Rate Balancing for Multiuser Multicell Downlink MIMO Systems

2022 Joint European Conference on Networks and Communications &Amp; 6G Summit (EuCNC/6G Summit)

2022

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In this work, we consider the power allocation problem via rate balancing optimization with imperfect Channel State Information at the Transmitter (CSIT), namely: expected user rate balancing. In particular, we study two closely related optimization problems: maximizing the minimum ergodic user rate under per cell transmit power constraints, and minimizing overall transmit power while satisfying per user rate targets. The max-min rate approach combines an operation of balancing at the user level and sum rate maximization at the level of the user streams. For imperfect CSIT, we exploit an approximation of the expected rate as the Expected Signal and Interference Power (ESIP) rate, based on an original minorizer for every individual rate term. Then, the transmit power is minimized while fulfilling user rate requirements when the latter are feasible, and otherwise switches to weighted rate balancing under the power constraints. Also, we handle the power minimization problem with two variations: minimizing the total transmit power and minimizing the maximum cell transmit power. Simulation results show the effectiveness of the proposed solutions.

“…Both of them become equivalent if we set Pmax = P opt in (19). Therefore, a modified version of the algorithms solving (19) from [23]- [26] can be used to solve PM in (18).…”

Section: Problem Formulationmentioning

confidence: 99%

“…In our previous work [23]- [26], we have focused on (weighted) user rate balancing, in which we consider the weighting factors as user priorities since the considered optimization problem aims to maximize the minimum user rate in the system w.r.t. the users priorities.…”

Section: Introductionmentioning

confidence: 99%

Multi-Cell MIMO Power Minimization via Rate Balancing with Partial CSIT

2022 Joint European Conference on Networks and Communications &Amp; 6G Summit (EuCNC/6G Summit)

2022

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Multi-Cell MIMO User Rate Balancing with Imperfect CSIT: SESIP vs. RESIP

2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)

2022

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In this work, we consider the max-min user rate balancing problem w.r.t. imperfect Channel Knowledge at the Transmitter (CSIT), namely: expected user rate balancing. This combines an operation of balancing at the user level and sum rate maximization at the level of the user streams. For the imperfect CSIT, we exploit an approximation of the expected rate as the Expected Signal and Interference Power (ESIP) rate, based on an original minorizer for every individual rate term. We study the latter with two expected rate approximations: i) Received signal level ESIP (RESIP), which may seem the most natural, and ii) Stream level ESIP (SESIP), which requires some more work for the stream level power optimization. Simulation results confirm the intuition that SESIP outperforms RESIP when the number of streams is lower than the number of receive antennas.

Multi-Cell MIMO User Rate Balancing with Partial CSIT

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)

2021

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In this paper, we consider the problem of user rate balancing in the downlink of multi-cell multi-user (MU) Multiple-Input-Multiple-Output (MIMO) systems with partial Channel State Information at the Transmitter (CSIT). With MIMO leading to multiple streams per user, user rate balancing involves both aspects of balancing and sum rate optimization. We linearize the problem by introducing a rate minorizer and by formulating the balancing operation as constraints leading to a Lagrangian, allowing to transform rate balancing into weighted sum minimization with Perron Frobenius theory. We provide original analytical expressions for the Lagrange multipliers for the multiple power constraints which can also handle the case in which some power constraints are satisfied with inequality, as can arise in a multi-cell scenario. We introduce two partial CSIT formulations. One is based on the ergodic rate Mean Squared Error (EMSE) relation, the other involves an original rate minorizer in terms of the received interference plus noise covariance matrix, in the partial CSIT case applied to the Expected Signal and Interference Power (ESIP) rate. The simulation results exhibit the improved performance of the proposed techniques over naive partial CSIT beamforming based on perfect CSIT algorithms, and in particular illustrate the close to optimal performance of the ESIP approach.