Channel, Mode and Power Optimization for non-Orthogonal D2D Communications: a Hybrid Approach

TL;DR

The paper tackles resource allocation for non-orthogonal D2D communications underlaying a cellular uplink by proposing a two-time-scale framework: centralized mode/channel selection on a long timescale using static or stochastic information, and distributed per-slot power control by D2D users using local CSI. It introduces a maximum-reward power allocation strategy parameterized by a blockage weight $\lambda$, proves existence/uniqueness, and derives closed-form throughputs; it also provides a centralized mode/channel assignment via a throughput matrix and the Hungarian algorithm. Theoretical results include explicit expressions for average D2D and cellular throughputs, and a detailed analysis of the single power-level case, with numerical results showing sizable gains in total system throughput and fairness over state-of-the-art distributed schemes. Simulations demonstrate that the proposed hybrid scheme yields significant improvements in throughput, while flexibility in power levels further enhances performance, validating the approach for practical dense networks with D2D underlay.

Abstract

The increasing traffic demand in cellular networks has recently led to the investigation of new strategies to save precious resources like spectrum and energy. Direct device-to-device (D2D) communication becomes a promising solution if the two terminals are located in close proximity. In this case, the D2D communications should coexist with cellular transmissions, so they must be carefully scheduled in order to avoid harmful interference impacts. In this paper, we outline a novel framework encompassing channel allocation, mode selection and power control for D2D communications. Power allocation is done in a distributed and cognitive fashion at the beginning of each time slot, based on local information, while channel/mode selection is performed in a centralized manner only at the beginning of an epoch, a time interval including a series of subsequent time slots. This hybrid approach guarantees an effective tradeoff between overhead and adaptivity. We analyze in depth the distributed power allocation mechanism, and we state a theorem which allows to derive the optimal power allocation strategy and to compute the corresponding throughput. Extensive simulations confirm the benefits granted by our approach, when compared with state-of-the-art distributed schemes, in terms of throughput and fairness.

Figures (6)

• Figure 1: Conceptual scheme of our approach. Mode selection is performed only at the beginning of each epoch; power allocation (for D2D mode only) is performed distributedly at the beginning of each time slot.
• Figure 2: The MR policy for the scenario with four nodes in these positions (quantities expressed in meters): $B = (0,0)$, $S = (100, 0)$, $D = (100, 80)$ and $U = (0, 120)$. The $N=4$ positive power levels are $P_i = 0.4\times2^{i-1}\,\,\,\!\! mW$, with $i\in\{1,2,3,4\}$, while $P_u=2\,\,\,\!\! mW$. In case (a) we have $W = 3$ slots and $\lambda = 0.8212$, whereas in (b) we have $W = 6$ and $\lambda = 1.1918$.
• Figure 3: The average CUE throughput $\Omega_C$, as a function of the D2D target SNR $\xi$.
• Figure 4: The average DUE throughput $\Omega_D$, as a function of the D2D target SNR $\xi$.
• Figure 5: The channel throughput $\Omega_{C+D}$, as a function of the D2D target SNR $\xi$.

Theorems & Definitions (13)

• Lemma 3.1
• proof
• Lemma 3.2
• proof
• Remark 3.1
• Remark 3.2
• Remark 3.3
• Theorem 3.1
• proof
• Proposition 3.2