In Federated Learning (FL) with over-the-air aggregation, the quality of the signal received at the server critically depends on the receive scaling factors. While a larger scaling factor can reduce the effective noise power and improve training performance, it also compromises the privacy of devices by reducing uncertainty. In this work, we aim to adaptively design the receive scaling factors across training rounds to balance the trade-off between training convergence and privacy in an FL system under dynamic channel conditions. We formulate a stochastic optimization problem that minimizes the overall Rényi differential privacy (RDP) leakage over the entire training process, subject to a long-term constraint that ensures convergence of the global loss function. Our problem depends on unknown future information, and we observe that standard Lyapunov optimization is not applicable. Thus, we develop a new online algorithm, termed AdaScale, based on a sequence of novel per-round problems that can be solved efficiently. We further derive upper bounds on the dynamic regret and constraint violation of AdaSacle, establishing that it achieves diminishing dynamic regret in terms of time-averaged RDP leakage while ensuring convergence of FL training to a stationary point. Numerical experiments on canonical classification tasks show that our approach effectively reduces RDP and DP leakages compared with state-of-the-art benchmarks without compromising learning performance.

This paper studies power-efficient uplink transmission design for federated learning (FL) that employs over-the-air analog aggregation and multi-antenna beamforming at the server. We jointly optimize device transmit weights and receive beamforming at each FL communication round to minimize the total device transmit power while ensuring convergence in FL training. Through our convergence analysis, we establish sufficient conditions on the aggregation error to guarantee FL training convergence. Utilizing these conditions, we reformulate the power minimization problem into a unique bi-convex structure that contains a transmit beamforming optimization subproblem and a receive beamforming feasibility subproblem. Despite this unconventional structure, we propose a novel alternating optimization (AO) approach that guarantees monotonic decrease of the objective value, to allow convergence to a partial optimum. We further consider imperfect channel state information (CSI), which requires accounting for the channel estimation errors in the power minimization problem and FL convergence analysis. We propose a CSI-error-aware joint beamforming algorithm, which can substantially outperform one that does not account for channel estimation errors. Simulation with canonical classification datasets demonstrates that our proposed methods achieve significant power reduction compared to existing benchmarks across a wide range of parameter settings, while attaining the same target accuracy under the same convergence rate.

This work addresses the trade-off between convergence and the overall delay in heterogeneous distributed learning systems, where the devices encounter diverse and dynamic communication conditions. We propose to apply adaptive sparsification across the devices and over iterations, formulating an optimization problem to minimize the overall delay while ensuring a specified level of convergence. The resultant stochastic optimization problem cannot be handled by conventional Lyapunov optimization techniques due to the dependency of the per-iteration objective function on the previous iterations. To overcome this challenge, we propose AdaSparse, an online algorithm with a novel per-slot problem that can be solved optimally by searching over a finite discrete space. We further introduce a low-complexity approximation of AdaSparse, termed LC-AdaSparse, which features linear computational complexity and diminishing approximation error. We show that AdaSparse offers strong performance guarantees, simultaneously achieving sub-linear dynamic regret in terms of delay and the optimal rate in terms of convergence. Numerical experiments on classification tasks using standard datasets and various models demonstrate that our approach effectively reduces the communication delay compared with existing benchmarks, to achieve the same levels of learning accuracy.

Combining over-the-air uplink transmission and multi-antenna beamforming can improve the efficiency of federated learning (FL). However, to mitigate the significant aggregation error due to communication noise and signal distortion, pre-processing of device signals and post-processing at the server are required. In this paper, we study the optimization of receiver beamforming and device transmit weights in over-the-air FL, to minimize the total transmit power in each communication round while guaranteeing the convergence of FL. We establish sufficient convergence conditions based on the analysis of gradient descent with error and formulate a power minimization problem. An alternating optimization approach is then employed to decompose the problem into tractable subproblems, and efficient solutions are developed for these subproblems. Our proposed method is evaluated through simulation on standard image classification tasks, demonstrating its effectiveness in achieving substantial reductions in transmit power compared with existing alternatives.

We study the performance of multi-branch hierarchical combining diversity systems. Closed-form expressions are derived for asymptotic error rate and asymptotic outage probability of hierarchical selection-combining maximal-ratio combining (SC-MRC) and selection-combining equal-gain combining over Rician channels with arbitrary correlation. Comparison is made between hybrid-selection maximal-ratio combining (HS/MRC) and hierarchical SC-MRC, and it is shown that SC-MRC suffers at most 0.6 dB signal-to-noise ratio (SNR) loss with respect to HS/MRC for practical number of antennas. However, SC-MRC incurs much smaller insertion loss. Numerical results show that the analytical solutions can provide accurate estimation of error rate and outage probability in large SNR region.

In this paper, we propose a step-wise optimal low power node (LPN) deployment algorithm for LTE heterogeneous networks. Our proposed LPN deployment algorithm takes signal quality, relative loading among macro cells and LPN cells, and user density into account. Simulation results show that the proposed algorithm can achieve close to 100% coverage gain and more than 50% average cell throughput gain over a random LPN deployment scheme.

This paper presents an optimization framework for downlink transmission parameters of mobile cellular systems. A typical network optimization approach is to divide the network into disjoint clusters of base-stations (BS). Optimization is then performed within each cluster for important parameters such as transmit power, precoder, etc. This approach is widely adopted in academic research and industrial standard bodies, e.g. 3GPP LTE-Advanced. While reducing the optimization complexity, this strategy suffers from a performance limit due to interference from the nodes outside cluster. We thus propose a framework to overcome this limit by allowing clusters to exchange their parameters and optimization information via low-rate and non-zero delay backhauls. System-level simulations for LTE downlink transmit power optimization show that the proposed optimization model, while having the low-complexity of cluster-based approach, could nearly achieve the performance of network-wise optimization. Therefore, this model is particularly suitable for optimization of 4G and beyond-4G cellular networks.

Resource scheduling in OFDMA cellular wireless networks is a powerful technique on the MAC layer. Utilizing adaptive modulation and coding allows the effective use of all signal-to-interference ratio (SINR) ranges. Typical single antenna spectral efficiency values for LTE-Advanced range between 4.8 near the base station and 0.2 b/s/Hz at the cell edge. With best-effort traffic and full buffer assumption the tradeoff between emphasizing the cell center or cell edge can be explored extensively. However, with real-time traffic present, this takes priority without fairness adjustment alternatives. In this paper the mixed traffic scenario is studied with an abstract stochastic Petri net model. The exploration of the degrees of freedom by studying the real-time traffic proportion and a fairness adjustment parameter provides new insight to the potential feasible region. The results show that the tradeoff between emphasizing the cell edge performance and maintaining a high average spectral efficiency is most powerful in the best-effort case, while an increasing level of real-time traffic reduces the room for a tradeoff. The stochastic Petri net analysis approach allows numeric analysis without simulation by utilizing Markov chain equivalence and steady state calculations. This model is deliberately abstract but flexible enough to study the tradeoff.

In this paper, we propose a stopping rule-driven channel access scheme for a secondary user pair in multichannel cognitive radio networks (CRNs), aiming to achieve a desired tradeoff between channel sensing and channel access. In the proposed approach, we first formulate the sensing-access tradeoff problem as a 1-stage look-ahead stopping problem. We then derive two stopping conditions, namely power-limited stopping and bandwidth-limited stopping, whereby a desired tradeoff between sensing overhead and throughput increase can be achieved. Once a stopping condition is reached, a secondary user pair stops sensing and starts accessing previously sensed free channels for packet transmission. Simulation results show that, in the case of perfect sensing, the proposed approach outperforms a greedy approach by at least 80% in terms of throughput. Imperfect sensing and its impact are also addressed and evaluated.

In cognitive radio networks (CRNs), effective and efficient channel exploitation is imperative for unlicensed secondary users to seize available network resources and improve resource utilization. In this paper, we propose a simple channel sensing order for secondary users in multi-channel CRNs without a priori knowledge of primary user activities. By sensing the channels according to the descending order of their achievable rates with optimal stopping, we show that the proposed channel exploitation approach is efficient yet effective in elevating throughput and resource utilization. Simulation results show that our proposed channel exploitation approach outperforms its counterparts by up to 18% in a single-secondary user pair scenario. In addition, we investigate the probability of packet transmission collision in a multi-secondary user pair scenario, and show that the probability of collision decreases as the number of channels increases and/or the number of secondary user pairs decreases. It is observed that the total throughput and resource utilization increase with the number of secondary user pairs due to increased transmission opportunities and multi-user diversity. Our results also demonstrate that resource utilization can be further improved via the proposed channel exploitation approach when the number of secondary user pairs approaches the number of channels.

In this paper, we study medium access control (MAC) protocol design for distributed cooperative wireless networks. We focus on beneficial node cooperation by addressing two fundamental issues of cooperative communications, namely when to cooperate and whom to cooperate with, from a cross-layer protocol design perspective. In the protocol design, taking account of protocol overhead we explore a concept of cooperation region, whereby beneficial cooperative transmissions can be identified. We show that a rate allocation in the cooperation region provides higher link utilization than in a non-cooperation region. To increase network throughput, we propose an optimal grouping strategy for efficient helper node selection, and devise a greedy algorithm for MAC protocol refinement. Analysis of a successful transmission probability with cooperative or direct transmission is presented. Simulation results show that the proposed approach can effectively exploit beneficial cooperation, thereby improving system performance. Further, analytical and simulation results shed some light on the tradeoff between multi-user diversity gain at the physical layer and the helper contention overhead at the MAC layer.

In this paper, we study two fundamental issues of cooperative communications in distributed wireless networks, namely when to cooperate and whom to cooperate with. In specific, we focus on cross-layer medium access control (MAC) protocol design with beneficial node cooperation. To increase long-term network throughput, we propose an optimal grouping strategy for relay node selection, and devise a greedy algorithm for MAC protocol parameter refinement. Quantitative relationships among the channel state, payload length, protocol overhead, and cooperation gain are illustrated. Simulation results show that the proposed approach can effectively exploit beneficial cooperation, thereby improving system performance.

In this paper, we propose a novel end-to-end resource allocation approach for wireless mesh networks with quality-of-service (QoS) assurance. By introducing additional interference tolerability to each multimedia flow, our proposed approach is shown effective in fostering frequency reuse and increasing the number of multimedia flows supported in the system, outperforming its conventional resource allocation counterpart. Further, the proposed approach is of low complexity, leading to a preferred candidate for practical implementation.

In this paper, we investigate packet-level non-altruistic node cooperation in wireless networks with regenerative nodes. Since each node has its own data to transmit, pure relays do not exist. We prove that the split of transmit power has no impact on the diversity performance of non-altruistic cooperative transmissions. Despite the beneficial diversity gain, our results show that non-altruistic cooperative transmissions are not always superior to ordinary direct transmissions. We also evaluate the performance gains due to beneficial packet-level node cooperations in a multi-node wireless network. Simulation results show that favorable packet-level cooperative transmissions provide a substantial gain over ordinary direct transmissions.

Node cooperation has been demonstrated promising in ameliorating system performance for wireless networks. To effectively and efficiently provision quality-of-service (QoS) at the packet level in wireless mesh networks (WMNs) supporting heterogeneous traffic, medium access control (MAC)-layer resource allocation and service differentiation are imperative. In this paper, we propose a low-complexity node cooperative resource allocation approach for WMNs, taking subcarrier allocation, partner allocation, QoS assurance, and service differentiation into account. With beneficial node cooperation, our proposed approach is shown to be promising in provisioning QoS and increasing system throughput. The proposed approach also achieves Pareto optimality, making efficient use of network resources.

Joint power-subcarrier-time resource allocation is imperative for wireless mesh networks due to the necessity of packet scheduling for quality-of-service (QoS) provisioning, multi-channel communications, and opportunistic power allocation. In this work, we propose an efficient intra-cluster packet-level resource allocation approach. Our approach takes power allocation, subcarrier allocation, packet scheduling, and QoS support into account. The proposed approach combines the merits of a Karush-Kuhn-Tucker (KKT)-driven approach and a genetic algorithm (GA)-based approach. It is shown to achieve a desired balance between time complexity and system performance. Bounds for the throughputs obtained by real-time and non-real-time traffic are also derived analytically.

Node clustering and subcarrier allocation are imperative to ameliorate system throughput and facilitate quality-of-service (QoS) provisioning by means of effective interference control and maximum frequency reuse. In this paper, we propose a novel node clustering algorithm with effective tax-based subcarrier allocation tailored for wireless mesh networks with QoS support. With increased frequency reuse, our proposed approach is shown to achieve a higher system throughput than a conflict-graph approach and a baseline approach. Also, our approach is demonstrated promising in balancing packet delay and end-to-end transmission rate. By carefully adjusting an upper bound of subcarriers allocated to each cluster, we can achieve improved system performance. The proposed resource allocation achieves the Pareto optimality, demonstrating efficient use of network resources. Further, our analysis reveals that how to allocate resources in a wireless network in a decentralized manner can affect the solution space of a performance tradeoff between QoS provisioning and throughput maximization.

Node cooperation has been demonstrated promising in system performance improvement for wireless networks. To effectively provision packet-level quality-of-service (QoS) in wireless mesh networks (WMNs) supporting heterogeneous traffic, medium access control (MAC) with service differentiation is imperative. In this paper, we study the problem of non-altruistic non-reciprocal node cooperative resource allocation for WMNs with QoS support, taking subcarrier allocation, power allocation, partner selection/allocation, service differentiation, and packet scheduling into account. Due to the NP hardness of our resource allocation problem, we propose two low-complexity yet effective approaches based on the Karush-Kuhn-Tucker (KKT) interpretations, tailored for WMNs with QoS assurance and MAC-layer service differentiation. Further, simulation results show that both proposed approaches can effectively provision packet-level QoS and enhance system performance. Our study also sheds some light on the question of whether and when non-altruistic node cooperation is beneficial to WMNs.

In this paper, we propose a novel node clustering algorithm with effective tax-based subcarrier allocation tailored for wireless mesh networks with quality-of-service support. With effective frequency reuse, our proposed approach is shown to achieve a higher system throughput than a conflict-graph approach and a baseline approach. Also, our approach is demonstrated promising in performance tradeoff between packet delay and end-to-end transmission rate.

Quality-of-service (QoS) provisioning, high system throughput, and fairness assurance are indispensable for heterogeneous traffic in future wireless broadband networks. With limited radio resources, increasing system throughput and maintaining fairness are conflicting performance metrics, leading to a natural tradeoff between these two measures. Balancing system throughput and fairness is desired. In this paper, we consider an interference-limited wireless network, and derive a generic optimization framework to obtain an optimal relationship of system throughput and fairness with QoS support and efficient resource utilization, by introducing the bargaining floor. From the relationship curve, different degrees of performance tradeoff between throughput and fairness can be obtained by choosing different bargaining floors. In addition, our framework facilitates call admission control to effectively guarantee QoS of. multimedia traffic. The solutions of resource allocation obtained from the optimization framework achieve the pareto optimality, demonstrating efficient use of network resources.

Due to the requisite of multi-channel communications for high-speed data transmissions, power allocation for opportunistically exploiting fading wireless channels, and packet scheduling for quality-of-service provisioning, joint power-frequency-time resource allocation is indispensable. In this paper, we propose a low-complexity intra-cluster resource allocation algorithm, taking power allocation, subcarrier allocation, and packet scheduling into consideration. Numerical results demonstrate that our algorithm is close to optimal, and that our optimality-driven resource allocation algorithm outperforms a greedy algorithm, achieving higher resource utilization and better performance compromise among throughput, packet dropping rate, and packet delay.

Throughput and fairness are conflicting performance metrics, leading to a natural tradeoff between these two measures. In this paper, we derive a generic optimization framework to obtain a relationship of system throughput and fairness, by introducing the bargaining floor. From the relationship curve, different degrees of performance tradeoff between throughput and fairness can be obtained by choosing different bargaining floors. The solutions of resource allocation obtained from the optimization framework achieve the Pareto Optimality, demonstrating efficient use of network resources.

Abstract Wireless mesh networking is an emerging technology for future broadband wireless access. The ad hoc manner of wireless mesh networks (WMNs) determines that distributed medium access control (MAC) protocols are desired. Multimedia traffic with heterogeneous quality of service (QoS) requirements is expected to be supported in small‐, medium‐, and large‐scale WMNs. Wireless mesh routers in WMNs are located in fixed sites with low (or no) mobility and no power constraints, thus comprising a robust and reliable wireless mesh backbone. Different networking characteristics between the mesh backbone and various mesh client networks give rise to the demand of heterogeneous MAC design. Due to new design purposes and new networking structures, existing MAC protocols designed for mobile ad hoc networks may not be effective or efficient for multi‐purpose WMNs. This paper provides an overview of distributed MAC protocols based on their underlying design objectives and methodology, discusses their features and suitability for WMNs, and identifies potential challenges and open research issues. Copyright © 2006 John Wiley & Sons, Ltd.

In this paper, we derive closed-form expressions for the bit error rate (BER) for a distributed space-time block coded system, considering both perfect and imperfect channel estimation. For the special case of single relay, under the assumption of an appropriate power control, we show that the diversity order of two is obtained, achieving the maximum diversity order. Our analysis also demonstrates the existence of an error floor due to channel estimation errors.