As advances for data in wireless technology take effect, the need to devise new technologies that satisfy the high data rates are being developed. The growth in data rates has challenges as well as opportunities. Through these advancements, higher data rates are obtained when the bandwidth is increased through flexible and cognitive spectrum usage. In the process of deploying networks, high costs can be exorbitant if the base stations are expensive. To maintain the high quality of the wireless networks high, there is a need for interface management in the lower level of the system. Having fewer users per cell can make the making the local interference awareness easy to implement because of its feasibleness. In a cellular network, a scheduler is used to organize various access points in the cell to optimize the system performance (Pekkah, 2011. p2). This means that there must be a tradeoff between efficiency and fairness; whereby the highest efficiency is given by the sum of all the performance metrics of the served nodes, given that these nodes have the most favorable conditions and leaving those that are otherwise. Fairness is mostly measured using Jain’s fairness index and it is maximized when all the users have equal throughputs irrespective of the efficiency. In conventional systems like the LTE, the scheduler possesses information on the quality of the resource blocks as stipulated in the UE and link direction instruction manual (Pekkah, 2011). This is important as it helps the scheduler to respond to time.
In a TDD (time division duplex) wireless network, the access is usually frame-based and each radio frame is subdivided into subframes in the time domain and into sub-bands (Pekkah, 2011 p. 2). Subframes and sub-band together are called resource blocks (RB). The (BS)base stations and the user equipment undergo synchronization and are hence equipped with quality radio that reduces interference between the RBs the frames are partitioned to the DL and UL access sub-frames. The scheduler is therefore allowed to assign the resource blocks to the user equipment freely while sticking to the constraints on the downlink and the uplink submissions that are scheduled on the related sub-frames. The EU is assigned to the base station that has the strongest channel gain on its network. The UEs and the serving BS generate a cell where the transmissions are structured at the BS. This transmission is undertaken by the scheduler.
The proportionate fair schedule is tasked with the role of determining the links that are active on the resources and the MCS to be employed in these transmissions (3). The PF scheduler is tasked with the role of determining pn,k, and MCS’s for the resultant (i + 1) frame. This is given by the observation that is made on the ith frame. The transmit powers can be obtained from the constraints given by the hardware and the spectrum usage requirements.
Interference awareness scheduler (IAs)
This scheduler employs similar principles to the PF scheduler, only that the cells neighboring the cell links are considered when scheduling the metric calculations. PF scheduling is defined as the sum of the logarithms of the average throughputs over the links that the scheduler handles (Pekkahs, 2011 p.4). To do the system-wide maximization, the same principle that the PF scheduler follows is applied. The interference awareness scheduling metric is formed as a result of calculating the mean of the logarithms of all the throughputs of the links involved (Pekkah, 2011 P.4). In the case of decentralized PRM, the metric calculation is obtained by assuming that other schedules do repeat the previous schedules. This approach is best used in static situations where the only changes necessary are the incremental changes. To modify the PF scheduler, intra cell scheduling metric found inline-five should be replaced with the scheduling metric of the system level. Metric is the geometric mean that occurs in all the links being considered and is obtained when an assumption that all scheduling frames are done as in the former frame indicates the system utility as per the scheduling decision.
After evaluation, the scheduling metric follows similar steps to those taken by the PF scheduler when activating the link and the resource pair. Despite this fact, if the maximum utility change is negative, the system performance would degrade if the resource is utilized. This is what distinguishes IA from the PF scheduler. Generally, not all interference is heard, and therefore only the local state of the other links is taken into consideration.
When implementing the IA scheduler, several factors are considered which include
- network deployment
- Data Rate in every Link
- A number of the Active Links.
- Mobility and Traffic Load
Implementation requires some resources to be distributed between the nodes like the
- signal power, Sn,k(i),
- Total interference
- Noise power, Zn,k(i), and
- Mean throughput of each of the receivers, Tn(i).
The IA scheduler is a system-wide metric that requires convergence. Convergence occurs as a result of the sequential updates matching the coordinate’s descent method. The multivariate optimization issues are solved through the calculation of scalar subproblems, where each operates on the scheduler. Sequential scheduling is too slow in large networks and this complicates their implementation. To overcome such a scenario, randomization is done.
Pekka, J., & Visa, K. (2011). Interference-aware radio resource management for local area wireless networks. EURASIP Journal on Wireless Communications and Networking, 2011.