Multi-hop transmission and routing with hierarchical modulation

Hierarchical modulation multiplexes layers of different robustness into one stream. Usually being exploited in broadcast systems such as digital video broadcasting, hierarchical modulation can also be applied to unicast communications in a cooperative manner. This article deals with novel multi-hop cooperative transmission and routing strategies with hierarchical modulation. In multi-hop networks, when intermediate nodes receive different sets of streams, they cooperatively forward streams toward the final destination. We first show how hierarchical modulation reduces the total number of transmissions in tandem networks. We then model an optimization problem that determines proper modulation schemes and routing strategies as a stationary infinite-horizon Markov decision process (MDP). The MDP problem requires exponential computations forcing us to delve for heuristic solutions. We simplify the optimization problem by restricting hierarchical modulation consisting of only two fixed constellations. Even with this approximation, the routing problem with hierarchical modulation is still too complex because we must consider all possible paths and hierarchical modulation schemes at the same time. We introduce an approximation scheme that determines the most efficient hierarchical modulation scheme for each three node subgraph assuming subgraphs are isolated each other. Based on the three node subgraph approximation, we map original graphs to virtual graphs. We then apply one of shortest path algorithm to the virtual graphs to find the best route with hierarchical modulation. We perform simulations with various network topologies to evaluate the efficiency of hierarchical modulation in multi-hop unicast communication. Our results show that hierarchical modulation enhances the throughput about 25–30% compared to non-hierarchical modulation. However, optimal solution derived by solving the MDP is also 5% better than heuristic solutions suggesting the necessity to develop better heuristics in future.