Subchannel Allocation in LTE Resource Scheduling
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Solution Overview
Problem
The complexity of allocating communication resources, such as subchannels, in wireless communication systems like 3GPP-LTE, is exacerbated by varying channel conditions and constraints on fairness and efficiency, leading to a computationally intensive problem that requires a more efficient solution than brute force exhaustive searching.
Innovation Solution
The problem is reformulated as a binary integer programming problem, specifically a set-partitioning problem, using a resource allocator that creates constraint and reward matrices to determine optimal subchannel allocations, reducing complexity through efficient algorithms like binary integer programming or greedy heuristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If brute force exhaustive searching is used to allocate subchannels to users, then optimal resource allocation can be achieved, but computational complexity becomes prohibitively high
Solution Approach 1:
The patent transforms the original continuous resource allocation problem into a discrete binary integer programming problem by introducing binary variables that indicate whether a user is allocated to a specific subchannel. This parameter transformation enables the use of efficient combinatorial optimization algorithms instead of brute force searching, significantly reducing computational complexity while maintaining optimality.
Solution Approach 2:
The patent extracts the essential allocation decision from the complex continuous optimization problem by focusing only on the binary allocation status (allocated or not allocated) for each user-subchannel pair. This extraction simplifies the problem structure and enables the use of specialized algorithms for binary integer programming, avoiding the need to evaluate all possible continuous allocations.
2Reliability
If fairness constraints are added to resource allocation to ensure service to all users, then system fairness is improved, but allocation complexity increases
Solution Approach 1:
The patent segments the resource allocation problem into discrete user-subchannel allocation decisions represented by binary variables. By dividing the continuous allocation space into discrete segments (allocated/not allocated), the problem becomes amenable to combinatorial optimization techniques that can efficiently handle fairness constraints through the binary variable framework, rather than requiring complex continuous constraint satisfaction.
3Quantity of substance
If the number of users and subchannels increases in the communication system, then system capacity is improved, but the number of possible allocations grows exponentially
Solution Approach 1:
The patent introduces dynamic programming or iterative optimization approaches that build solutions incrementally rather than evaluating all possibilities at once. The binary variable framework allows the algorithm to dynamically adjust allocations by flipping individual binary decisions, enabling efficient exploration of the solution space even as the number of users and subchannels increases, avoiding exponential complexity growth.
Data Source
AI summary
Methods and corresponding systems for allocating resources in a communications system includes determining feasible sets of subchannels for allocation to a user subject to an allocation constraint. In one approach, a constraint matrix representing constraints for allocating subchannels to users in allocations of selected subchannels is computed, subject to the allocation constraint. Then a vector containing metrics corresponding to allocations of selected subchannels to the communication links is estimated. A binary decision vector representing an allocation of the subchannels to the users is computed using binary integer processing. In another approach a greedy heuristic allocation is used. The allocation constraint can be a restriction limiting multiple subchannels allocated to a user to be adjacent to one another. The metrics can be weighted capacities of allocations of selected subchannels to the users.