number of antennas to serve many single antenna users. For a given number of users in a cell, the optimum number of active antennas maximizing EE has been derived where total BS downlink power is assumed to be fixed. Under the same assumption, the PAs have been dimensioned in a way that maximizes network EE not only for a single time snapshot but over twenty four hours of operation while considering dynamic efficiency characteristics of the PAs. In order to incorporate this daily load profile, each BS has been modeled as an M/G/m/m state dependent queue under the assumption that the network is dimensioned to serve a maximum number of users at a time corresponding to 100% cell traffic load. This load adaptive system along with the optimized PA dimensioning achieves 30% higher energy efficiency compared to a base line system where the BSs always run with a fixed number of active antennas which are most energy efficient while serving 100% traffic load.

cs.NI↗