To model the HQAM approach, the modulation index was increased from the reference modulation level currently employed to the maximum level possible to reach 99.995% uptime. Each modulation scale introduces more capacity, but not all links can reach the maximum modulation scheme because of link length and interference scenarios. Thus, the approach taken represents a sort of tradeoff between modulation scheme scale and network spectrum efficiency (see refs. 4 and 5 for more details). By adopting this method across the entire experimental network, the total capacity increased to 7 Gb/s, or a four-fold improvement.

2. This plot shows the distribution of modulation indexes in the population under analysis.

Figure 2 shows the percentage of links (both last mile and nodal) for which a certain modulation index is achievable. A modulation rate of 128QAM represents the level after which less than 50% of links can sustain a further increase. With a 1024QAM format, that percentage goes down to 25%. For modulation formats higher than 1024QAM, the probability to support a higher modulation keeps declining, but less steeply.

Some additional findings from this analysis include the following:

1. While 64QAM can be achieved in most of the network, for rates of 128QAM and higher it is critical to determine whether the link is in aggregation (node) or last mile. In most cases, 128QAM and higher-order modulation formats cannot be employed in nodes unless changes are applied (e.g., bigger antennas, antenna class upgrade, link geometry variation).

2. Areas where 1024QAM and higher-order formats are applicable are constituted by last-mile links (tails). Looking specifically at this subset, 4096QAM can be achieved when the link length does not exceed 600 m, while 1024QAM can be sustained for link lengths to about 1 km.

Scaling Capacity Using Packet Compression

Packet compression gains are directly linked to the length and types of packets being carried. Once the packet traffic composition or profile is known, the gain offered by packet compression mechanisms can be expressed as a percentage representing the increase in capacity. Knowledge of traffic profile is indispensable since the gain resulting from packet compression is a function of packet length: the smaller the packet, the higher the gain. This aspect is particularly important in mobile backhaul applications, where voice traffic originates in very small packets (64 to 128 B). The packet-compression analysis is based on a conservative assumption: a traffic distribution close to IMIX profile,1 carried through IPv4 and with traffic steering based on virtual local area network (VLAN) (with a double VLAN TAG). Based on these assumptions, a gain of around 40% can be achieved, bringing total network capacity from 1.9 Gb/s to around 2.7 Gb/s.

3. Packet compression gain is compared here to net throughput and system gain for modulation formats through 1024QAM.

Figure 3 contrasts packet compression versus pure net throughput obtained by scaling the modulation index in a 14-MHz channel. For simplicity’s sake, 1024QAM is the maximum modulation displayed. The gain in capacity provided by packet compression, when compared to the net radio capacity, is represented by the solid red curve. Two conclusions can be immediately derived:

1. Given a certain capacity value (such as 100 Mb/s, represented by the dashed, dark blue line), that capacity can be provided using a lower modulation index (in this example, 128QAM instead of 512QAM) when packet compression is used.

2. A lower modulation scheme implies less transmitted power (in this example, 5-dB less power, represented by the difference between the two dotted orange lines), enabling energy saving while reducing dangerous RF pollution and the overall interference scenario in the network.