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.
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.
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.
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.
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.
With the recent advancements in the field of combinatorial optimization, QUBO (quadratic unconstrained binary optimization) has become a topic of intense interest, as a problem format acceptable to several hardware heuristics such as quantum and digital annealers. Accordingly, transforming hard mathematical problems into QUBO problems is of particular importance. In this paper, we restate three problems in NP-complete and NP-hard classes as QUBO problems with proofs of equivalence. Our problems of interest are the numerical three-dimensional matching problem, the monochromatic triangle problem and the optimum facility location problem. Furthermore, we comment on the feasibility of these formulations on sophisticated QUBO solving platforms.
Resource management in next-generation wireless communication networks poses substantial computational challenges due to its combinatorial structure and mixed discrete–continuous nature. Many such problems are NP-hard (non-deterministic polynomial-time hard), rendering conventional optimization techniques computationally demanding and often impractical for large-scale deployments. Recent advances in neutral atom quantum computing have demonstrated innovative potential for solving combinatorial optimization problems through graph-based formulations. However, their application to wireless resource management remains largely unexplored. This paper investigates the use of neutral atom quantum processors to address the maximum access problem (MAP) in uplink non-orthogonal multiple access (NOMA)-enabled non-terrestrial networks (NTNs). The MAP is first formulated as a large-scale mixed-integer optimization problem that jointly captures admission control, user clustering, channel assignment, and power allocation under signal-to-interference-plus-noise ratio (SINR) constraints, while accounting for imperfect successive interference cancellation (SIC) at the receiver. To enable hardware-aligned quantum implementation, the problem is transformed into a maximum independent set (MIS) problem over a conflict graph. The resulting graph structure is compatible with the operational principles of neutral atom quantum hardware, allowing direct mapping onto Rydberg atom arrays via blockade-induced interactions. Numerical evaluations assess admission performance, graph complexity, and computational behavior, highlighting the effectiveness of the proposed neutral atom-based framework for solving such graph-theoretic problems. By integrating quantum combinatorial optimization into wireless resource management, this work demonstrates a hardware-aligned mapping for NOMA-enabled NTN resource allocation and provides proof-of-concept validation on a current neutral atom processor, showcasing the potential of neutral atom quantum processors for addressing optimization problems in future 6G and beyond networks.
To run an algorithm on a quantum computer, one must choose an assignment from logical qubits in a circuit to physical qubits on quantum hardware. This task of initial qubit placement, or qubit allocation, is especially important on present-day quantum computers which have a limited number of qubits, connectivity constraints, and varying gate fidelities. In this work we formulate and implement the qubit placement problem as a quadratic, unconstrained binary optimization (QUBO) problem and solve it using simulated annealing to obtain a spectrum of initial placements. Compared to contemporary allocation methods available in t|ket$\rangle $ and Qiskit, the QUBO method yields allocations with improved circuit depth for $>$50% of a large set of benchmark circuits, with many also requiring fewer CX gates.