In this paper, we investigate the problem of minimizing the long-term energy consumption of mobile edge computing (MEC) systems combining reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) techniques, based on the dynamic scenarios with sequential task arrival and time-varying channels. We jointly optimize the transmission power, transmission time, offloading ratio, local computing frequency, MEC server’s computing resource allocation, and phase shift of RIS elements. The problem is formulated as a challenging time-dependent non-linear programming problem with a large number of variables. By theoretically deriving the optimal expression of server computing frequency and local computing frequency, we simplify the original problem and decompose it into two nested subproblems, namely, the joint offloading ratio and communication process optimization (JORCPO) subproblem and the RIS’s phase shift optimization (RPSO) subproblem. Then, we propose a two-level approach for the solution. In the outer level, we formulate the RPSO subproblem as a deep reinforcement learning (DRL) problem and adopt the proximal policy optimization (PPO) algorithm to obtain the real-time decision of RIS’s phase shift. In the inner level, based on the given RIS’s phase shift, we first simplify the JORCPO subproblem by deriving the closed-form expression of transmission power and then adopt the Lagrange dual (LD) method to obtain the near-optimal solution of transmission time and offloading ratio. Simulation results testify the performance advantage of the proposed solution.

Accurate modeling of proximity relationships between individuals and groups in urban spaces is critical for public safety alerts, emergency evacuation planning, commercial flow analysis, and urban planning optimization. Existing indoor localization techniques-including TOA/TDOA/AoA geometry-based methods, visual SLAM, inertial navigation (PDR), and RSSI/UWB fingerprinting-achieve high accuracy in static environments but rely heavily on large-scale manual labeling, strict clock synchronization, or dense anchor deployment, and struggle to accommodate both trajectory continuity and dynamic environmental changes. To address these limitations, we propose a dual-modal deep learning framework based on Graph Convolutional Networks and Multidimensional Scaling embedding. First, channel feature similarities are computed from CIR sequences sampled continuously by mobile terminals, and an attentionenhanced proximity graph is constructed. Then, the graph adjacency matrix is fed into the MDS algorithm to recover the relative low-dimensional layout of observation points, enabling robust reconstruction of crowd density and flow topology. To mitigate class imbalance and manage positive and negative pair ratios both within and across trajectories, we introduce a spatiotemporal pair generation strategy and employ a weighted binary crossentropy loss during training. Simulation results demonstrate that under sparse sampling and dynamic conditions, our method significantly outperforms traditional thresholding and existing unsupervised baselines in both precision and recall, and approaches the performance of supervised methods under moderate label noise. This framework offers an efficient, annotation-free solution for indoor crowd density estimation and flow analysis.

The converging trends of reinforcement learning (RL) control and cloud-fog automation in industrial cyber-physical systems impose multiple challenges for communications to cope with stringent requirements in latency, reliability, control effectiveness and bifurcating user demands. Progressive goal-oriented (GO) communication is a promising technology to tackle the above challenges. This paper takes a two-step approach to design the first progressive codec of GO communications tailored for RL control tasks. The first step is to design a variable-rate coding scheme that extends the boundaries of rate regimes. This step is achieved by empowering the hierarchical variational autoencoder (HVAE) framework with novel algorithms such as mutual information based soft state abstraction (MISA). The second step is to transform variable-rate encoding into progressive encoding. This is achieved by applying residual-based encoding techniques upon latent representations learned by deep neural networks. Experiments on the Cartpole Swingup task demonstrate that the proposed progressive codec can facilitate smooth transitions from the ultra-low rate regime to regular rate regime, while achieving the state-of-the-art performance in terms of rate-distortion-effectiveness tradeoff.

In addressing the dataset requirements for indoor inertial navigation technology based on deep learning, this paper proposes a comprehensive dataset construction and data preprocessing scheme which includes equipment preparation, data collection and data alignment. To meet the demands of deep learning for data size and distribution diversity, a detailed data collection strategy is introduced. For the acquired dataset, a robust data processing methodology is employed, involving time calibration, data alignment and coordinate system transformation to synchronize sensor data with groundtruth data. The dataset is then trained and tested on the Baseline network, followed by ablation experiments. The results show that the dataset constructed by the proposed method has the best trajectory reconstruction performance, while the small-size dataset that does not contain complex motion patterns has obvious drift and the dataset that does not use the proposed data alignment algorithm has a very poor performance. These outcomes validate the proposed scheme’s capability to enhance the performance of indoor inertial navigation models.

Multiple description coding (MDC) is a type of error-resilient source coding that is advantageous for communications over channels with high loss and long delays. While neural network-based MDC promises higher compression efficiency and better semantic awareness, the aspect of semantic awareness is under-investigated. This paper is among the first efforts to study MDCs with competing performance goals known as the distortion-classification tradeoff. A new design concept called semantic polarization is proposed to contradict the traditional philosophy of balanced side encoder designs. We present a simple conceptual model to demonstrate the advantage of polarized MDC in having higher probabilities of satisfying at least one performance goal. We also propose a detailed implementation of polarized MDC based on SRGANs. Experiments on public datasets show that compared with state-of-the-art single description neural image codecs, the proposed MDC has multiple benefits in terms of enlarged rate range, superior semantic protection, and better perception quality, at the cost of slightly reduced but competitive distortion performance.

Navigating challenging conditions characterized by stringent bandwidth constraints and noisy channels, Distributed Sensor Networks (DSNs) demand robust feature compression techniques for accurate data fusion. The advent of Supervised Contrastive Learning with Mask-Sparsity (SCL-MS) presents a neural approach to feature compression, offering compact and semantically aligned continuous representations tailored for DSNs. However, the unconventional structure of SCL-MS poses a challenge for traditional neural scalar quantization. This paper introduces an innovative quantization method, Doubly Progressive Quantization, specifically crafted for SCL-MS. Experiments on distribution image classification tasks show that with marginal reduction in accuracy, the proposed method can achieve a compression gain of 37 times compared with the baseline without quantization. In addition, the proposed method is shown to outperform decision-level data fusion in terms of noise-resilience and node-scaling.

In this article, a dynamic multiuser multiaccess mobile-edge computing (MEC) network where users split their tasks into multiple parts and offload them concurrently to multiple MEC servers via NOMA is investigated. In order to reduce the long-term energy, a complicated time-dependent nonconvex problem with many deeply coupled variables is formulated. We employ a novel approach that integrates the theoretical derivation and the deep reinforcement learning (DRL) algorithm to solve the problem in real time. In particular, we decompose the considered energy minimization problem into a computational resource and transmission power allocation subproblem as the offloading decision and offloading time of each user are known, and an offloading decision and offloading time allocation subproblem. Then, the first subproblem is decomposed into many independent problems, and we theoretically solve them in parallel and derive the optimal solution. For the second subproblem, in order to obtain the real-time allocation of offloading time and offloading decision, we apply a DRL algorithm that adjusts the reward function with a penalty mechanism while the total offloading ratio constraint is unsatisfied or the transmission power and computational resource allocation subproblem does not have a solution. Via simulations, we verify the optimality of the proposed theoretical derivation-based solutions, and, demonstrate that the system performance can be significantly improved by the proposed approach.

Semantic communications is a key enabler of the Internet of Things (IoT). By focusing on the semantic meaning of data rather than bit-level recovery, it allows intelligent agents to communicate necessary information at much lower rates. A promising technique for semantic communications is discrete neural representation learning (DNRL). The main idea is to learn discrete symbols from low-level, high dimensional sensory data, such that each symbol is grounded to a meaningful pattern in the sensory domain. This paper proposes a DNRL scheme that integrates three mechanisms into a coherent framework: contrastive learning, sparse coding, and neural index quantization. The proposed scheme is applied to public image datasets for lossy image compression with a downstream classification task. Results show that the proposed approach produces a highly compact continuous latent representation and a semantic discrete representation, with marginal degradation to the classification accuracy. The interpretability and consistency of the learned sub-symbolic discrete representations are validated by experiments of neural-net dissection, neural-net visualization, and MaxAmp-K classification test, a concept that we propose to evaluate classification performance of extremely compressed signals. Finally, the discrete representations are shown to be useful in rate-adaptive distributed sensing applications at the low-to-medium signal-to-noise ratios (SNR).

In this article, we aim to minimize the longterm energy consumption of a partial offloading mobile-edge computing (MEC) system with noncompletely overlapping nonorthogonal multiple access (NCO-NOMA) when time-varying channels and continuous tasks arrival are considered. Different from the existing NCO-NOMA-assisted MEC studies, the task data transmission, offloading decision, and task computation in users and the MEC server are jointly optimized. We describe the considered energy minimization problem as a complex nonconvex problem with lots of tightly correlated variables. To solve it, we decompose it into a set of computational and communication resource optimization subproblems and a computational resource optimization subproblem at the MEC server. For the computational and communication resource optimization subproblem, we introduce new variables to transform it into a standard difference of convex functions (DC) programming and propose the concave–convex procedure (CCCP) algorithm to solve it. To find out the real-time solution for the computational resource optimization subproblem at the MEC server, we propose a deep reinforcement learning (DRL) algorithm by adding penalty mechanisms to the reward function. Simulations demonstrate that the proposed solution converges quickly and achieves satisfying performance.

Multipath propagation greatly affects the accuracy of time of arrival (ToA)-based indoor positioning when line-of-sight (LOS) signals are only used. In this paper, we present a novel real-time and low computation complexity multipath-assisted ToA positioning method, namely MSC-MAP. The delays of reflected signals are taken as additional spatial observations to compensate for an insufficient number of physical transmitters to locate a moving user equipment (UE). Virtual anchors are used to model the propagation path of reflected signals, whose locations are obtained via a multi-state constraint estimator, along with the trajectory of UE. In addition, we demonstrate the mismatch problem in data association and its impact on positioning performance. To achieve real-time processing, we propose two robust multipath-assisted positioning methods with mismatch alleviation by randomly selecting subset and constraint relaxation respectively, to meet various computational complexity requirements. Simulation results show that, for the MSC-MAP method, the mean square error of the position is generally less than 0.2 m in challenging indoor environments. Among mismatch alleviation algorithms, positioning error is reduced by 69% even when the percentage of mismatched measurement data is as high as 42%. The proposed algorithms can also efficiently handle signals with non-Gaussian impairments, a common characteristic in real-world data. Moreover, these algorithms can substantially improve positioning performance while adding minimal computation time in the presence of measurement mismatches, outperforming state-of-the-art methods utilizing different data association techniques.

While sensing imagery in space missions has broad applications, the growing image resolution and data volume have caused a major challenge due to limited deep space channel capacities. To address this challenge, semantics-aware image compression becomes a promising direction. This paper is motivated to explore lossy compression at the ultra-low rate regime, which is a deviation from the high-fidelity- oriented tradition. Specifically, we propose an ultra-low rate deep image compression (DIC) codec by synthesizing multiple neural computing techniques such as style generative adversarial network (GAN), inverse GAN mapping, and contrastive disentangled representation learning. In addition, a residual-based progressive encoding framework is proposed to enable smooth transitions from the ultra-low rate regime to near- lossless regime. Experiments on the FFHQ and DOTA dataset demonstrate that compared with existing DICs, the proposed DIC can push the minimum rate boundary by about one order of magnitude while preserving the semantic attributes and maintaining a high perception quality. We further elaborate the design considerations for cross-rate-regime progressive DIC. Our study confirm that a semantically disentangled DIC holds the promise to bridge multiple rate regimes.

In this article, we investigate the long-term energy minimization for nonorthogonal multiple access (NOMA)-based mobile edge computing (MEC) systems with user mobility, continuous tasks arrival, and time-varying channel when the reconfiguration energy cost caused by dynamic voltage frequency scaling (DVFS) technology and the effect of imperfect successive interference cancelation (SIC) decoding in NOMA transmission are taken into account. We formulate the considered problem as a nonconvex optimization problem. To solve it, we decompose it into a computation resource and transmit power optimization subproblem, and an offloading ratio and transmission time optimization subproblem. We first show that when the offloading ratio and transmission time are given, the optimal local CPU frequency, the optimal computation resource allocation in the base station, and the optimal transmit power can be theoretically derived. Then, based on the above theoretical derivation, a soft actor–critic (SAC)-based deep reinforcement learning (DRL) algorithm is proposed to learn the near-optimal offloading ratio and transmission time for users. Simulation results show that the proposed algorithms can significantly improve the system performance.

Progressive deep image compression (DIC) with hybrid contexts is an under-investigated problem that aims to jointly maximize the utility of a compressed image for multiple contexts or tasks under variable rates. In this paper, we consider the contexts of image reconstruction and classification. We propose a DIC framework, called residual-enhanced mask-based progressive generative coding (RMPGC), designed for explicit control of the performance within the rate-distortion-classification-perception (RDCP) trade-off. Three independent mechanisms are introduced to yield a semantically structured latent representation that can support parameterized control of rate and context adaptation. Experimental results show that the proposed RMPGC outperforms a benchmark DIC scheme using the same generative adversarial nets (GANs) backbone in all six metrics related to classification, distortion, and perception. Moreover, RMPGC is a flexible framework that can be applied to different neural network backbones. Some typical implementations are given and shown to outperform the classic BPG codec and four state-of-the-art DIC schemes in classification and perception metrics, with a slight degradation in distortion metrics. Our proposal of a nonlinear-neural-coded and richly structured latent space makes the proposed DIC scheme well suited for image compression in wireless communications, multi-user broadcasting, and multi-tasking applications.

Multipath-assisted positioning is a promising way to realize robust and accurate indoor positioning, taking advantage of the environmental information carried by multipath signals. In this paper, we propose a novel multipath-assisted time-of-arrival (TOA) positioning method for complex indoor scenarios without a-priori knowledge of the floor plan. We use virtual anchors (VAs) to model the propagation path of reflected signals. The trajectory of the user equipment (UE) and the locations of VAs are iteratively estimated using two unscented Kalman filters (UKFs), considering the changeable visibility of VAs due to the birth and death of multipath components (MPCs). To use TOA measurements of MPCs as input for the update phase of the UKF, we present a data association method based on multipath peak flow tracking by baseband signal processing to establish the correspondence between measurements and VAs. We derive the analytical solution using the received power of MPCs for multipath tracking, which can realize low computational complexity data association. Simulation results show that, most of the MPCs can be correctly tracked using the peak flow method, even if some MPCs are densely distributed. For the proposed iterative UKF, the mean square error of the UE's position, with associated measurements obtained by the peak flow method as input, is generally less than 0.42 m in the complex indoor scenario.

On-demand streaming of high-quality video content is a widely anticipated vehicular infotainment service. How to reduce the cost of content streaming is a primary concern of the service providers, but is still an underinvestigated subject in the literature. This paper proposes an integrated mobile streaming and caching scheme that jointly leverages two communication service tiers and on-board caching resource for cost reduction. Algorithms are presented to achieve optimal buffering at the session level and optimal caching at the device level. An analytical framework is established to characterize the average cost as a function of the streaming rate in a large scale network. Numerical results demonstrate how the “cost-streaming rate” function changes with vehicle density, network congestion level, content length, and average packet transmission time. We learn an important insight that there is a minimum cost threshold even when the streaming rate approaches zero. We also show that the proposed protocol can effectively reduce the overall cost when the network is not congested. Our findings can provide useful guidelines for the business planning and operation of vehicular content streaming services.

By providing shared and flexible communication, computation, and storage resources along the cloud-to-things continuum, fog computing has become an attractive technology to support delay-sensitive applications in Internet of Things (IoT) and future wireless networks. Consider a typical heterogeneous fog network consisting of different types of fog nodes (FNs), wherein some task nodes (TNs) have computation-intensive and delay-sensitive tasks, while some helper nodes (HNs) have spare computation resources for sharing with their neighboring nodes. In order to minimize the delay of every task, these TNs and HNs should be effectively associated in a distributed manner, which is the fundamental multi-task multi-helper (MTMH) problem. To tackle this challenging problem, a potential game called paired offloading of multiple tasks (POMT) is formulated and studied. Theoretical analysis proves the existence of the Nash equilibrium (NE) for this proposed game. Further, the corresponding POMT algorithm is developed for every TN to achieve the NE of the general game. The analytical and simulation results show that our POMT algorithm can offer the near-optimal performance in system average delay and delay reduction ratio (DRR), and achieve more number of beneficial TNs, at two orders of magnitude lower complexity than a centralized optimal algorithm for computation offloading.

Cellular communication networks are plagued with redundant capacity, which results in low utilization and cost-effectiveness of network capital investments. The redundant capacity can be exploited to deliver secondary traffic that is ultra-elastic and delay-tolerant. In this paper, we propose an analytical framework to study the capacity-delay tradeoff of elastic/secondary traffic in large scale cellular networks with spectrum aggregation. Our framework integrates stochastic geometry and queueing theory models and gives analytical insights into the capacity-delay performance in the interference limited regime. Closed-form results are obtained to characterize the mean delay and delay distribution as functions of per user throughput capacity. The impacts of spectrum aggregation, user and base station densities, traffic session payload, and primary traffic dynamics on the capacity-delay tradeoff relationship are investigated. The fundamental capacity limit is derived and its scaling behavior is revealed. Our analysis shows the feasibility of providing secondary communication services over cellular networks and highlights some critical design issues.

Over the past decade, deep learning developed rapidly and had significant impact on a variety of application domains. It has been applied to the field of human activity recognition to substitute for well-established analysis techniques that rely on handcrafted feature extraction and classification methods in recent years. However, less attentions have been paid to the influence of training data on recognition accuracy. In this paper, we assessed the influence factors of data volume and data balance in human activity recognition when using deep learning approaches. We evaluated the relationship between data volumes of training dataset and predict accuracy of deep learning algorithms. Given the impact of the data balance between activity categories on the recognition accuracy, we modified the SMOTE algorithm so that it can be applied to human activity recognition. Results show that when the data volume is small (<;4M), the recognition accuracy increased quickly with the increase of the quantity of training data. However, the growth trend of recognition accuracy slows down when the data quantity reaches 4 million. Further increase the data volume does not significantly improve the activity recognition performance. So we can conclude that 4 million data volume can ensure a sufficient accuracy for human activity recognition. Meanwhile, the data set balance operation can not only improve the recognition accuracy of minority categories, but also helps to increase the overall accuracy.

Cellular communication networks are plagued with redundant capacity, which results in low infrastructure utilization and low cost-effectiveness of network capital investments. This paper proposes a context-aware system that recycles the redundant capacity to provide cognitive secondary content delivery services. The proposed system applies bottom- up cross-layer design to provide best-effort access to recommended contents tailored to the real-time traffic condition. To characterize the secondary service performance of the proposed system, a discrete time Markov model (DTMC) is adopted for a novel cross-layer analysis that incorporates physical layer, data link layer and application layer. Unlike existing literature, our analysis reveals the impact of content file size (session length) on various quality-of- service (QoS) metrics such as blocking probability, average throughput, and delay distribution. Our analyses provide useful guidelines for cross-layer optimization of cognitive content delivery systems.

Recent literature has suggested the benefits of integrating licensed radio and cognitive radio into a hybrid cooperative communication system. The fundamental properties of such hybrid systems, however, have not been thoroughly investigated. This paper studies the hybrid cognitive Gaussian relay channel (HCGRC), which uses licensed radio resource (RR) and cognitive/unlicensed RR for forward and relay transmissions, respectively. HCGRC fundamentally differs from conventional relay channels in that the licensed and cognitive RRs are not subject to a total resource constraint and that the cognitive RR is opportunistic in nature. With respect to both the upper and lower bounds, we derive the optimal power-bandwidth allocation strategies for the cognitive relay to maximize the capacity, spectrum efficiency (SE), and energy efficiency (EE). The Pareto-optimal EE-SE tradeoff curve is also derived analytically. Our study leads to two key observations. First, the multi-objective power-bandwidth allocation problem is characterized by five regions, each representing a unique performance tradeoff. Second, the reliability of cognitive RR has no impact on the EE-SE tradeoff given unlimited bandwidth and power.

Motivated by the deployment of cognitive radio (CR) based relays in cellular networks, this paper studies the fundamental limits of heterogeneous cognitive Gaussian relay channels (HCGRCs). Unlike conventional relay channels, in HCGRC a source transmits to a relay in a licensed band, while the relay transmits to a destination in an unlicensed cognitive spectrum band. The licensed and unlicensed bands are characterized by different power, bandwidth and reliability constraints. Taking an information-theoretic perspective, the fundamental properties of the HCGRC are analyzed thoroughly in terms of capacity, spectral efficiency (SE), and energy efficiency (EE). With regard to each metric, we derive the optimal resource allocation strategy and discuss the impacts of CR spectrum reliability and relay location on the metric. We find that in HCGRC, improving the SE and EE are not necessarily conflicting objectives. Instead, both metrics can be optimized simultaneously with proper resource allocation.

Virtual multiple-input multiple-output (V-MIMO) technology promises significant performance enhancements to cellular systems in terms of spectral efficiency (SE) and energy efficiency (EE). How these two conflicting metrics scale up in large cellular V-MIMO networks is unclear. This paper studies the EE-SE trade-off of the uplink of a multi-user cellular V-MIMO system with decode-and-forward type protocols. We first express the trade-off in an implicit function and further derive closed-form formulas of the trade-off in low and high SE regimes. Unlike conventional MIMO systems, the EE-SE trade-off of the V-MIMO system is shown to be susceptible to many factors including protocol design (e.g., resource allocation) and scenario characteristics (e.g., user density). Focusing on the medium and high SE regimes, we propose a heuristic resource allocation algorithm to optimize the EE-SE trade-off. The fundamental performance limits of the optimized V-MIMO system are subsequently investigated and compared with conventional MIMO systems in different scenarios. Numerical results reveal a surprisingly chaotic behavior of V-MIMO systems when the user density scales up. Our analysis indicates that low frequency reuse factor, adaptive resource allocation, and user density control are critical to harness the full benefits of cellular V-MIMO systems.

This paper presents a wireless multiple-input/multiple-output (MIMO) testbed for the UK-China Science Bridges Project: R&D on Beyond Fourth Generation (B4G) Wireless Mobile Communications (UC4G). An introductory review of other approaches is first presented and then subsequently the motivation, architecture and specifications of the UC4G testbed are outlined. The flexibility of operation, versatility of function and modular design of the UC4G testbed are shown to be its key benefits.

Infrastructure-based vehicular networks (consisting of a group of Base Stations (BSs) along the road) will be widely deployed to support Wireless Access in Vehicular Environment (WAVE) and a series of safety and non-safety related applications and services for vehicles on the road. As an important measure of user satisfaction level, uplink connectivity probability is defined as the probability that messages from vehicles can be received by the infrastructure (i.e., BSs) through multi-hop paths. While on the system side, downlink connectivity probability is defined as the probability that messages can be broadcasted from BSs to all vehicles through multi-hop paths, which indicates service coverage performance of a vehicular network. This paper proposes an analytical model to predict both uplink and downlink connectivity probabilities. Our analytical results, validated by simulations and experiments, reveal the trade-off between these two key performance metrics and the important system parameters, such as BS and vehicle densities, radio coverage (or transmission power), and maximum number of hops. This insightful knowledge enables vehicular network engineers and operators to effectively achieve high user satisfaction and good service coverage, with necessary deployment of BSs along the road according to traffic density, user requirements and service types.

In this paper, we present interference models for cognitive radio (CR) networks employing various interference management mechanisms including power control, contention control or hybrid power/contention control schemes. For the first case, a power control scheme is proposed to govern the transmission power of a CR node. For the second one, a contention control scheme at the media access control (MAC) layer, based on carrier sense multiple access with collision avoidance (CSMA/CA), is proposed to coordinate the operation of CR nodes with transmission requests. The probability density functions (PDFs) of the interference received at a primary receiver from a CR network are first derived numerically for these two cases. For the hybrid case, where power and contention controls are jointly adopted by a CR node to govern its transmission, the interference is analyzed and compared with that of the first two schemes by simulations. Then, the interference PDFs under the first two control schemes are fitted by log-normal PDFs to reduce computation complexity. Moreover, the effect of a hidden primary receiver on the interference experienced at the receiver is investigated. It is demonstrated that both power and contention controls are effective approaches to alleviate the interference caused by CR networks. Some in-depth analysis of the impact of key parameters on the interference of CR networks is given as well.

In this paper, we investigate the interference mitigation from a cross-layer perspective for a cognitive radio (CR) multiple-input multiple-output (MIMO) network coexisting with a primary time-division-duplexing (TDD) system. The channel allocation in the media access control (MAC) layer and a subspace-based precoding scheme in the physical layer of the CR network are jointly considered to minimise the interference to the primary user and maximise the CR throughput. Two distributed cross-layer algorithms, namely, joint iterative channel allocation and precoding (JICAP) and non-iterative channel allocation and precoding (NICAP), are proposed for the cases with and without channel information among CR nodes, respectively. Moreover, a channel estimation scheme is also proposed to enable the NICAP. The effectiveness of the proposed algorithms over non-cross-layer counterpart is demonstrated via simulations.

Characterization and modeling of co-channel interference is critical for the design and performance evaluation of realistic multi-cell cellular networks. In this paper, based on alpha stable processes, an analytical co-channel interference model is proposed for multi-cell multiple-input multi-output (MIMO) cellular networks. The impact of different channel parameters on the new interference model is analyzed numerically. Furthermore, the exact normalized downlink average capacity is derived for a multi-cell MIMO cellular network with co-channel interference. Moreover, the closed-form normalized downlink average capacity is derived for cell-edge users in multi-cell multiple-input single-output (MISO) cooperative cellular networks with co-channel interference. From the new co-channel interference model and capacity formulas, the impact of cooperative antennas and base stations on cell-edge user performance in the multi-cell multi-antenna cellular network is investigated by numerical methods. Numerical results show that cooperative transmission can improve the capacity performance of multi-cell multi-antenna cooperative cellular networks, especially in a scenario with a high density of interfering base stations. The capacity performance gain is degraded with the increased number of cooperative antennas or base stations.

In this paper, we present an interference model for cognitive radio (CR) networks employing power control or contention control scheme. The probability density functions (PDFs) of the interference received at a primary receiver from a CR network are derived for two cases. For the first case, a power control scheme is proposed to govern the transmission power of a CR node. For the second one, a cognitive media access control (MAC) employs carrier sense multiple access with collision avoidance (CSMA/CA) based contention control to coordinate the operation of CR nodes with transmission requests. These two control schemes are compared in terms of their resulting interference distributions. It is demonstrated that both power and contention controls are effective approaches to alleviate the interference caused by CR networks. Some in-depth analysis for the impact of key parameters on the interference of CR networks is given via numerical studies as well.

Cognitive radio (CR) has been considered as a promising technology to improve the spectrum utilization. In this paper we analyze the capacity of a CR network with average received interference power constraints. Under the assumptions of uniform node placements and a simple power control scheme, the maximum transmit power of a target CR transmitter is characterized by its cumulative distribution function (CDF). We study two CR scenarios for future applications. The first scenario is called the CR based central access network, which aims at providing broadband access to CR devices. In the second scenario, the so-called CR assisted virtual multiple-input multiple-output (MIMO) network, CR is used to improve the access capability of a cellular system. The uplink ergodic channel capacities of both scenarios are derived and analyzed with an emphasis on understanding the impact of numbers of primary users and CR users on the capacity. Numerical and simulation results suggest that the CR based central access network is more suitable for less-populated rural areas where a relatively low density of primary receivers is expected; while the CR assisted virtual MIMO network performs better in urban environments with a dense population of mobile CR users.

In this paper we study the system level performance of cognitive radio (CR) networks under average received interference power constraints. Under the assumption of uniform node placements and a simple power control scheme, we derive the closed-form expression for the cumulative distribution function (CDF) of the maximum allowable transmit power of the target CR transmitter. We further study two CR network scenarios: a CR based central access network and a CR assisted virtual multiple-input multiple-output (MIMO) network. The average uplink capacities of both networks are derived and analyzed, with an emphasis on understanding the effect of the numbers of primary users and CR users on the capacity. Numerical and simulation results demonstrate that the CR based central access network is more suitable for less-populated rural areas where a lower density of primary receivers is expected, while the CR assisted virtual MIMO network performs better in urban environments with a dense population of mobile CR users.

Cognitive radio (secondary) networks have been proposed as means to improve the spectrum utilization. A secondary network can reuse the spectrum of a primary network under the condition that the primary services are not harmfully interrupted. In this paper, we study the distribution of the interference power at a primary receiver when the interfering secondary terminals are distributed in a Poisson field. We assume that a secondary terminal is able to cease its transmission if it is within a distance of R to the primary receiver. We derive a general formula for the characteristic function of the random interference generated by such a secondary network. With this general formula we investigate the impacts of R, shadowing, and small scale fading on the probability density function (PDF) of the interference power. We find that when there is no interference region (R = 0), the interference PDFs follow heavy-tailed alpha-stable distributions. In case that a proper interference region is defined by a positive value of R, the tails of the interference power PDFs can be significantly shortened. Moreover, the impacts of shadowing and small scale fading on the interference PDFs are studied and the small scale fading is found to be beneficial in terms of reducing the mean value and outage probability of the interference power.