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Benchmarking Practical RRM Algorithms for D2D Communications in LTE Advanced

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Abstract

Device-to-device (D2D) communication integrated into cellular networks is an advanced tool to take advantage of the proximity of devices and allow for reusing cellular resources and thereby to increase the user bitrates and the system capacity. However, the introduction of D2D in legacy long term evolution (LTE) cellular spectrum requires to revisit and modify the existing radio resource management and power control (PC) techniques in order to fully realize the potential of the proximity and reuse gains and to limit the interference to the cellular layer. In this paper, we examine the performance of the legacy LTE PC tool box and benchmark it against an utility optimal iterative scheme. We find that the open loop PC scheme of LTE performs well for cellular users both in terms of the used transmit power levels and the achieved signal-to-interference-and-noise-ratio distribution. However, the performance of the D2D users as well as the overall system throughput can be boosted by the utility optimal scheme, by taking better advantage of both the proximity and the reuse gains. Therefore, in this paper we propose a hybrid PC scheme, in which cellular users employ the legacy LTE open loop PC, while D2D users exploits the utility optimizing distributed PC scheme. We also recognize that the hybrid scheme is not only nearly optimal, and can balance between spectral and energy efficiency, but it also allows for a distributed implementation at the D2D users, while preserving the LTE PC scheme for the cellular users.

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Notes

  1. Note that (2) is valid only in the case when a \(\gamma ^{tgt}\) value exists.

  2. We assume that the G matrix is obtained after Layer-1 filtering, that is typically used for open loop power control, mobility management and other purposes in LTE [31].

  3. We draw a box around equations that need to be implemented by a receiver or transmitter node, as will be summarized in Fig. 3.

  4. We note that BRA can be made completely distributed by skipping the usage of \(\rho _j\) in the algorithm. Simulation results (not shown here) indicate that the impact of skipping \(\rho _j\) in BRA is not significant.

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Author information

Authors and Affiliations

  1. Ericsson AB, Stockholm, Sweden

    Marco Belleschi, Gábor Fodor & Aidilla Pradini

  2. KTH, Stockholm, Sweden

    Demia Della Penda & Mikael Johansson

  3. University of Siena, Siena, Italy

    Andrea Abrardo

Authors
  1. Marco Belleschi
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  2. Gábor Fodor
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  3. Demia Della Penda
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  4. Aidilla Pradini
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  5. Mikael Johansson
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  6. Andrea Abrardo
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Corresponding author

Correspondence to Marco Belleschi.

Additional information

The work of G. Fodor has been partially performed in the framework of the FP7 project ITC 317669 METIS. G. Fodor has also been supported by the Swedish Foundation for Strategic Research Strategic Mobility SM13-0008 Matthew Project.

Appendices

Appendix 1: Derivation of Inequality (24)

Constraints in Problem (23) can be elaborated by appealing to the definition of matrix \(\mathbf {H}^T\) in Eq. (21) as follows:

$$\begin{aligned} \mathbf {H}^T \varvec{\lambda }^{\mathrm{(LP)}}&\succeq - \omega \mathbf {1} \nonumber \\ \sum _k h_{kl} \lambda _k^{\mathrm{(LP)}}&\ge - \omega , \quad \forall l; \nonumber \\ -h_{ll} \lambda _l^{\mathrm{(LP)}} + \displaystyle \sum _{k\ne l} h_{kl} \lambda _k^{\mathrm{(LP)}}&\ge - \omega , \quad \forall l\\ \frac{\lambda _l^{\mathrm{(LP)}}}{\omega } - \displaystyle \sum _{k\ne l} \frac{G_{kl}}{G_{kk}} \gamma _k^{tgt} \frac{ \lambda _k^{\mathrm{(LP)}}}{\omega }&\le 1 ,\quad \forall l. \end{aligned}$$

Appendix 2: Derivation of Inequality (26)

Inequality (26) can be derived by appealing to Eq. (25) as follows:

$$\begin{aligned} \frac{\lambda _l^{\mathrm{(LP)}}}{\omega } - \displaystyle \sum _{k\ne l} \frac{G_{kl}}{G_{kk}} \gamma _k^{tgt} \frac{\lambda _k^{\mathrm{(LP)}}}{\omega }&\le 1\\ \frac{\lambda _l^{\mathrm{(LP)}}}{\omega } \frac{\gamma _l^{tgt} \sigma _l}{G_{ll}} \frac{G_{ll}}{\gamma _l^{tgt} \sigma _l } - \displaystyle \sum _{k\ne l} \frac{G_{kl}}{G_{kk}} \gamma _k^{tgt} \frac{\lambda _k^{\mathrm{(LP)}}}{\omega } \frac{\sigma _k}{\sigma _k}&\le 1\\ \frac{\mu _l G_{ll}}{\gamma _l^{tgt} \sigma _l}&\le \displaystyle \sum _{k\ne l} \frac{G_{kl}}{\sigma _k}\mu _k +1\\ \gamma _l^{cc} (\varvec{\mu }) \triangleq \frac{\mu _l G_{ll}}{\sigma _l + \sum _{k\ne l} G_{kl} \frac{\sigma _l}{\sigma _k}\mu _k}&\le \gamma _l^{tgt}, \quad \forall l. \end{aligned}$$

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Belleschi, M., Fodor, G., Penda, D.D. et al. Benchmarking Practical RRM Algorithms for D2D Communications in LTE Advanced. Wireless Pers Commun 82, 883–910 (2015). https://doi.org/10.1007/s11277-014-2258-1

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