Joint Downlink Base Station Association and Power Control for Max-Min Fairness: Computation and Complexity

In a heterogeneous network (HetNet) with a large number of low power base stations (BSs), proper user-BS association and power control is crucial to achieving desirable system performance. In this paper, we systematically study the joint BS association and power allocation problem for a downlink cellular network under the max-min fairness criterion. First, we show that this problem is NP-hard. Second, we show that the upper bound of the optimal value can be easily computed, and propose a two-stage algorithm to find a high-quality suboptimal solution. Simulation results show that the proposed algorithm is near-optimal in the high-SNR regime. Third, we show that the problem under some additional mild assumptions can be solved to global optima in polynomial time by a semi-distributed algorithm. This result is based on a transformation of the original problem to an assignment problem with gains $\log(g_{ij})$, where $\{g_{ij}\}$ are the channel gains.Comment: 24 pages, 7 figures, a shorter version submitted to IEEE JSA

Joint Pilot Design and Uplink Power Allocation in Multi-Cell Massive MIMO Systems

This paper considers pilot design to mitigate pilot contamination and provide good service for everyone in multi-cell Massive multiple input multiple output (MIMO) systems. Instead of modeling the pilot design as a combinatorial assignment problem, as in prior works, we express the pilot signals using a pilot basis and treat the associated power coefficients as continuous optimization variables. We compute a lower bound on the uplink capacity for Rayleigh fading channels with maximum ratio detection that applies with arbitrary pilot signals. We further formulate the max-min fairness problem under power budget constraints, with the pilot signals and data powers as optimization variables. Because this optimization problem is non-deterministic polynomial-time hard due to signomial constraints, we then propose an algorithm to obtain a local optimum with polynomial complexity. Our framework serves as a benchmark for pilot design in scenarios with either ideal or non-ideal hardware. Numerical results manifest that the proposed optimization algorithms are close to the optimal solution obtained by exhaustive search for different pilot assignments and the new pilot structure and optimization bring large gains over the state-of-the-art suboptimal pilot design.Comment: 16 pages, 8 figures. Accepted to publish at IEEE Transactions on Wireless Communication

Dynamic Non-Orthogonal Multiple Access (NOMA) and Orthogonal Multiple Access (OMA) in 5G Wireless Networks

In this paper, facilitated via the flexible software defined structure of the radio access units in 5G, we propose a novel dynamic multiple access technology selection among orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) techniques for each subcarrier. For this setup, we formulate a joint resource allocation problem where a new set of access technology selection parameters along with power and subcarrier are allocated for each user based on each user's channel state information. Here, we define a novel utility function taking into account the rate and costs of access technologies. This cost reflects both the complexity of performing successive interference cancellation and the complexity incurred to guarantee a desired bit error rate. This utility function can inherently demonstrate the trade-off between OMA and NOMA. Due to non-convexity of our proposed resource allocation problem, we resort to successive convex approximation where a two-step iterative algorithm is applied in which a problem of the first step, called access technology selection, is transformed into a linear integer programming problem, and the nonconvex problem of the second step, referred to power allocation problem, is solved via the difference-of-convex-functions (DC) programming. Moreover, the closed-form solution for power allocation in the second step is derived. For diverse network performance criteria such as rate, simulation results show that the proposed new dynamic access technology selection outperforms single-technology OMA or NOMA multiple access solutions.Comment: 28 pages, 6 figure

Federated Learning for 6G: Applications, Challenges, and Opportunities

Traditional machine learning is centralized in the cloud (data centers). Recently, the security concern and the availability of abundant data and computation resources in wireless networks are pushing the deployment of learning algorithms towards the network edge. This has led to the emergence of a fast growing area, called federated learning (FL), which integrates two originally decoupled areas: wireless communication and machine learning. In this paper, we provide a comprehensive study on the applications of FL for sixth generation (6G) wireless networks. First, we discuss the key requirements in applying FL for wireless communications. Then, we focus on the motivating application of FL for wireless communications. We identify the main problems, challenges, and provide a comprehensive treatment of implementing FL techniques for wireless communications

Dynamic non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) in 5G wireless networks

In this paper, a novel dynamic multiple access technology selection among orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) techniques is proposed. For this setup, a joint resource allocation problem is formulated in which a new set of access technology selection parameters along with power and subcarrier are allocated for each user based on each user’s channel state information. Here, a novel utility function is defined to take into account the rate and costs of access technologies. This cost reflects both the complexity of performing successive interference cancellation and the complexity incurred to guarantee a desired bit error rate. This utility function can inherently capture the tradeoff between OMA and NOMA. Due to non-convexity of the proposed resource allocation problem, a successive convex approximation is developed in which a two-step iterative algorithm is applied. In the first step, called access technology selection, the problem is transformed into a linear integer programming problem, and then, in the second step, a nonconvex problem, referred to power allocation problem, is solved via the difference-of-convexfunctions (DC) programming. Moreover, the closed-form solution for power allocation in the second step is derived. For diverse network performance criteria such as rate, simulation results show that the proposed new dynamic access technology selection outperforms single-technology OMA or NOMA multiple access solutions