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Practical large-scale coordinated scheduling in LTE-Advanced networks

Abstract

In LTE-Advanced, the same spectrum can be re-used in neighboring cells, hence coordinated scheduling is employed to improve the overall network performance (cell throughput, fairness, and energy efficiency) by reducing inter-cell interference. In this paper, we advocate that large-scale coordination can be obtained through a layered solution: a cluster of few (i.e., three) cells is coordinated at the first level, and clusters of coordinated cells are then coordinated at a larger scale (e.g., tens of cells). We model both small-scale coordination and large-scale coordination as optimization problems, show that solving them at optimality is prohibitive, and propose two efficient heuristics that achieve good results, and yet are simple enough to be run at every transmission time interval. Detailed packet-level simulations show that our layered approach outperforms the existing ones, both static and dynamic.

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Notes

  1. 1.

    One method to deal with interference measurement is reported in [31], Chapter 15.2: a set of neighboring cells can be configured to transmit either a non-zero- or a zero-power Reference Signal (RS), hence one can measure the interference with/without transmission from that set of cells. RSs are transmitted using Resource Elements in the Physical Downlink Shared Channel. As more cells are added to the set, more RSs are required, which increases the overhead.

  2. 2.

    Multi-user Multiple-Input/Multiple-Output (MIMO) techniques are outside the scope of this paper.

  3. 3.

    We leave out techniques such as joint processing, whereby two cells target the same UE simultaneously, reinforcing the useful signal.

  4. 4.

    This problem can be reformulated as a mixed-integer-linear problem (MILP), through a careful reformulation (omitted for the sake of conciseness), but only at the price of increasing the number of variables to O(2^(2K)).

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Acknowledgments

The subject matter of this paper includes description of results of a joint research project carried out by Telecom Italia and the University of Pisa. Telecom Italia reserves all proprietary rights in any process, procedure, algorithm, article of manufacture, or other result of said project herein described. Authors would like to thank Prof. Antonio Frangioni and Dr. Laura Galli of the University of Pisa for their useful suggestions.

Author information

Correspondence to Giovanni Stea.

Additional information

Some of the material included in the present paper also appeared, in a preliminary form, in [24] and [25].

Appendix

Appendix

We provide here a formal description of the algorithms run in Step 2 of the SSC. The bid composition algorithm is reported in Fig. 29. For each inequality in (2), we compute the exceeding RBs (lines 1–3). We then scan the inequalities and decrease the bids that appear in each of them (line 9), until the excess of the inequality is nullified. Since a bid appears in more than one inequality, the excesses must be updated (lines 15–16). By scanning the inequalities by decreasing order of their excess (line 4), the number of required iteration is in general smaller, as it is more likely that fixing those with larger excesses first will make some other inequalities hold as well.

Fig. 29
figure29

Pseudo-code for the composition of the cells’ bids

Once the bids have been composed, we can define the ILSs. With reference to the pseudo-code in Fig. 30 we denote the size of an ILS as Δ(x), where x is the set of active cells in that ILS. Double-muting ILSs are easily defined (line 1). Since the size of single- and no-muting ILSs is defined as the maximum among the requests from neighboring cells, there may be unused RBs in each subframe. Starting from the single-muting ILSs, for each cell we compute the size of the (possibly two) unassigned areas (lines 3–4) and fill them with as many RBs as possible from the no-muting bid (lines 5–10). Then, the size of single-muting ILSs is defined as the minimum between the corresponding bids (line 14), while their difference is added to the double-muting ILS of the cell that requested more RBs (lines 15–17). Similarly, no-muting ILS is defined as the minimum among the no-muting bids (line 19) and the excesses are redistributed to single- and double-muting ILSs (lines 20–23). According to this procedure, some of the single-muting RBs will be upgraded to double-muting, and some of the no-muting RBs will be upgraded to either double-muting or single-muting.

Fig. 30
figure30

Pseudo-code for the definition of the ILSs

Finally, if there are enough unallocated RBs, we can transform one no-muting RB into three double-muting RBs. With reference to the pseudo-code of Fig. 31, given the number of unallocated RBs (line 1), we compute the amount of RBs that can be moved to double-muting, taking into account that one no-muting RBs will become three double-muting RBs (line 2). That amount of RBs is carved from the no-muting ILS (line 3) and added to double-muting ILSs of the three cells proportionally, allowing for some integer rounding which preserves the original amount (lines 4–10).

Fig. 31
figure31

Pseudo-code for upgrading RBs from the no-muting to double-muting ILSs

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Nardini, G., Stea, G., Virdis, A. et al. Practical large-scale coordinated scheduling in LTE-Advanced networks. Wireless Netw 22, 11–31 (2016). https://doi.org/10.1007/s11276-015-0948-6

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Keywords

  • LTE-A
  • Coordinated scheduling
  • CoMP
  • Optimization