Large System Analysis of Base Station Cooperation for Power Minimization

This paper focuses on developing a decentralized framework for coordinated minimum power beamforming wherein L base stations (BSs), each equipped with N antennas, serve K single-antenna users with specific rate constraints. This is realized by considering user specific intercell interference (ICI) strength as the principal coupling parameter among BSs. First, explicit deterministic expressions for transmit powers are derived for spatially correlated channels in the asymptotic regime in which N and K grow large with a non-trivial ratio K/N . These asymptotic expressions are then used to compute approximations of the optimal ICI values that depend only on the channel statistics. By relying on the approximate ICI values as coordination parameters, a distributed non-iterative coordination algorithm, suitable for large networks with limited backhaul, is proposed. A heuristic algorithm is also proposed relaxing coordination requirements even further as it only needs pathloss values for non-local channels.The proposed algorithms satisfy the target rates for all users even when N and K are relatively small.Finally, the potential benefits of grouping users with similar statistics are investigated to further reduce the overhead and computational effort of the proposed solutions. Simulation results show that the proposed methods yield near-optimal performance. Index TermsLarge scale antenna arrays, MIMO cellular networks, large system analysis, power minimization, distributed multicell beamforming.

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“…which along with (28) l)). On the other hand, we notice thatm b M ,M (−(1 + l)) at l = 0 is equal tom b M ,M in (24)…”

Section: Appendix III Proof Of Theoremmentioning

confidence: 82%

“…The authors in [20]- [24] perform such analysis under i.i.d. Rayleigh fading channels in single-cell [20], [21] and multicell [22]- [24] settings. The impact of spatial correlation on the asymptotic power assignment is studied in [25] for a single-cell scenario with UEs experiencing identical correlation matrices.…”

Section: A Prior Workmentioning

confidence: 99%

Decentralizing Multicell Beamforming via Deterministic Equivalents

Asgharimoghaddam

Tölli

Sanguinetti

et al. 2019

IEEE Trans. Commun.

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“…As M and K are both large, this introduces tremendous computational burden and therefore the methods for optimizing the precoders in the literature are not feasible in massive MIMO. Luckily, we can apply results in random matrix theory [46][47][48][49] to simplify the problem. When M and K are both large, the randomness of the small-scale fading can be averaged out and the only parameters left are the large-scale fading coefficients.…”

Section: Help Of Asymptotic Analysismentioning

confidence: 99%

Optimizing Massive MIMO : Precoder Design and Power Allocation

Cheng

2018

Linköping Studies in Science and Technology. Dissertations

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The past decades have seen a rapid growth of mobile data traffic, both in terms of connected devices and data rate. To satisfy the ever growing data traffic demand in wireless communication systems, the current cellular systems have to be redesigned to increase both spectral efficiency and energy efficiency. Massive MIMO (Multiple-Input-Multiple-Output) is one solution that satisfy both requirements. In massive MIMO systems, hundreds of antennas are employed at the base station to provide service to many users at the same time and frequency. This enables the system to serve the users with uniformly good quality of service simultaneously, with low-cost hardware and without using extra bandwidth and energy. To achieve this, proper resource allocation is needed. Among the available resources, transmit power beamforming are the most important degrees of freedom to control the spectral efficiency and energy efficiency. Due to the use of excessive number of antennas and low-end hardware at the base station, new aspects of power allocation and beamforming compared to current systems arises.In the first part of the thesis, new uplink power allocation schemes that based on long term channel statistics is proposed. Since quality of the channel estimates is crucial in massive MIMO, in addition to data power allocation, joint power allocation that includes the pilot power as additional variable should be considered. Therefore a new framework for power allocation that matches practical systems is developed, as the methods developed in the literature cannot be applied directly to massive MIMO systems. Simulation results confirm the advantages brought by the the proposed new framework.In the second part, we introduces a new approach to solve the joint precoding and power allocation for different objective in downlink scenarios by a combination of random matrix theory and optimization theory. The new approach results in a simplified problem that, though non-convex, obeys a v simple separable structure. Simulation results showed that the proposed scheme provides large gains over heuristic solutions when the number of users in the cell is large, which is suitable for applying in massive MIMO systems.In the third part we investigate the effects of using low-end amplifiers at the base stations. The non-linear behavior of power consumption in these amplifiers changes the power consumption model at the base station, thereby changes the power allocation and beamforming design. Different scenarios are investigated and results show that a certain number of antennas can be turned off in some scenarios.In the last part we consider the use of non-orthogonal-multiple-access (NOMA) inside massive MIMO systems in practical scenarios where channel state information (CSI) is acquired through pilot signaling. Achievable rate analysis is carried out for different pilot signaling schemes including both uplink and downlink pilots. Numerical results show that when downlink CSI is available at the users, our proposed NOMA scheme outperforms o...

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“…A key question in this respect is how to select a proper value for this regularization factor. To tackle this issue, we consider the asymptotic regime in which the number of UEs per cell and the number of antennas deployed in each BS increase with the same pace, which allows us to leverage results from random matrix theory [25]- [27]. The analysis provides accurate closed-form approximations for the SINR and SLNR for each BS and UE pair that depend solely on the channel large-scale statistics and the power of the channel estimation error.…”

Section: Introductionmentioning

confidence: 99%