Non-orthogonal multiple access (NOMA) is a key technology for 5G and beyond, enabling more devices to communicate simultaneously than the traditional orthogonal frequency-division multiple access (OFDMA) of 4G. However, the underlying optimization problem for NOMA resource allocation is NPhard. It is possible to formulate this allocation problem as a quadratic unconstrained binary optimization (QUBO) problem to benefit from specialized solvers, but existing software-based solvers are often too slow for real-time implementationIn this paper, we address this bottleneck by proposing a hybrid constraint handling strategy applicable to a wide class of QUBO problems. We introduce the swap-based simulated annealing (SA) solver to implement this strategy and present its implementation on a field-programmable gate array (FPGA) for significant acceleration. Our experimental results demonstrate that the proposed solver on FPGA achieves a 49.2 times speedup compared to PyQUBO and 5.11 times speedup compared to a C++ implementation of the same algorithm while maintaining comparable solution optimality.
Non-orthogonal multiple access (NOMA) is a key technology for 5G and beyond, enabling more devices to communicate simultaneously than the traditional orthogonal frequency-division multiple access (OFDMA) of 4G. However, the underlying optimization problem for NOMA resource allocation is NPhard. It is possible to formulate this allocation problem as a quadratic unconstrained binary optimization (QUBO) problem to benefit from specialized solvers, but existing software-based solvers are often too slow for real-time implementationIn this paper, we address this bottleneck by proposing a hybrid constraint handling strategy applicable to a wide class of QUBO problems. We introduce the swap-based simulated annealing (SA) solver to implement this strategy and present its implementation on a field-programmable gate array (FPGA) for significant acceleration. Our experimental results demonstrate that the proposed solver on FPGA achieves a 49.2 times speedup compared to PyQUBO and 5.11 times speedup compared to a C++ implementation of the same algorithm while maintaining comparable solution optimality.
To support the rapid proliferation of IoT devices, non-uniform multiple access (NOMA) admission control appears as a promising technology. Indeed, through spectrum reuse, it allows more devices to communicate than the traditional orthogonal frequency-division multiple access scheme of 4G. Previous research has shown that the NOMA allocation problem can be formulated into a maximum independent set problem, which is NP-hard. Recent advances in quadratic unconstrained binary optimization (QUBO) solvers show promise in their capability to solve this kind of allocation problems efficiently. We explore various QUBO formulations to determine which is best-suited for the high-frequency requirements of the NOMA allocation problem.
Field-Programmable Gate Arrays (FPGAs) have become indispensable components in modern computing, providing adaptable and reconfigurable hardware platforms. FPGA-based accelerators offer promising avenues for advancements in diverse fields such as real-time data processing, machine learning, and cryptography. However, optimizing the spatial arrangement of functional blocks on an FPGA, known as placement, presents a challenging task due to its NP-hard nature, necessitating sophisticated algorithms for efficient solutions. Enhancing the placement process directly translates to reduced resource utilization during implementation. Quantum computing (QC) presents a promising approach for tackling such combinatorial problems by exploring vast solution spaces. In this paper, we propose a novel approach to the placement problem, re-formulating it as a series of quadratic unconstrained binary optimization (QUBO) problems, which we then solve using QC. Our novel formulation allows for straightforward integration of design constraints and enables the adaptation of sub-problem sizes to match hardware capabilities. Alongside presenting our method, we also investigate the resilience of contemporary quantum hardware in finding placements for real-world-sized FPGAs. A numerical assessment conducted on a D-Wave Advantage 5.4 quantum annealer indicates that the answer is indeed positive.
Non-Orthogonal Multiple Access (NOMA) is a nextgeneration communication technology that enables multiple users share the same wireless channels. To optimize the performance of NOMA, appropriate channel allocation for each user is essential. Previous research has applied a fast optimization approach using the coherent Ising machine (CIM) to channel allocation in NOMA, multiplexing up to two users per channel, which can be formulated as a basic Ising Hamiltonian including up to second-order interactions. However, NOMA can support multiplexing more than two users per channel depending on channel conditions. This paper propose a method for optimizing NOMA systems with more than two multiplexed users per channel, which becomes a Higher Order Binary Optimization problem. We apply a method to reduce the order of the objective function, converting it to Quadratic Unconstrained Binary Optimization, and derive the Ising Hamiltonian for higher-order NOMA. We evaluate our proposed method through simulations using the CIM for optimization. Our results show that the proposed method leads to better solutions than conventional methods.
In wireless communication networks, many resource optimization problems are nondeterministic polynomial-time hard (NP-hard) due to their combinatorial nature and high computational complexity. Recently, neutral-atom-based quantum computing has emerged as a promising platform for efficiently solving such problems by leveraging quantum superposition and entanglement. However, its application to wireless communication optimization problems remains largely unexplored. In this paper, we investigate the use of neutral-atom quantum platforms to solve the maximum access problem (MAP), formulated as a mixed-integer programming task that jointly considers admission control, user clustering, channel assignment, and power allocation in a non-orthogonal multiple access (NOMA)-enabled uplink network. To reduce the computational burden, the MAP is equivalently reformulated as a maximum independent set (MIS) problem in graph theory. This reformulation enables the use of the neutral atom platform based on Rydberg atom arrays, where the MIS problem is naturally encoded into the physical geometry and blockade constraints of the quantum system. Numerical results demonstrate the feasibility and potential of this approach for addressing large-scale wireless resource optimization problems.
Quantum annealing (QA) offers a physics-inspired approach for such subproblems by mapping them onto an Ising Hamiltonian, which is algebraically equivalent to a Quadratic Unconstrained Binary Optimization (QUBO) model executable on quantum annealers or hybrid solvers. However, their practical employment in wireless systems critically depends on the specific problem formulation, on the particular variable encoding, constraint handling, solver selection, hardware embedding, and fair benchmarking. This paper develops wireless-oriented QUBO modeling guidelines for QA-assisted resource allocation. We first present a beginner-friendly toy example in which a terrestrial-aerial resource-allocation problem with a fractional signal-to-interference-plus-noise ratio (SINR) maximization objective is reformulated into a two-bit QUBO via Boolean interpolation, converted into an Ising representation, and decoded back into a wireless scheduling decision. The same scheduling is applicable to UAV, aircraft, satellite, and terrestrial links in SAGIN systems. We then review the QA foundations and solver ecosystems, including binary quadratic, discrete quadratic, constrained quadratic, and hybrid models from the perspective of NG wireless optimization. Furthermore, we discuss both binary and discrete encoding, penalty-based constraint conversion, embedding overhead, solution decoding, and feasibility verification. A QUBO Compatibility Index is introduced as a lightweight diagnostic metric for assessing the associated QUBO formulation readiness, rather than a predictor of runtime or solution quality. Finally, a case study on max-min uplink power control in cell-free massive MIMO illustrates how a fairness-oriented wireless problem can be discretized, constrained, converted into the concept of a QUBO, and evaluated against classical baselines. A host of key challenges and future directions are also discussed.
Abstract Quadratic unconstrained binary optimization (QUBO) is a combinatorial optimization problem. Since various NP‐hard problems such as the traveling salesman problem can be formulated as a QUBO instance, QUBO is used with a wide range of applications. The main contribution of this article is to propose high‐throughput FPGA implementations for the QUBO solver. We perform the local search using different bit‐selection strategies based on the simulated annealing in the proposed implementation. The hardware is a pipeline structure with no pipeline hazards using multiple instances, where the bit‐flip operation is always performed every clock cycle. We implemented the proposed circuit on Xilinx UltraScale+ FPGA VU9P. The implementation result shows that the circuit can search solutions per second. Besides, by sharing the block RAM that stores a weight matrix, we implemented a dual annealer architecture that has two QUBO solvers into the FPGA. As a result, the dual annealer architecture can search solutions per second.