BitGun
To change that long established paradigm of miner rewards being inversely proportional to network hashrate we have developed the advanced BitGun feature.
Usually a miner’s reward directly depends on network hashrate, meaning larger nethash results in lower reward portion going to each miner. In other words, if the amount of GPUs in the network grows 1000 times, the average miners reward within given time frame will become 1000 times less. Some cryptocurrencies use non-linear dependencies, but their principle also remains the same, higher the network hashrate, less each miner receives. BitGun uses another approach – as total network hashrate grows, the block reward gradually increases, allowing to keep an average miners reward relatively stable.
This is how it works.
The reward size for each block changes every time at the moment of block generation, depending on the total average nethash recorded for the last 24 blocks.
The reward size for each block changes in accordance with a set of “levels” reflecting the Fibonacci series. There are 15 levels.
Table 1 presents the levels defined by total nethash and the corresponding block reward size .
For the block reward size to change automatically, the total XDNA nethash must overcome the corresponding threshold value from the table.
What are the advantages of using this approach?
Comparing with the conventional block reward calculation methods, BitGun allows us to stabilize the reward amount received by miners in the certain period of time. With a sufficient growth of network hashrate the average reward slightly decreases, remaining, however, much larger than in systems with traditional distribution.
Level | Network hashrate (Gh/s) | Block reward | Amount of 1080ti | Daily emission | 1080ti/day min | 1080ti/day max |
1 | 10 | 4 | 741 | 5,760 | 2.72 | |
2 | 20 | 5 | 1,482 | 7,200 | 2.27 | 3.40 |
3 | 30 | 7 | 2,223 | 10,080 | 1.91 | 3.17 |
4 | 50 | 10 | 3,704 | 14,400 | 1.70 | 2.72 |
5 | 80 | 14 | 5,926 | 20,160 | 1.47 | 2.38 |
6 | 130 | 19 | 9,630 | 27,360 | 1.23 | 1.99 |
7 | 210 | 25 | 15,556 | 36,000 | 1.00 | 1.62 |
8 | 340 | 32 | 25,186 | 46,080 | 0.79 | 1.28 |
9 | 550 | 40 | 40,741 | 57,600 | 0.61 | 0.99 |
10 | 890 | 49 | 65,926 | 70,560 | 0.46 | 0.75 |
11 | 1,440 | 59 | 106,667 | 84,960 | 0.34 | 0.56 |
12 | 2,330 | 70 | 172,593 | 100,800 | 0.25 | 0.41 |
13 | 3,770 | 82 | 279,260 | 118,080 | 0.18 | 0.30 |
14 | 6,100 | 95 | 451,852 | 136,800 | 0.13 | 0.21 |
15 | 9,870 | 109 | 731,112 | 156,960 | 0.15 |
Table shows comparison of an average reward for one Nvidia GTX 1080Ti GPU for 24 hours using classical calculation method and BitGun.
Mathematical modelling was performed for levels 1-8. For this simulation, we used the following conditions: a miner gets a reward from each block.
Figure 1 presents the comparison of daily reward for a single Nvidia GeForce GTX 1080ti GPU depending on total network hashrate, stated in number of mining GPUs.
As we see even with a significant increase in network hashrate each given GPU will keep receiving relatively stable reward within one BitGun level, this reward is much larger than a reward, calculated using classic system would be.
These simulation results are valid for Levels 2-14 and can be successfully approximated for any time interval.
The novelty of this approach is primarily in the fact that it changes the very paradigm of pseudolinear inverse relation of miner`s income to the nethash.
BitGun also has another, not so obvious advantage. If ASIC miners for HEX algo are ever developed, XDNA won’t have to change it’s consensus, algorithm, or to implement a hardfork in order to keep GPU miners happy. Few minor amendments in BitGun parameters should be enough.
Miners from all over the world can now count on decent mining rewards even if the network hashrate suddenly grows 1000 times.