Multi Agent DeepRL based Joint Power and Subchannel Allocation in IAB networks

Integrated Access and Backhauling (IAB) is a viable approach for meeting the unprecedented need for higher data rates of future generations, acting as a cost-effective alternative to dense fiber-wired links. The design of such networks with constraints usually results in an optimization problem of non-convex and combinatorial nature. Under those situations, it is challenging to obtain an optimal strategy for the joint Subchannel Allocation and Power Allocation (SAPA) problem. In this paper, we develop a multi-agent Deep Reinforcement Learning (DeepRL) based framework for joint optimization of power and subchannel allocation in an IAB network to maximize the downlink data rate. SAPA using DDQN (Double Deep Q-Learning Network) can handle computationally expensive problems with huge action spaces associated with multiple users and nodes. Unlike the conventional methods such as game theory, fractional programming, and convex optimization, which in practice demand more and more accurate network information, the multi-agent DeepRL approach requires less environment network information. Simulation results show the proposed scheme's promising performance when compared with baseline (Deep Q-Learning Network and Random) schemes.Comment: 7 pages, 6 figures, Accepted at the European Conference on Communication Systems (ECCS) 202

Morphological responses of pulse (Vigna spp.) crops to soil water deficit

The present experiment was conducted with two common pulse crops namely black gram (Vigna mungo L.) and green gram (Vigna radiata L.) with the objective to study the morpho-physiological changes that took place in response to low moisture stress. Parameters such as plant height, leaf number, leaf area and pod number were studied under moisture stress condition as well as subsequent recovery stages. At harvest, yields of these two crops were recorded and various yield indexes like drought susceptibility index, drought tolerance index, mean and productivity rate were calculated. The study revealed that moisture stress has a significant impact on all these parameters in both crops. The effect was more significant in green gram compared to black gram. From the findings it is observed that moisture stress during flowering stage is detrimental for yield of the pulse crops and re-watering does not have a significant impact on yield improvement. Black gram variety T9 and green gram variety Pratap were identified as drought-tolerant varieties

Impact of N fertilization on C balance and soil quality in maize-dhaincha cropping sequence

Excess N fertilization to achieve high crop yield is a grand old practice in developing countries. However, inorganic nutrient sources considerably replenish soil organic C (SOC). In the present study, we applied six different levels of N keeping P and K constant for maize, grown under maize (Zea mays) - dhaincha (Sesbania aculeata) cropping sequence. We recorded high crop yield, profuse root biomass and SOC stock with increasing N fertilization. Moreover, water holding capacity, microbial biomass carbon and particulate organic carbon improved significantly with increasing levels of N. Conversely, bulk density, mineral associated organic carbon and pH decreased with increasing application of inorganic N. Furthermore, a significant positive correlation was recorded between root biomass and soil organic carbon. A study of the sensitivity index showed particulate organic carbon and microbial biomass carbon to be good indicators of nutrient management practices. Dhaincha cultivation accelerated C and N mineralization in soil, which is reflected in increased biomass and crop yield. Hence, we conclude that inorganic N fertilization rate (7280 kg ha-1) in maize-dhaincha cropping sequence successfully maintains the SOC balance and optimize N stock in soil