Non-terrestrial networks (NTNs) have been proposed by 3GPP as a next-gen infrastructure paradigm to enhance terrestrial network coverage and capacity. Among various NTNs, ultra-dense LEO satellite networks are holding immense potential to offload terrestrial traffic and support increasing demands of emerging services, such as real-time applications with stringent delay requirements. However, some challenges in ultra-dense LEO satellite networks can severely degrade transmission performance, such as satellite movement, handover, sun outage and electromagnetic interference. Multipath transport protocol Multipath QUIC (MPQUIC) can be a viable solution to provide aggregated bandwidth and robust transmission in such environments. While the Out-of-Order (OFO) problem of MPQUIC can significantly degrade throughput and even block the entire transmission when path characteristics are much diverse. This issue is further amplified in LEO satellite networks due to the inherent orbital dynamics and space environment effects, which makes it exceptionally difficult to guarantee the demands of real-time services. In this paper, we propose a Prediction-Based Multipath QUIC Scheduler (PBMS) to enhance the transmission performance of real-time applications in NTNs. It primarily comprises two integral components:Path ManagerandBlock Scheduler.Path Managerdynamically allocates packets across available paths by considering packet loss rates, round-trip time (RTT), and congestion window states. AndBlock Schedulercalculates the estimated delivery time for each block in sender buffer according to the allocation scheme, and prioritizes block transmission based on the real-time requirements. We implement PBMS in Kuiper K3 shell network simulated by NS-3. The experiments show that PBMS can significantly guarantee and improve the transmission performance of real-time applications in NTNs.

Unsupervised domain adaptation aims to transfer knowledge from a labeled source domain to an unlabeled target domain. Existing methods, whether based on distribution matching or self-supervised learning, often focus solely on classifying individual source samples, potentially overlooking discriminative information. To address this limitation, we propose FlowUDA, a novel plugin method that enhances existing UDA frameworks by constructing semantically invariant flows from individual source samples to corresponding target samples, forming cross-domain trajectories. By leveraging a diffusion network guided by ordinary differential equations, FlowUDA ensures these flows preserve the topological structure of the source domain, maintaining their distinguishability. Our method then classifies these flows by sampling points along them and transferring labels from source samples, effectively capturing spatial relationships between domains. In essence, FlowUDA transforms the traditional point-based classification on individual source samples into flow-based classification on flows, allowing the model to learn richer, more discriminative features that bridge the gap between source and target domains. Extensive experiments on standard benchmarks demonstrate that integrating FlowUDA into existing UDA methods leads to notable performance gains, highlighting its effectiveness in addressing domain shift challenges.

Test-time adaptation with pre-trained vision-language models, such as CLIP, aims to adapt the model to new, potentially out-of-distribution test data. Existing methods calculate the similarity between visual embedding and learnable class embeddings, which are initialized by text embeddings, for zero-shot image classification. In this work, we first analyze this process based on Bayes theorem, and observe that the core factors influencing the final prediction are the likelihood and the prior. However, existing methods essentially focus on adapting class embeddings to adapt likelihood, but they often ignore the importance of prior. To address this gap, we propose a novel approach, Bayesian Class Adaptation (BCA), which in addition to continuously updating class embeddings to adapt likelihood, also uses the posterior of incoming samples to continuously update the prior for each class embedding. This dual updating mechanism allows the model to better adapt to distribution shifts and achieve higher prediction accuracy. Our method not only surpasses existing approaches in terms of performance metrics but also maintains superior inference rates and memory usage, making it highly efficient and practical for real-world applications.

The ultra-dense low-earth orbit (LEO) satellite networks (ULSNs) have become an important component of next-generation (6G) wireless networks, offering large-scale coverage and high-capacity service. Unlike traditional terrestrial network backbones deployed in closed, protected environments, satellite networks are exposed to highly dynamic environments, where space environment interference can severely affect channel conditions. This paper addresses the impact of sun outages, one of the most significant spatial interference factors, on satellite communication and proposes an adaptive coding and modulation scheme that dynamically adjusts the modulation and coding schemes (MCSs) based on real-time channel conditions to enhance the performance and communication quality of ULSNs. First, we model the channel environment of satellite-terrestrial microwave and inter-satellite laser links under sun outage interferences in ULSNs, which involves sun outage occurrence prediction and their interference quantification. Subsequently, to implement the ACM scheme, we use the seasonal autoregressive integrated moving average (SARIMA) algorithm combined with the bidirectional long short-term memory (BiLSTM) algorithm for time-series prediction of the channel state. Based on this, we apply the knowledge distillation-assisted proximal policy optimization (KD-PPO) algorithm to select the appropriate MCS. Simulation results show that by calculating the sun outage duration and the resulting interference, as well as predicting the channel state, the proposed KD-PPO algorithm can minimize the bit error rate (BER) and maximize the spectrum utilization (SU) in ULSNs.

We consider the problem of exploring travel demand statistics to optimize ride-sharing routing, which is to determine a route to transport multiple customers with similar itineraries and schedules in a cost-effective and timely manner. This problem is important for unleashing the economic and societal benefits of ride-sharing. Meanwhile, it is challenging due to the need to (i) meet the travel delay requirements of customers and (ii) make online decisions without knowing the exact travel demands beforehand. We present a general framework for exploring the new design space enabled by the demand-aware approach. We show that demand-aware ride-sharing routing is fundamentally a two-stage stochastic optimization problem. While the problem is combinatorial in nature and challenging, we exploit the two-stage structure to design an optimal solution that maximizes the expected reward of the route with polynomial time complexity, which makes it amenable to practical implementation. Our approach can be applied to optimize a variety of objectives in a ride-sharing trip, including revenue, driver’s profit, and total travel time reduction compared to serving customers individually. We carry out extensive simulations based on real-world travel demand traces in Manhattan. Simulation results show that applying our demand-aware solution increases the revenue by 26% against conceivable alternatives. As compared to the case without ride-sharing, our ride-sharing solution increases the driver’s profit per slot by 12% and decreases the total vehicle travel time (an indicator of greenhouse gas emission) by 16%.

Designing efficient random access is a vital problem for urgency-constrained packet delivery in uplink Internet of Things (IoT), which has not been investigated in depth so far. In this paper, we focus on unpredictable frame-synchronized traffic, which captures a number of scenarios in IoT communications, and generalize prior studies on this issue by considering a general ALOHA-like protocol, a general single-packet reception (SPR) channel, urgency-dependent throughput (UDT) based on a general urgency function, and the dynamic programming optimality. With a complete knowledge of the number of active users, we use the theory of Markov Decision Process (MDP) to explicitly obtain optimal policies for maximizing the UDT, and prove that a myopic policy is in general optimal. With an incomplete knowledge of the number of active users, we use the theory of Partially Observable MDP (POMDP) to seek optimal policies, and show that a myopic policy is in general not optimal by presenting a counterexample. Because of the prohibitive complexity to obtain optimal or near-optimal policies for this case, we propose two practical policies that utilize the inherent property of our MDP framework and channel model. Simulation results show that both outperform other alternatives. The robustness under relaxed system settings is also examined.

In this paper, the problem of designing the maximum-distance-separable (MDS) array codes for repairing a single node failure in a distributed storage system (DSS) is addressed. We consider the class of heterogeneous DSSs which can be represented as a fully connected storage network consisting of links having possibly different per-symbol transmission costs and assume that the repair process only allows a single-hop transmission from any helper node to a failed node. First, we consider the repair cost of a failed node to be the total transmission cost from all helper nodes incurred by the repair process. For a storage network represented by a complete weighted graph with the weights being the persymbol transmission costs, we derive a repair cost lower bound of every node. Somewhat surprisingly, even for a storage network represented by a complete weighted graph with time-varying weights, we can also construct a single optimal-repair-cost MDS array code that can achieve the repair cost lower bounds of all nodes. Next, we consider the repair cost of a failed node to be the worst-case transmission cost over all helper nodes incurred by the repair process. For a storage network represented by a static complete weighted graph, we derive a lower bound on the repair cost of every node and construct a single optimal-repair-cost MDS array code that can achieve the repair cost lower bounds of all nodes.

We consider a single-unicast networking scenario where a sender streams a flow at a fixed rate to a receiver across a multi-hop network, possibly using multiple paths. Transmission over a link incurs a traffic-dependent link delay. We optimize network delay concerning two popular metrics, namely maximum delay and average delay. Well-known pessimistic results state that a flow cannot simultaneously achieve a maximum delay and an average delay both within bounded-ratio gaps to optimal. Instead, we pose an optimistic note on the fundamental compatibility of the two delay metrics. Specifically, we design two polynomial-time solutions each of which can deliver (1 - ϵ)-fraction of the flow with maximum delay and average delay simultaneously within (1/ϵ)-ratio gap to optimal, for any ϵ ∈ (0, 1). We prove that the ratio (1/ϵ) is at least near-tight. Moreover, our solutions can be extended to the multiple-unicast setting. In this setting, the two delay metrics of our solutions are both within a boundedratio gap of (R/(Rmin · ϵ)) to optimal, where R (resp. Rmin) is the aggregate (resp. minimum) flow rate requirement of all sender-receiver pairs. Hence we pose a similar optimistic note. Simulations based on real-world continent-scale network topology show that the empirical delay gaps observed under practical settings can be much smaller than their theoretical counterparts. In addition, our solutions can achieve over 10% reduction on the maximum delay and average delay simultaneously, only in the cost of losing 3% traffic, as compared to a conceivable delay-aware baseline without traffic loss. Our results can be of particular interest to delay-centric networking applications that can tolerate a small fraction of traffic loss, including cloud video conferencing that recently attracts substantial attention.

Small-cell architecture is widely adopted by cellular network operators to increase spectral spatial efficiency. However, this approach suffers from low spectrum temporal efficiency. When a cell becomes smaller and covers fewer users, its total traffic fluctuates significantly due to insufficient traffic aggregation and exhibits a large “peak-to-mean” ratio. As operators customarily provision spectrum for peak traffic, large traffic temporal fluctuation inevitably leads to low spectrum temporal efficiency. To address this issue, in this paper, we advocate device-to-device (D2D) load-balancing as a useful mechanism. The idea is to shift traffic from a congested cell to its adjacent under-utilized cells by leveraging inter-cell D2D communication, so that the traffic can be served without using extra spectrum, effectively improving the spectrum temporal efficiency. We provide theoretical modeling and analysis to characterize the benefit of D2D load balancing, in terms of total spectrum requirements and the corresponding cost, in terms of incurred D2D traffic overhead. We carry out empirical evaluations based on real-world 4G data traces and show that D2D load balancing can reduce the spectrum requirement by 25% as compared to the standard scenario without D2D load balancing, at the expense of negligible 0.7% D2D traffic overhead.

Motivated by the proliferation of real-time applications in multimedia communication systems, tactile Internet, networked controlled systems, and cyber-physical systems, supporting delay-constrained traffic become critical for the communication system. In delay-constrained traffic, each packet has a hard deadline and if it cannot be delivered before its deadline, it becomes useless and will be removed from the system. In this work, we consider a slotted ALOHA system where multiple stations need to deliver delay-constrained traffic to a common receiver by accessing a shared channel. We prove that, under the frame-synchronized traffic pattern, the maximum system timely throughput converges to 1/e = 36.8% as the number of stations goes to infinity, which is the same as the asymptotic maximum system throughput for delay-unconstrained slotted ALOHA system with saturate traffic. While this is not completely surprising, we further investigate the speed of such a maximum system throughput approaching 1/e under borderline traffic.

We consider the problem of exploring travel demand statistics to optimize ride-sharing routing, in which the driver of a vehicle determines a route to transport multiple customers with similar itineraries and schedules in a cost-effective and timely manner. This problem is important for unleashing economical and societal benefits of ride-sharing. Meanwhile, it is challenging due to the need of (i) meeting travel delay requirements of customers, and (ii) making online decisions without knowing the exact travel demands beforehand. We present a general framework for exploring the new design space enabled by the demand-aware approach. We show that the demand-aware ride-sharing routing is fundamentally a two-stage stochastic optimization problem. We show that the problem is NP-Complete in the weak sense. We exploit the two-stage structure to design an optimal solution with pseudo-polynomial time complexity, which makes it amenable for practical implementation. We carry out extensive simulations based on real-world travel demand traces of Manhattan. The results show that using our demand-aware solution instead of the conventional greedy-routing scheme increases the driver's revenue by 10%. The results further show that as compared to the case without ride-sharing, our ride-sharing solution reduces the customers' payment by 9% and the total vehicle travel time (indicator of greenhouse gas emission) by 17%. The driver can also get 26% extra revenues per slot by participating in ride-sharing.

We consider the scenario where a source streams a flow at fixed rate to a receiver across a network, possibly using multiple paths. Transmission over a link incurs a delay modeled as a non-negative, non-decreasing and differentiable function of the link aggregated transmission rate. This setting models various practical network communication scenarios. We study network delay optimization concerning two popular metrics, namely maximum delay and average delay experienced by the flow. A well-known pessimistic result says that a flow cannot simultaneously achieve optimal maximum delay and optimal average delay, or even within constant-ratio gaps to the two optimums. In this paper, we pose an optimistic note on the fundamental compatibility of the two delay metrics. Specifically, we design two polynomial-time solutions to deliver (1 -ε) fraction of the flow with maximum delay and average delay simultaneously within 1/ε to the optimums for any ε ∈ (0,1). Hence, the two delay metrics are “largely” compatible. The ratio 1/ε is independent to the network size and link delay function, and we show that it is tight or near-tight. Simulations based on real-world continent-scale network topology verify our theoretical findings. Note that the proposed delay gap 1/ε, upon sacrificing ε fraction of the flow rate, is guaranteed even under the worst theoretical case setting. Our simulation results show that the empirical delay gaps observed under practical settings can be much smaller than 1/ε. Our results are of particular interest to delay-centric networking applications that can tolerate a small fraction of traffic loss, including cloud video conferencing that recently attracts substantial attention.

We study delay-constrained input-queued switches where each packet has a deadline that will expire if it is not delivered before its deadline. Such a new scenario is motivated by the proliferation of real-time applications in multimedia communication systems, tactile Internet, networked controlled systems, and cyber-physical systems. One fundamental problem centering around the performance metric of timely throughput is how to characterize the capacity region. In this work, for the frame-synchronized traffic pattern, we characterize the capacity region by a polynomial number of linear constraints.

In this paper, we study the delay-constrained input-queued switch, where each packet has a deadline and it will expire if it is not delivered before its deadline. Such new scenario is motivated by the proliferation of real-time applications in multimedia communication systems, tactile Internet, networked controlled systems, and cyber-physical systems. The delay-constrained input-queued switch is completely different from the well-understood delay-unconstrained one and thus poses new challenges. We focus on three fundamental problems centering around the performance metric of timely throughput: (i) how to characterize the capacity region? (ii) how to design a feasibility/throughput-optimal scheduling policy? and (iii) how to design a network-utility-maximization scheduling policy? We use three different approaches to solve these three fundamental problems. The first approach is based on Markov Decision Process (MDP) theory, which can solve all three problems. However, it suffers from the curse of dimensionality. The second approach breaks the curse of dimensionality by exploiting the combinatorial features of the problem. It gives a new capacity region characterization with only a polynomial number of linear constraints. The third approach is based on the framework of Lyapunov optimization, where we design a polynomial-time maximum-weight $T$ -disjoint-matching scheduling policy which is proved to be feasibility/throughput-optimal. Our three approaches apply to the frame-synchronized traffic pattern but our MDP-based approach can be extended to more general traffic patterns.

We consider a timely transportation problem where a heavy-duty truck travels between two locations across the national highway system, subject to a hard deadline constraint. Our objective is to minimize the total fuel consumption of the truck, by optimizing both route planning and speed planning. The problem is important for cost-effective and environment-friendly truck operation, and it is uniquely challenging due to its combinatorial nature as well as the need of considering hard deadline constraint. We first show that the problem is NP-complete; thus exact solution is computational prohibited unless P = NP. We then design a fully polynomial time approximation scheme (FPTAS) to solve it. While achieving highly-preferred theoretical performance guarantee, the proposed FPTAS still suffers from long running time when applying to national-wide highway systems with tens of thousands of nodes and edges. Leveraging elegant insights from studying the dual of the original problem, we design a heuristic with much lower complexity. The proposed heuristic allows us to tackle the energy-efficient timely transportation problem on large-scale national highway systems. We further characterize a condition under which our heuristic generates an optimal solution. We observe that the condition holds in most of practical instances in numerical experiments, justifying the superior empirical performance of our heuristic. We carry out extensive numerical experiments using real-world truck data over the actual U.S. highway network. The results show that our proposed solutions achieve 17% (resp. 14%) fuel consumption reduction, as compared with a fastest path (resp. shortest path) algorithm adapted from common practice.

We consider the scenario where a cloud service provider (CSP) operates multiple geo-distributed datacenters to provide Internet-scale service. Our objective is to minimize the total electricity and bandwidth cost by jointly optimizing electricity procurement from wholesale markets and geographical load balancing (GLB), i.e., dynamically routing workloads to locations with cheaper electricity. Under the ideal setting where exact values of market prices and workloads are given, this problem reduces to a simple LP and is easy to solve. However, under the realistic setting where only distributions of these variables are available, the problem unfolds into a non-convex infinite-dimensional one and is challenging to solve. Our main contribution is to develop an algorithm that is proven to solve the challenging problem optimally and efficiently, by exploring the full design space of strategic bidding. Trace-driven evaluations corroborate our theoretical results, demonstrate fast convergence of our algorithm, and show that it can reduce the cost for the CSP by up to 20% as compared to baseline alternatives. Our study highlights the intriguing role of uncertainty. While variability in workloads deteriorates the cost-saving performance of joint electricity procurement and GLB, counter-intuitively, variability in market prices can be exploited to achieve a cost reduction even larger than the setting without price variability.

We study a delay-sensitive information flow problem where a source streams information to a sink over a directed graph G = (V, E) at a fixed rate R possibly using multiple paths to minimize the maximum end-to-end delay, denoted as the Min-Max-Delay problem. Transmission over an edge incurs a constant delay within the capacity. We prove that Min-Max-Delay is weakly NP-complete, and demonstrate that it becomes strongly NP-complete if we require integer flow solution. We propose an optimal pseudo-polynomial time algorithm for Min-Max-Delay, with time complexity O(log(Ndmax)(N5dmax2.5)(log R + N2dmaxlog(N2dmax))), where N =△max{|V|, |E|} and dmaxis the maximum edge delay. Besides, we show that the integrality gap, which is defined as the ratio of the maximum delay of an optimal integer flow to the maximum delay of an optimal fractional flow, could be arbitrarily large.

The device-to-device load balancing (D2D-LB) paradigm has been advocated in recent small-cell architecture design for cellular networks. The idea is to exploit inter-cell D2D communication and dynamically relay traffic of a busy cell to adjacent under-utilized cells to improve spectrum temporal efficiency, addressing a fundamental drawback of small-cell architecture. Technical challenges of D2D-LB have been studied in previous works. The potential of D2D-LB, however, cannot be fully realized without providing proper incentive mechanism for device participation. In this paper, we address this economical challenge using an online procurement auction framework. In our design, multiple sellers (devices) submit bids to participate in D2D-LB and the auctioneer (cellular service provider) evaluates all the bids and decides to purchase a subset of them to fulfill load balancing requirement with the minimum social cost. Different from similar auction design studies for cellular offloading, battery limit of relaying devices imposes a time-coupled capacity constraint that turns the underlying problem into a challenging multi-slot one. Furthermore, the dynamics in the input to the multi-slot auction problem emphasize the need for online algorithm design. We first tackle the single-slot version of the problem, show that it is NP-hard, and design a polynomial-time offline algorithm with a small approximation ratio. Building upon the single-slot results, we design an online algorithm for the multi-slot problem with sound competitive ratio. Our auction algorithm design ensures that truthful bidding is a dominant strategy for devices. Extensive experiments using real-world traces demonstrate that our proposed solution achieves near offline-optimum and reduces the cost by 45% compared with an alternative heuristic.

Most existing wireless networking solutions are best-effort and do not provide any delay guarantee required by important applications, such as mobile multimedia conferencing and real-time control of cyber-physical systems. Recently, Hou and Kumar provided a novel framework for analyzing and designing delay-guaranteed wireless networking solutions. While inspiring, their idle-time-based analysis applies only to flows with a special traffic pattern called the frame-synchronized setting. The problem remains largely open for general traffic patterns. This paper addresses this challenge by proposing a general framework that characterizes and achieves the complete delay-constrained capacity region with general traffic patterns in single-hop downlink access-point wireless networks. We first show that the timely wireless flow problem is fundamentally an infinite-horizon Markov decision process (MDP). Then, we judiciously combine different simplification methods to prove that the timely capacity region can be characterized by a finite-size convex polygon. This for the first time allows us to characterize the timely capacity region of wireless flows with general traffic patterns. We then design three scheduling policies to optimize network utility and/or support feasible timely throughput vectors for general traffic patterns. The first policy achieves the optimal network utility and supports any feasible timely throughput vector but suffers from the curse of dimensionality. The second and third policies are inspired by our MDP framework and are of much lower complexity. Simulation results show that both achieve near-optimal performance and outperform other existing alternatives.

Most existing wireless networking solutions are best-effort and do not provide any delay guarantee required by important applications such as the control traffic of cyber-physical systems. Recently, Hou and Kumar provided the first framework for analyzing and designing delay-guaranteed network solutions. While inspiring, their idle-time-based analysis appears to apply only to flows with a special traffic (arrival and expiration) pattern, and the problem remains largely open for general traffic patterns. This paper addresses this challenge by proposing a new framework that characterizes and achieves the complete delay-constrained capacity region with general traffic patterns in single-hop downlink access-point wireless networks. We first formulate the timely capacity problem as an infinite-horizon Markov Decision Process (MDP) and then judiciously combine different simplification methods to convert it to an equivalent finite-size linear program (LP). This allows us to characterize the timely capacity region of flows with general traffic patterns for the first time in the literature. We then design three timely-flow scheduling algorithms for general traffic patterns. The first algorithm achieves the optimal utility but suffers from the curse of dimensionality. The second and third algorithms are inspired by our MDP framework and are of polynomial-time complexity. Simulation results show that both achieve near-optimal performance and outperform other existing alternatives.

One important issue associated with spectrum management in heterogeneous cognitive radio network is: how to appropriately allocate the spectrum to the secondary senderdestination (S-D) pair for sensing and utilization. In this work, the authors investigate the spectrum allocation problem under a more practical scenario, taking the heterogeneous characteristics of both secondary S-D and PU channels into consideration. With the objective to maximize the achievable throughput for secondary S-D, we formulate the spectrum allocation problem as a linear integer optimization problem under spectrum availability constraint, spectrum span constraint and interference free constraint. This problem is NP-complete, and a recent result in theoretical computer science called randomized rounding algorithm with polynomial computational complexity is developed to find the ρ-approximation solution. Evaluation results show that our proposed algorithm can achieve a close-to-optimal solution while keeping the complexity low.

Small-cell architecture is widely adopted by cellular network operators to increase network capacity. By reducing the size of cells, operators can pack more (low-power) base stations in an area to better serve the growing demands, without causing extra interference. However, this approach suffers from low spectrum temporal efficiency. When a cell becomes smaller and covers fewer users, its total traffic fluctuates significantly due to insufficient traffic aggregation and exhibiting a large "peak to-mean" ratio. As operators customarily provision spectrum for peak traffic, large traffic temporal fluctuation inevitably leads to low spectrum temporal efficiency. In this work, we first carryout a case-study based on real-world 3G data traffic traces and confirm that 90% of the cells in a metropolitan district are less than 40% utilized. Our study also reveals that peak traffic of adjacent cells are highly asynchronous. Motivated by these observations, we advocate device-to-device (D2D) load-balancing as a useful mechanism to address the fundamental drawback of small-cell architecture. The idea is to shift traffic from a congested cell to its adjacent under-utilized cells by leveraging inter-cell D2D communication, so that the traffic can be served without using extra spectrum, effectively improving the spectrum temporal efficiency. We provide theoretical modeling and analysis to characterize the benefit of D2D load balancing, in terms of sum peak traffic reduction of individual cells. We also derive the corresponding cost, in terms of incurred D2D traffic overhead. We carry out empirical evaluations based on real-world 3G data traces to gauge the benefit and cost of D2D load balancing under practical settings. The results show that D2D load balancing can reduce the sum peak traffic of individual cells by 35% as compared to the standard scenario without D2D load balancing, at the expense of 45% D2D traffic overhead.

Cognitive radio is proposed as an efficient way to address the issue of spectrum shortage and under- utilization, in which cooperative spectrum sensing (CSS) is used to enhance the sensing performance. One of the most fundamental problems of CSS is: how to appropriately assign the secondary users (SUs) to sense the primary user (PU) channels? In this paper, We study the CSS problem under a more practical scenario where taking the heterogeneous characteristics of both SUs and PU channels into consideration. With the objective to maximize the achievable throughput for SUs, we propose a cluster- based CSS to obtain a proper assignment policy, in which all the cluster members cooperative in sensing the same channels, moreover, the CSS problem is formulated as a Maximum Weight One-Sided Biclique Problem, and a greedy heuristic algorithm is proposed to find the suboptimal assignment policy. To evaluate the tradeoff between sensing accuracy and spectrum opportunity, the simulation is conducted between the number of sensed channels and the achievable throughput.

In this paper, we tackle the energy efficiency (EE) issue in uplink virtual MIMO systems, which requires the optimization of two interlaced parameters: the number of constituent mobile users in the virtual MIMO and their corresponding power allocation. By exploiting the fact that increasing the number of active users can increase the number of contributors to the total EE on one hand but reducing the diversity order for each single user on the other, we can show the existence of an optimal transmission mode and find a simple way for its search. Through in-depth analysis, we show the existence of a unique globally optimal power allocator for the case without power constraints, and further reveal the impact of power constraints upon power allocation, as compared to its global counterpart, aiming to provide a powerful means for power-constrained EE optimization. Finally, we establish theories, for homogeneous networks, to narrow down the search range for possible transmission modes, leading to a significant reduction of computational complexity in optimization. Simulation results are presented to substantiate the proposed schemes and the corresponding theories.

Driven by green communications, energy-efficient transmission is becoming an important design criterion for wireless systems, aiming to extend the life cycle of batteries in mobile devices. In this paper, we tackle the energy efficiency (EE) issue in uplink virtual multiple-input multiple-output (MIMO) systems, which requires the optimization of two interlaced parameters: the number of constituent mobile users in the virtual MIMO and their corresponding power allocation. The former parameter is a structural parameter defining the size of the virtual MIMO (usually known as the transmission mode) and its optimization relies on the method of enumeration. The difficulty is further aggravated by the fact that the EE is a non-convex function of power, even for a given transmission mode. By exploiting the fact that increasing the number of active users can increase the number of contributors to the total EE on one hand but reducing the diversity order for each single user on the other, we can show the existence of an optimal transmission mode and find a simple way for its search. Through in-depth analysis, we show the existence of a unique globally optimal power allocator for the case without power constraints under the assumption of zero-forcing receivers, and further reveal the impact of power constraints upon power allocation, as compared to its global counterpart, aiming to provide a powerful means for power-constrained EE optimization. Finally, we establish theories, for isometric networks, to narrow down the search range for possible transmission modes, leading to a significant reduction of computational complexity in optimization. Simulation results are presented to substantiate the proposed schemes and the corresponding theories.

Energy-efficient transmission is increasing in importance for wireless system design because of limited battery power in mobile devices. In this paper, we consider energy efficiency optimization in uplink virtual MIMO systems. First, accurate closed-form expressions are derived for the achievable energy efficiency for both multi-user (MU) transmission mode and single-user (SU) transmission mode, which are based on the ergodic throughput, transmit power and circuit power. Then, we demonstrate the existence of unique globally optimal energy efficiency for SU and MU mode, respectively. Since users have data rates requirement and peak power limits, we further consider power constraint, and develop joint mode switching with power loading schemes to optimize energy efficiency for both homogeneous and heterogeneous networks. Finally, our simulation results show that the proposed adaptive transmission strategies significantly improve energy efficiency.

We consider how to efficiently employ D2D communications for secondary users (SUs) in a cognitive cellular network. In this network, primary users (PUs) transmit via base station normally, while SUs can employ multiple transmission modes. One is to transmit via base station (BS mode), and the other is to employ D2D communication (D2D mode) due to the scarce idle spectrum. The SUs who have the potential to transmit to each other using D2D mode form a group. Within this group, they can transmit to each other via BS mode or using D2D mode directly. Outside this group, only BS mode is available. To investigate how to employ D2D mode into this network, first we define the utilities of SUs employing BS mode and D2D mode respectively considering achieved data rate, power consumption, price of unit bandwidth and the impact of interference. Then we analyze the optimal power allocation for each mode. To optimize SUs' strategies of mode selection, we adopt replicator dynamics in evolution theory to model the behaviors of SUs. Furthermore, we prove the existence of SUs' evolutionary stable strategy (ESS) of the mode selection process. Based on our model, we finally propose a distributed protocol for SUs within a D2D group to converge to ESS automatically. Numerical results show that our proposed protocol is not only efficient to achieve ESS with improved network performance, but also robust in ESS.