Federated edge learning (FEEL) is an advanced paradigm in edge artificial intelligence, enabling privacy-preserving collaborative model training through periodic communication between edge devices and a central server. FEEL involves three key processes: 1) sensing; 2) computation; and 3) communication for data acquisition, processing, and exchange, respectively. Due to limited system resources, optimizing each process individually may lead to suboptimal learning performance. This challenge has sparked research into integrated sensing-computation–communication (ISCC) design for enhanced FEEL. While previous work has optimized general learning parameters, such as batch size and computing frequency, there is a lack of customized designs considering the neural network architecture as an optimizable variable in ISCC for FEEL. To close this gap, we introduce a novel design where each device generates a submodel through controllable weight dropout, adding flexibility by directly manipulating the learning process and reducing computation and communication overhead. To guide ISCC resource allocation in this new setting, we present a comprehensive convergence analysis, revealing the tight coupling of sensing, computation, and communication across devices and their impact on FEEL convergence. Building on these theoretical insights, we formulate an ISCC problem aiming to maximize the FEEL convergence rate through joint optimization of variables, such as batch size, sensing power, dropout rate, and communication power. This nonconvex problem is decomposed into two subproblems via alternating optimization: one controls batch size using a sorting algorithm, while the other focuses on ISCC device parameters, transformable into a convex problem solved by successive convex approximation. Extensive experiments using human motion recognition datasets demonstrate the superiority of the proposed design over baseline schemes.

In this paper, the training time minimization problem is investigated in a quantized FEEL system, where the heterogeneous edge devices send quantized gradients to the edge server via orthogonal channels. In particular, a stochastic quantization scheme is adopted for compression of uploaded gradients, which can reduce the burden of per-round communication but may come at the cost of increasing number of communication rounds. The intrinsic trade-off between the number of communication rounds and per-round latency is characterized. Specifically, we analyze the convergence behavior of the quantized FEEL in terms of the optimality gap. Constrained by total bandwidth, the training time minimization problem is formulated as a joint quantization level and bandwidth allocation optimization problem. To this end, an algorithm based on alternating optimization is proposed, which alternatively solves the subproblem of quantization optimization via successive convex approximation and the subproblem of bandwidth allocation via bisection search. With different learning tasks and models, the validation of our analysis and the near-optimal performance of the proposed algorithm are demonstrated by the experimental results.

Training a machine learning model with federated edge learning (FEEL) is typically time consuming due to the constrained computation power of edge devices and the limited wireless resources in edge networks. In this study, the training time minimization problem is investigated in a quantized FEEL system, where heterogeneous edge devices send quantized gradients to the edge server via orthogonal channels. In particular, a stochastic quantization scheme is adopted for compression of uploaded gradients, which can reduce the burden of per-round communication but may come at the cost of increasing the number of communication rounds. The training time is modeled by taking into account the communication time, computation time, and the number of communication rounds. Based on the proposed training time model, the intrinsic trade-off between the number of communication rounds and per-round latency is characterized. Specifically, we analyze the convergence behavior of the quantized FEEL in terms of the optimality gap. Furthermore, a joint data-and-model-driven fitting method is proposed to obtain the exact optimality gap, based on which the closed-form expressions for the number of communication rounds and the total training time are obtained. Constrained by the total bandwidth, the training time minimization problem is formulated as a joint quantization level and bandwidth allocation optimization problem. To this end, an algorithm based on alternating optimization is proposed, which alternatively solves the subproblem of quantization optimization through successive convex approximation and the subproblem of bandwidth allocation by bisection search. With different learning tasks and models, the validation of our analysis and the near-optimal performance of the proposed optimization algorithm are demonstrated by the simulation results.

This paper investigates a X-duplex decode-and-forward relay system in the presence of direct link from the source to destination. X-duplex relays can adaptively switch between half-duplex mode and full-duplex mode according to the instantaneous channel conditions. Unlike previous work on X-duplex relay, we treat the direct link as an additional information path and propose a new transmission scheme based on dirty paper coding (DPC). By using DPC as the precoding scheme at the source, the messages can be divided into two parts which are sent from the source to destination through the relay link and direct link, respectively. In addition, the destination performs successive interference cancellation as the decoding strategy. The optimal transmit powers at the source and relay by maximizing the end-to-end achievable rate are obtained. The numerical results show that the new transmission scheme achieves a better performance over the reference scheme.

In this paper, we investigate the hard combining scheme of cooperative spectrum sensing in a practical scenario where secondary users (SUs) may use different local detectors and have different signal-to-noise ratios (SNRs). For simplicity, energy detector and pilot-based detector are considered for local quantization. The suboptimal AND and OR rules are implemented in the fusion center (FC). We point out that the necessary condition of the optimal local threshold in distributed detection can be extended to the heterogeneous cooperative sensing network by some modification. Subsequently, two iterative threshold optimization algorithms are extended from methods in conventional cooperative sensing scenarios: the ascending order of Bayesian risk optimization (AOBO) and the person-by-person optimization (PBPO). Simulation results show that both the AOBO and PBPO outperform the uniform-threshold approach, when the sensing abilities at SUs are much different. When all SUs have an identical SNR and only use energy detectors, the PBPO method has a same near optimal performance compared to the uniform-threshold approach.

The joint multicast beamforming and user scheduling problem is studied, with the objective of minimising total transmitting power across multiple channels by jointly assigning each user to appropriate channel and designing multicast beamformer for each channel. The problem of interest is formulated in two different optimisation problems, a mixed binary quadratically constrained quadratic program and a highly‐structured non‐smooth program. Two different algorithms, based on convex relaxation and convex restriction, respectively, are proposed to solve the problem. The performance ratio between the approximate solution provided by the convex‐relaxation‐based algorithm and optimal solution is proven to be upper bounded by a constant independent of problem data. The convex‐restriction‐based algorithm is guaranteed to converge to a critical point to the non‐smooth formulation problem. Finally, extensive simulation results verify the theoretical analysis and demonstrate the advantage of the proposed co‐design scheme over conventional fixed scheduling and random scheduling in terms of power consumption.

This paper discusses a typical cooperative sensing model where the local observations are conditionally dependent. The primary user sends a binary phase-shifted keying modulated signal and the hard combining strategy is implemented among second users. Two low-complexity local quantization schemes corresponding to the choice of quantizing either the amplitude or the sign as the one-bit reported message are compared. We show that the optimal fusion rule for quantizing the sign is non-monotone, which is different from the design for quantizing amplitude scheme. Based on large deviation theory, we finish the parameter optimization for both schemes and use the error exponent to evaluate the asymptotical performance. Several closed-form expressions are derived. Numerical results show that to quantize the sign is asymptotically optimal in low-SNR regime, while to quantize the amplitude performs well in high-SNR regime. This work shows that the dependence among local observations may greatly help the final decision in some case, and the non-monotone fusion rule may achieve better performance than the monotone one for dependent observations.

In this paper, downlink transmission schemes that are able to improve the energy efficiency of multi-cell massive distributed antenna systems (DAS) are investigated. We employ a power consumption model by considering the transmit power, the backhaul power, the uplink pilot transmit power and the circuit power, in contrast to existing works which focus on co-located antenna systems where the backhaul power is negligible. By applying random matrix theory and given the power consumption model, we derive the asymptotic energy efficiency achieved by two transmission schemes, first a full transmission scheme where all remote radio heads (RRHs) in a cell jointly transmit, and second single-RRH user association transmission scheme where each user is associated with only one RRH. A new algorithm to associate user with RRH is proposed based on the asymptotic energy efficiency. The proposed algorithm takes into consideration the power attached to antennas as well as the backhauling power of RRH. Simulations show that the proposed user association algorithm for DAS achieves higher energy efficiency than full transmission scheme and the baseline nearest RRH association scheme, especially when the number of antennas is large.

In the paper by Shiyong Yang et al. , a closed-form power allocation scheme of weighted rate sum maximization in Gaussian broadcast channel was given. Their scheme is indeed optimal, but there are some critical defects in their proof for the optimality. In this paper, we first analyze the defects in their proof and then present a new proof for the optimality of their scheme.

Massive multiple-input multiple-output (MIMO) is an attractive solution to achieve high data rate and tackle interference. In this paper, we study the downlink of a two-cell massive MIMO system employing coordinated zero-forcing beamforming with limited feedback, where each base station (BS) acquires both the direct link channel state information (DCSI) and the cross link CSI (CCSI) from the users through a limited feedback link. The DCSI is used to support multi-user MIMO transmission (control intra-cell interference) at each BS and the CCSI is used to control the inter- cell interference. Since the total number of feedback bits at each user is fixed, there is a tradeoff between the quantization errors of the DCSI and CCSI. We consider the allocation of the feedback bits over the DCSI and CCSI to maximize the sum-rate of the system. We obtain closed-form solution for the asymptotically optimal feedback bits allocation as the number of antennas per BS goes to infinity. The solution reveals insights on how the key system parameters such as path gains, transmit powers and the number of antennas/users affect the optimal feedback bits allocation. Numerical results validate our theoretical analysis.

ABSTRACT With the reformation of spectrum policy and the development of cognitive radio, secondary users will be allowed to access spectrums licensed to primary users. Spectrum auctions can facilitate this secondary spectrum access in a market‐driven way. To design an efficient auction framework, we first study the supply and demand pressures and the competitive equilibrium of the secondary spectrum market, considering the spectrum reusability. In well‐designed auctions, competition among participants should lead to the competitive equilibrium according to the traditional economic point of view. Then, a discriminatory price spectrum double auction framework is proposed for this market. In this framework, rational participants compete with each other by using bidding prices, and their profits are guaranteed to be non‐negative. A near‐optimal heuristic algorithm is also proposed to solve the auction clearing problem of the proposed framework efficiently. Experimental results verify the efficiency of the proposed auction clearing algorithm and demonstrate that competition among secondary users and primary users can lead to the competitive equilibrium during auction iterations using the proposed auction framework. Copyright © 2011 John Wiley & Sons, Ltd.