Large Language Models (LLMs) have the potential to greatly enhance the performance of task-oriented semantic communication (TOSC) through their extensive knowledge. However, hallucinations from LLMs may cause semantic noise, thus degrading task performance. To address this issue, we propose a TOSC scheme with an LLM-enabled knowledge base (TOSCLKB). In the considered system, the transmitter transmits the features of the source over a wireless channel to accomplish downstream tasks at the receiver. Meanwhile, the transmitter utilizes the LLM-enabled Knowledge Base (KB) to provide additional data for downstream tasks. To effectively exploit the extensive knowledge of LLMs while mitigate its hallucination, a cross-domain fusion codec framework with a hallucination filtering phase and a cross-domain fusion phase is proposed. In particular, the first phase filters out data irrelevant to the source generated by the LLM-enabled KB based on semantic similarity. Then, a cross-domain fusion phase is proposed which fuses source data with LLM-generated data based on their semantic importance, thereby enhancing task performance. Experiment results on the text-based person retrieval task demonstrate that the proposed TOSC-LKB can achieve up to 26.7 % and 7.1 % performance gains without introducing additional time overhead compared to DeepSC and TOSC-LKB without LLMs.

In amembership inference attack(MIA), an attacker exploits the overconfidence exhibited by typical machine learning models to determine whether a specific data point was used to train a target model. In this paper, we analyze the performance of thelikelihood ratio attack(LiRA) within an information-theoretical framework that allows the investigation of the impact of thealeatoric uncertaintyin the true data generation process, of theepistemic uncertaintycaused by a limited training data set, and of thecalibration levelof the target model. We compare three different settings, in which the attacker receives decreasingly informative feedback from the target model:confidence vector(CV) disclosure, in which the output probability vector is released;true label confidence(TLC) disclosure, in which only the probability assigned to the true label is made available by the model; anddecision set(DS) disclosure, in which an adaptive prediction set is produced as in conformal prediction. We derive bounds on the advantage of an MIA adversary with the aim of offering insights into the impact of uncertainty and calibration on the effectiveness of MIAs. Simulation results demonstrate that the derived analytical bounds predict well the effectiveness of MIAs.

Task-oriented communication exploits the task to improve communication efficiency. Most existing works on task-oriented communication transmit analog signals without quantization, which limits its application in digital communication systems. This paper studies digital task-oriented communication systems with hardware-limited scalar quantization (TOC-SQ) for computation-limited scenarios such as the Internet of Things, where the source data is encoded by a source encoder and then quantized by hardware-limited quantizers for digital transmission. Accordingly, the receiver contains a source decoder to decode the received signal. Our goal is to minimize the mean squared error (MSE) between the transmitted and received task-relevant signals to achieve optimal task performance under a certain bit budget. In particular, we first establish a theoretical analysis framework for TOC-SQ. Then, the closed-form expressions of the source encoder and decoder are derived for linear tasks. Finally, the lower bound of the MSE between the transmitted and received task-relevant information is analyzed to evaluate the task performance. Simulation results verify that the proposed TOC-SQ achieves 6.9 dB MSE gains in frequency-selective channels compared with analog TOC systems.

The goal of Temporal Action Localization (TAL) is to find the categories and temporal boundaries of actions in an untrimmed video. Most TAL methods rely heavily on action recognition models that are sensitive to action labels rather than temporal boundaries. More importantly, few works consider the background frames that are similar to action frames in pixels but dissimilar in semantics, which also leads to inaccurate temporal boundaries. To address the challenge above, we propose a Boundary-Aware Proposal Generation (BAPG) method with contrastive learning. Specifically, we define the above background frames as hard negative samples. Contrastive learning with hard negative mining is introduced to improve the discrimination of BAPG. BAPG is independent of the existing TAL network architecture, so it can be applied plug-and-play to mainstream TAL models without training. Extensive experimental results on THUMOS14 and ActivityNet-1.3 demonstrate that BAPG can significantly improve the performance of TAL.

Type-based multiple access (TBMA) is a semantics-aware multiple access protocol for remote inference. In TBMA, codewords are reused across transmitting sensors, with each codeword being assigned to a different observation value. Existing TBMA protocols are based on fixed shared codebooks and on conventional maximum-likelihood or Bayesian decoders, which require knowledge of the distributions of observations and channels. In this letter, we propose a novel design principle for TBMA based on the information bottleneck (IB). In the proposed IB-TBMA protocol, the shared codebook is jointly optimized with a decoder based on artificial neural networks (ANNs), so as to adapt to source, observations, and channel statistics based on data only. We also introduce the Compressed IB-TBMA (CIB-TBMA) protocol, which improves IB-TBMA by enabling a reduction in the number of codewords via an IB-inspired clustering phase. Numerical results demonstrate the importance of a joint design of codebook and neural decoder, and validate the benefits of codebook compression.

Densely deployed light emitting diodes (LEDs) typically lead to high spatial correlation in multiple-input multiple-output (MIMO) visible light communication (VLC) systems. Although precoding can effectively alleviate the spatial correlation issue, most existing precoding algorithms require a dedicated baseband chain for each LED, leading to high energy consumption and hardware complexity when a large number of LEDs are used. In this paper, a successive interference cancellation (SIC)-based precoding scheme with sub-connected architecture (SIC-SA) is proposed. In the considered model, each baseband chain is connected to an LED sub-array containing multiple LEDs to reduce the complexity. Since, in this case, SIC-based precoding can only determine the signal of each baseband chain for an LED sub-array, while its target is to mitigate the spatial correlation between individual LEDs, the electrical/optical power of each LED must also be jointly optimized to accomplish the target. This joint SIC-based precoding, power allocation, and direct current offset design problem is formulated as an achievable sum rate maximization problem under dimming control and electrical power constraints. A two-step iterative algorithm is proposed to solve this problem. In the first step, the SIC-based precoding is designed to alleviate the multi-user interference. In the second step, the power allocation of LEDs is optimized by matrix decomposition and convex optimization, and a closed-form solution of DC offset is derived. Furthermore, considering the dynamic scenarios, a SIC-based precoding scheme with dynamic sub-connected architecture (SIC-DSA) is proposed, in which a switch network is used to adaptively adjust the LED sub-array structure based on the channel state information. Simulation results show that the proposed SIC-SA and SIC-DSA respectively achieve 0.1340 bps/Hz/W and 0.1305 bps/Hz/W energy efficiency gains over the zero-forcing precoding scheme with SA, when the signal-to-noise ratio is 30 dB.

High spatial correlation is one of the main factors limiting the communication performance of multiple-input multiple-output (MIMO) visible light communication (VLC), which can be alleviate by precoding. However, most existing precoding algorithms require a dedicated baseband chain for each light emitting diode (LED), which may result in high energy consumption and hardware complexity, especially when LEDs are densely deployed. To solve this issue, a successive interference cancellation-based precoding scheme with sub-connected architecture (SIC-SA) is proposed, where each baseband chain is connected to an LED sub-array. In this case, since the SIC-based precoding can only determine the signal of each baseband chain for an LED sub-array, the electrical/optical power of each LED must be jointly optimized to alleviate the spatial correlation among individual LEDs. This joint SIC-based precoding, power allocation and direct current offset design problem is formulated as an achievable sum rate maximization problem under dimming control and electrical power constraints. To solve this problem, the original problem is separated into two subproblems. In the first subproblem, the SIC-based precoding is calculated by successive null space computation. With the obtained SIC-based precoding, the second subproblem optimizes the power allocation and direct-current offset. Finally, these two subproblems are iteratively solved to obtain a convergent solution. Simulation results demonstrate that the proposed SIC-SA achieves 0.1306 bps/Hz/W and 0.1340 bps/Hz/W improvements in terms of energy efficiency compared to minimum mean square error precoding with fully-connected architecture (FA) and zero-forcing precoding with FA, respectively, when the signal-to-noise ratio is 30 dB.

This paper proposes a visible light communication (VLC) assisted perspective circle and arc algorithm (V-PCA) for indoor positioning. In the considered model, the coordinate information obtained via VLC and the geometric information of circle feature are jointly used for indoor positioning. In particular, V-PCA first designs a space-time coding model based on VLC, which can help determine the center and a mark point of the circular luminaire on the image plane. It also transmits the world coordinate information of the luminaire to the camera through the activated LEDs. Then, based on the plane and solid geometry, the geometric information of circular feature is exploited to estimate the orientation and the coordinate of the luminaire in the camera coordinate system. Finally, the pose and the location of the camera in the world coordinate system are obtained by the single-view geometry. V-PCA can achieve accurate positioning without tilting angle limitation and inertial measurement unit for the receiver. Simulation results show that the proposed indoor positioning algorithm can achieve a 95th percentile positioning accuracy of around 10 cm.

Graph convolutional networks (GCNs) have proven their effectiveness and success in skeleton-based action recognition. Regular GCNs separate spatial and temporal operations. And for spatial graph convolution, the self-attention mechanism is commonly used to enable the generation of adaptive data-driven graphs, which limits the receptive field of each vertex. In order to address such a concern, we propose the graph involution network (GIN). A novel operation called graph involution is applied to offer each joint a global reception field, while also largely improving temporal-wise and channel-wise flexibility during the process of feature aggregation. We also find that predefined graphs that came from human natural skeleton structures restrict the remote information exchanges. Therefore we also propose a novel graph topology called spiral graph topology, connecting body joints spirally, which brings valuable additional information hints and improves feature aggregation efficiency. Experiments conducted on different benchmarks on two large-scale datasets show the effectiveness of our proposed network.

This article proposes a hybrid dimming (HD) scheme based on joint light-emitting diode (LED) selection and precoding design (TASP-HD) for multiple-user (MU) multiple-cell (MC) visible light communications (VLCs) systems. In TASP-HD, both the LED selection and the precoding of each cell can be dynamically adjusted to reduce the intra- and inter-cell interferences while satisfying illumination constraints. First, an MU-MC-VLC system model is established, and then a sum-rate maximization problem under the dimming level and illumination uniformity constraints is formulated. In this studied problem, the indices of activated LEDs and precoding matrices are optimized, which result in a complex nonconvex mixed-integer problem. To solve this problem, the original problem is separated into two subproblems. The first subproblem, which maximizes the sum rate of users via optimizing the LED selection with a given precoding matrix, is a mixed-integer problem solved by the penalty method. With the optimized LED selection matrix, the second subproblem which focuses on the maximization of the sum-rate via optimizing the precoding matrix is solved by the Lagrangian dual method. Finally, these two subproblems are iteratively solved to obtain a convergent solution. Simulation results verify that in a typical indoor scenario under a dimming level of 70%, the mean bandwidth efficiency (MBE) of TASP-HD is 4.8 bit/s/Hz and 7.13 bit/s/Hz greater than analog dimming (AD) and digital dimming (DD), respectively.

With the rapid growth of data traffic in cellular networks, edge caching has become an emerging technology for traffic offloading. We investigate the caching placement and content delivery in cache-enabling cellular networks. To cope with the time-varying content popularity and user location in practical scenarios, we formulate a long-term joint dynamic optimization problem of caching placement and user association for minimizing the content delivery delay which considers both content transmission delay and content update delay. To solve this challenging problem, we decompose the optimization problem into two sub-problems, the user association sub-problem in a short time scale and the caching placement in a long time scale. Specifically, we propose a low complexity user association algorithm for a given caching placement in the short time scale. Then we develop a deep deterministic policy gradient based caching placement algorithm which involves the short time-scale user association decisions in the long time scale. Finally, we propose a joint user association and caching placement algorithm to obtain a sub-optimal solution for the proposed problem. We illustrate the convergence and performance of the proposed algorithm by simulation results. Simulation results show that compared with the benchmark algorithms, the proposed algorithm reduces the long-term content delivery delay in dynamic networks effectively.

Visible light positioning and computer vision-based localization have the potential to be cost-effective technologies for accurate indoor localization. However, the feasibility of existing methods in this domain is limited. In this paper, a novel visible light communication (VLC)-assisted perspective-four-line algorithm (V-P4L) is proposed for practical indoor localization. The basic idea of V-P4L is to jointly use VLC and computer vision techniques to achieve high localization accuracy regardless of LED height differences. In particular, the space-domain information is first exploited to estimate the orientation and coordinate information of a single rectangular LED luminaire in the camera coordinate system based on plane geometry theory and solid geometry theory. Then, by using time-domain information transmitted by VLC and the estimated luminaire information, the proposed V-P4L can estimate the position and pose of the camera using single-view geometry theory and the linear least square (LLS) method. To further mitigate the effect of height differences among LEDs on localization accuracy, a correction algorithm based on the LLS method and a simple optimization method is proposed. Due to the combination of time- and space-domain information, V-P4L can achieve accurate localization using a single luminaire without limitation on the correspondences between the features and their projections in conventional perspective-n-line (PnL) algorithms. Simulation results show that the position error caused by the proposed V-P4L algorithm is always less than 15 cm and the orientation error is always less than 4° using popular indoor luminaires. Experimental results with real hardware show that the average position error is less than 3 cm under both similar and different heights for the LEDs.

Lots of videos are transmitted for content understanding tasks in vehicular networks, which puts pressure on limited communication resources. Traditional resource allocation schemes do not consider video content, so the performance of content understanding tasks performed on the transmitted videos is not optimal. To solve this problem, in this paper, we proposed a video frames priority driven and reinforcement Q-learning based resource allocation scheme. First, we propose a video frames content priority evaluation method from the perspective of video content, and the evaluation results are the basis of resource allocation. Then, we propose a Q-learning based resource allocation scheme in vehicular network scenario, which provides more reliable resources for video frames with higher priority. Finally, experiments on real datasets validate that the proposed scheme can help to improve the performance of video content understanding tasks, such as traffic accident detection tasks.

In cache-enabling cellular networks, we investigate the caching placement and content delivery. To cope with the time-varying content popularity and user location in practical scenarios, we formulate a long-term joint dynamic optimization problem of caching placement and user association for minimizing the content delivery delay. We decompose the optimization problem into two sub-problems, the user association sub-problem in short time-scale and the caching placement in long timescale. Specifically, we propose a low complexity belief propagation based user association algorithm in the short time-scale. Then we develop a deep deterministic policy gradient based caching placement algorithm in the long time-scale. Finally, we propose a joint user association and caching placement algorithm to obtain a sub-optimal solution for the proposed problem. We demonstrate the convergence and performance of the proposed algorithm by simulation results. Simulation results show that compared with the benchmark algorithms, the proposed algorithm reduces the long-term content delivery delay in dynamic networks effectively.

A novel dimming control scheme, termed as generalized dimming control (GDC), is proposed for visible light communication (VLC) systems. The basic idea of GDC is to exploit adaptively time, spatial and signal domain resources for enhancing communication performance under practical illumination constraints. In particular, to satisfy the illumination constraints, an incremental algorithm for index mapping is first proposed to achieve target optical power and uniform illumination. Next, GDC having the optimal activation pattern is investigated to improve the bit-error rate (BER) performance of VLC. In particular, the BER performance of GDC is analyzed using the union bound technique. Based on the analytical BER bound, the optimal activation pattern of GDC scheme having the minimum BER criterion (GDC-MBER) is obtained by exhaustively searching all conditional pairwise error probabilities. However, since GDC-MBER requires high search complexity, two low-complexity GDC schemes having the maximum free distance criterion (GDC-MFD) are proposed. The first low-complexity GDC-MFD scheme, termed as GDC-MFD1, is derived by a lower bound of the free distance using the Rayleigh-Ritz theorem. The second low-complexity GDC-MFD scheme, termed as GDC-MFD2, is proposed to reduce further the computation by exploiting the time-invariance characteristics of the VLC channel. Simulation and numerical results show that GDC-MBER, GDC-MFD1 and GDC-MFD2 have similar BER performance, and they can achieve more than 3 dB SNR gains compared with conventional dimming control schemes for a BER of 10-4and a dimming level of 50%.

A high-coverage algorithm termed enhanced camera assisted received signal strength ratio (eCA-RSSR) positioning algorithm is proposed for visible light positioning (VLP) systems. The basic idea of eCA-RSSR is to utilize visual information captured by the camera to estimate first the incidence angles of visible lights. Based on the incidence angles, eCA-RSSR utilizes the received signal strength ratio (RSSR) calculated by the photodiode (PD) to estimate the ratios of the distances between the LEDs and the receiver. Based on an Euclidean plane geometry theorem, eCA-RSSR transforms the ratios of the distances into the absolute values. In this way, eCA-RSSR only requires three LEDs for both orientation-free 2D and 3D positioning, implying that eCA-RSSR can achieve high coverage. Based on the absolute values of the distances, the linear least square method is employed to estimate the position of the receiver. Therefore, for the receiver having a small distance between the PD and the camera, the accuracy of eCA-RSSR does not depend on the starting values of the non-linear least square method and the complexity of eCA-RSSR is low. Furthermore, since the distance between the PD and camera can significantly affect the performance of eCA-RSSR, we further propose a compensation algorithm for eCA-RSSR based on the single-view geometry. Experiment results show that positioning errors of less than five centimeters is achievable for eCA-RSSR. Simulation results show that eCA-RSSR can achieve 80th percentile accuracy of about four centimeters and can improve the coverage ratio at low cost.

In this paper, a novel visible light communication (VLC) assisted Perspective-four-Line algorithm (V-P4L) is proposed for indoor localization. The basic idea of V-P4L is to joint the coordinate information obtained by VLC and the geometric information in computer vision for practical indoor localization. In particular, V-P4L first exploits the geometric information to estimate the orientation and coordinate of a single rectangular LED luminaire in the camera coordinate system based on the plane and solid geometry. Then, VLC is used to transmit the world coordinate information of the luminaire to the camera. Next, the position and pose of the camera in the world coordinate system are obtained by the single-view geometry and the linear least square method. Due to the combination of VLC and computer vision, V-P4L only requires a single luminaire for localization and does not rely on the channel model. Therefore, V-P4L is more robust to dust and other factors that interfere visible light signals. Besides, unlike conventional Perspective-n-Line (PnL) algorithms, V-P4L does not require the 3 dimensional (3D)-2 dimensional (2D) correspondences. Simulation results show that for V-P4L the location error is always less than 18 cm and the orientation error is always less than 4.5°.

In this article, we investigate the content distribution in the hotspots area, whose traffic is offloaded by the combination of the unmanned aerial vehicle (UAV) communication and edge caching. In cache-enabling UAV-assisted cellular networks, the network deployment and caching placement are vital for quality of experience (QoE) of users with content distribution applications. We formulate a joint optimization problem of UAV deployment and caching placement for maximizing QoE of users, which is evaluated by mean opinion score (MOS). To solve this challenging problem, we decompose the optimization problem into two sub-problems. Specifically, we propose a swap matching based UAV deployment algorithm, then obtain the near-optimal caching placement by greedy algorithm. Finally, we propose a low complexity iteration algorithm for the joint UAV deployment and caching placement optimization, which achieves good computational complexity-optimality tradeoff. Simulation results reveal that the proposed algorithm obtains the near-optimal solution of exhaustive search, converges within several iterations and achieves better performance compared with the benchmark algorithms.

This paper proposes a simple dimming control scheme for multi-LED visible light communications (VLC), termed as generalized dimming control (GDC) scheme. The GDC performs dimming control by simultaneously adjusting the intensity of transmitted symbols and the number of active elements in a space-time matrix. The indices of active elements in each space-time matrix, as well as the modulated constellation symbols, are used to transmit information base on the space-time index modulation. Furthermore, the tradeoff between the intensity of transmitted symbols and the number of active elements in the space-time matrix is analyzed for enhancing the reliability of communication. To improve the communication reliability, a GDC with the minimum bit error rate (BER) criterion is first proposed to select the optimal dimming control pattern based on exhaustive search. Furthermore, to reduce the computational complexity, a low complexity GDC with the maximum free distance criterion is proposed. Simulation and numerical results show that the proposed scheme can outperform conventional dimming control schemes at both low and high dimming levels in terms of the BER performance.

In the paper, we study the problem of inductive embedding learning on attributed heterogeneous networks, and propose a Multi-task sequence-to-sequence learning based Inductive Network Embedding framework (MINE) capturing the attribute similarity, network proximity, and partial label information simultaneously. In particular, MINE trains an encoder function that aggregates information from a node's long-range scope of contexts, with the node attribute sequences generated by the proposed type-guided heterogeneous random walk as inputs. We present an one-to-many multi-task sequence-to-sequence model where the encoder is shared between two related tasks: an unsupervised node identity sequence generation task to learn context-aware embeddings, and a semi-supervised label prediction task to learn semantics-rich embeddings. Extensive experiments on real-world datasets demonstrate that the proposed method significantly outperforms several state-of-the-art methods.

Session-based recommendations recently receive much attentions due to no available user data in many cases, e.g., users are not logged-in/tracked. Most session-based methods focus on exploring abundant historical records of anonymous users but ignoring the sparsity problem, where historical data are lacking or are insufficient for items in sessions. In fact, as users' behavior is relevant across domains, information from different domains is correlative, e.g., a user tends to watch related movies in a movie domain, after listening to some movie-themed songs in a music domain (i.e., cross-domain sessions). Therefore, we can learn a complete item description to solve the sparsity problem using complementary information from related domains. In this paper, we propose an innovative method, called Cross-Domain Item Embedding method based on Co-clustering (CDIE-C), to learn cross-domain comprehensive representations of items by collectively leveraging single-domain and cross-domain sessions within a unified framework. We first extract cluster-level correlations across domains using co-clustering and filter out noise. Then, cross-domain items and clusters are embedded into a unified space by jointly capturing item-level sequence information and cluster-level correlative information. Besides, CDIE-C enhances information exchange across domains utilizing three types of relations (i.e., item-to-context-item, item-to-context-co-cluster and co-cluster-to-context-item relations). Finally, we train CDIE-C with two efficient training strategies, i.e., joint training and two-stage training. Empirical results show CDIE-C outperforms the state-of-the-art recommendation methods on three cross-domain datasets and can effectively alleviate the sparsity problem.

Uplink transmission is an issue for visible light communications due to the unpleasant irradiance from the source light when placed close to the users. To overcome this problem, this paper proposes the use of relays to lower the required source optical power. A popular multi-carrier modulation scheme, termed direct-current biased optical orthogonal frequency division multiplexing, is employed to achieve high spectral efficiency. In addition, both amplitude-and-forward (AF) and decode-and-forward (DF) protocols are used. The theoretical models of AF and DF protocols are also obtained and verified by simulations. To minimize the source optical power while satisfying reliable communications, this paper formulates the associated optimization problems for AF and DF protocols. Exhaustive search is first used to obtain the optimal configuration for the system. As exhaustive search requires high computation efforts and can be time-consuming, two low-complexity suboptimal designs for AF and DF protocols are then proposed, and the proposed suboptimal designs can approximate the performance of exhaustive search in high signal-to-noise ratio regions. Numerical and experimental results indicate that when compared with the counterpart without a relay, the proposed relay-assisted system requires much lower source optical power under the constraints of reliable transmissions.

Representation learning for images is widely recognized as critical to the performance of end tasks such as image classification and cross-modal retrieval. However, most existing methods extract features only from visual content, far from adequate in interpreting semantics latent in images. For social images, there also exists rich social context information, e.g. owners, tags and groups, which provides cues for interpreting semantics. In this paper, we propose a representation learning framework via deep heterogeneous hypergraph embedding (DHHE), considering both visual content and social contexts. In particular, images and their contexts are first represented as a heterogeneous hypergraph, which is then embedded into a low-dimensional space. To incorporate visual information and generalize for unseen images, we learn the mapping from visual content to the semantic space. We conduct experiments with the tasks of classification, cross-modal retrieval and recommendation, which demonstrates the effectiveness of our approach and the merits of social guidance.

In massive multiple-input multiple-output (MIMO) uplink systems, phase noise introduced by oscillators can cause severe performance loss. It leads to common phase error and inter-carrier interference in massive MIMO-OFDM uplink. To solve the issue, a novel phase noise cancellation scheme based on polarization modulation is proposed. We first introduce the polarization modulation (PM) exploited in massive MIMO-OFDM uplink. Then, by exploiting zero-forcing detection, we analyze ICI and the distribution of the transformed noise. Furthermore, we demonstrate that phase noise can be asymptotically cancelled and only the transformed additive white Gaussian noise exists as the number of antennas at the base station is very lager. Moreover, we derive the instantaneous signal-to-noise ratio (SNR) on each subcarrier and analyze the ergodic capacity. The simulation results show that the proposed scheme can effectively mitigate phase noise and achieve a higher ergodic capacity.

This paper focuses the problems of self-interference cancellation (SIC) in massive MIMO full-duplex system. And a coupling channel estimation based SIC (BF-SIC) scheme is proposed to eliminate SI effectively without the instantaneous information of self-interference (SI) channel, which is very difficult to obtain in massive MIMO full-duplex system. In addition, in order to evaluate the BF-SIC scheme, the SI cancellation capability and the uplink achievable rate are derived and analyzed when the channel estimation error and channel training overhead are considered. Furthermore, the simulation result shows that when the number of antennas and signal-to-noise (SNR) is very large, SI can be eliminated almost completely with BF-SIC scheme, and the uplink rate would increase significantly.

In time-varying multipath channels, the performance of polarization technologies in wireless networks such as polarization diversity, polarization multiplexing and polarization modulation is susceptible to polarization mode dispersion (PMD). PMD is an important element to describe the depolarization effects of wireless channels and is useful in polarized signal processing. We can use PMD information to choose modulation modes which can improve energy efficiency, or to choose power allocation model which can improve data transmission rate. Therefore, this paper presents a PMD estimation algorithm based on selection combining to obtain PMD information of time-varying multipath channels. The proposed algorithm can be divided into two steps. Firstly, to improve the accuracy of estimation, we use selection combining at the receiver, which choose signals having higher SNR from horizontal and vertical component. Then, We use selected signals to estimate multipath delay. Secondly, we compute PMD directly by using delay spread and max delay which can be calculated from multi-path delay. The performance of the proposed PMD estimation algorithm is evaluated by the mean square error (MSE). Theoretical analysis and simulation show that the proposed algorithm have better estimation performance compared with PMD estimation algorithm using single component.

In full duplex (FD) multiuser MIMO system, multiple mobile stations (MSs) with FD transmission simultaneously access a given base station (BS), significant interuser interferences exist, limiting FD system performance. Existing methods addressing in the inter-user interference cancellation mostly need to know the interference channel state information and to decode and subtract the interference from the receiver. In this paper, we propose a blind polarization oblique projection based inter-user interference cancellation method without any prior information of the interference channel. This method merely utilizes the polarization state (PS) information and the autocorrelation matrix information of received signals at the downlink MS receivers to construct an oblique projection operator, which can be used to project the inter-user interference to the null space of the operator. Numerical results demonstrate that the proposed method can cancel the inter-user interference effectively when compared to the system with inter-user interference existing, and it performs more robust when the signal to noise ratio (SNR) and the receive antennas number is small.

To cancel phase noise which causes the bit error rate of OFDM systems degradation, a novel orthogonal-polarization-based phase noise self- cancellation (OP-PNSC) scheme in the polarization dependent loss (PDL) channel is proposed. In the proposed scheme, the orthogonal polarizations are utilized to transmitted orthogonally polarized signals which are added together at the receiver to cancel phase noise. These orthogonally polarized signals transmitted in the same-frequency channel enable that the proposed scheme has the advantages of more efficient in cancelling phase noise and the spectral efficiency (SE) improvement. Considering the realistic wireless channel, the distortion of PDL is investigated and a PDL pre-compensated OP- PNSC (PPC-OP-PNSC) scheme is proposed to mitigate the power imbalance caused by PDL. Then, the signal- to-interference-plus-noise ratio (SINR) and SE performances are analyzed to evaluate the performance of the OP-PNSC scheme. Finally, the numerical results show that the OP-PNSC scheme achieves performance close to that of OFDM system without phase noise in the PDL channel.

Benefit from the caching hits on the routers along the request routing path, in-network caching plays an important role in diminishing the distance between the consumers and their desired contents. A content caching model is proposed to intuitively analyze the small world characteristics of in-network caching in information centric networks. A directed long edge from a caching node to the provider of its stored content is created with the probability of the content replica's cache hit ratio. Simulation results validate that the information-centric networks demonstrate the small world characteristics due to the adoption of in-network caching.

Phase noise caused by the unideal local oscillators limits the self-interference cancellation performance severely in full-duplex systems. In this paper, a novel digital polarization self-interference cancellation method is proposed to break the bottleneck of phase noise. The basic idea here is that the polarization is insensitive to the absolute phase and will not be affected by the random phase noise. Without any prior knowledge of the phase noise, the proposed method exploits the polarization signal processing to transform the multiplicative phase noise to a new additive white Gaussian noise, which benefits from the vectorial property of polarization. Based on the polarization system and signal models with the phase noises both in the upconversion and the downconversion, we demonstrate that the self-interference can be cancelled by regeneration where only a transformed additive white Gaussian noise exists. Moreover, the proposed method obtains an upper bound for digital self-interference cancellation when the phase noise exists if the dual-polarized channel estimation is perfect. The simulation is conducted in advanced design system, and results show that the proposed method cancels the self-interference to noise floor.

In dual-polarized channel, the BER performance of polarization modulation (PM) can be decreased by cross-polarization discrimination (XPD) effect which can introduce cross power leakage. Therefore, a XPD compensation algorithm for PM is proposed. Based on the analyses about the effect of XPD on PM, the compensation factor is obtained by the channel state information. Then by compensating the state of polarization of the received signals, the cross power leakage between two orthogonal components of the states of polarization is mitigated, and the constellation distortion of PM is reduced. The analyses reveal that the compensation algorithm can effectively improve the BER performance of PM affected by XPD. Finally, simulation results show that when the XPD is a definite value, to achieve equivalent BER performance of 2PM, the SNR needed is 2.5dB reduction after compensation.

To improve the energy efficiency of power amplifier in OFDM systems, an adaptive polarization-QAM modulation scheme is proposed considering the data rate requirement variation at the transmitter. Since QAM and polarization modulation have different energy efficiency performance in the linear and nonlinear region of power amplifier respectively, thus the optimal energy efficiency can be obtained by combining the two modulation schemes together and adjusting their modulation order adaptively to fulfill the varying data rate requirement. The power series model of power amplifier is used to analyze the effects of both the in-band distortions and the out-of-band emissions on OFDM signals. Based on above, the proposed scheme can be formulated as an optimization problem regarding the output back off value as well as the modulation order of polarization modulation and QAM. Simulations under the same symbol error rate threshold and data rate requirement show that, the energy efficiency of the proposed scheme is nearly twice as that of the traditional QAM scheme in OFDM systems.

Cognitive Radio (CR) is a promising technology to solve the spectrum scarcity. Spectrum sensing becomes more challenging in Cognitive Vehicular Networks (CVNs) due to Secondary Users (SUs) mobility. Current studies on cooperative spectrum sensing usually assume that sensors are static and independent, which is unreasonable in vehicular networks with dense traffic. In this paper, we investigate the cooperative spectrum sensing performance in dense traffic with the presence of SUs mobility and correlation. First of all, we establish a mobility model in dense traffic by analyzing the trajectory data provided by Next Generation SIMulation (NGSIM) program. Secondly, detection probability and false alarm probability are investigated with mobile SUs and spatial-temporal spectrum opportunities. Next, mobility-driven sensing capacity is proposed to evaluate the sensing capacity available for mobile SUs. Note that SU mobility increases the sensing performance by providing spatial diversity and also enables SUs to achieve higher sensing capacity because of the presence of spatial spectrum opportunities. We also indicate that in dense networks, correlation is a crucial factor that may affect the network performance. In addition, the effects of protection range and primary user activity on spectrum sensing are studied. The theoretical analysis is further validated through simulations.

This paper studies the nonlinear transceiver design in downlink coordinated multi-cell system where base stations (BSs) are powered by hybrid sources, including conventional grid and renewable energy. Renewable energy is collected by energy harvesting equipment deployed at BS. However, the harvested energy from different BSs varies in space and time, and the system gain will be limited by the least energy of the coordinated BSs. One of the methods to settle this drawback is energy cooperation among BSs. The nonlinear joint transceiver is formulated as an optimization problem to maximize the minimum signal-to-interference noise ratio of streams under the transmit power constraint. The closed-form solutions are derived by the proposed two-step algorithm. The proposed algorithm guarantees the performance balancing among all users and all streams of each user. Simulation results illustrate the performance improvement of the proposed algorithm.

Along with the exponential growth of mobile data traffic over the past few years, dense small cell networks are considered an attractive solution to cope with the mobile traffic demand. However, users of small cells in dense environment are exposed to serious co-tier interference problem and users at the edge of small cells have worse performance especially. In order to improve the performance of redefined edge users and maintain the throughput of the network, we propose a clustering-based co-tier interference coordination (CCIC) scheme on the downlink (DL) side to mitigate co-tier interference to edge users. Firstly, small cells are clustered based on the graph coloring algorithm (GCA). Secondly, we schedule frequency resources into two bands as well as group users into two categories. Different kinds of bands are assigned to corresponding users. Accordingly we reduce power on a band defined as center band to mitigate interference to edge users. Then a utility function is proposed to find the optimal transmission power of each small cell base station (SBS) on center band. Finally, differential evolution (DE) algorithm is applied to obtain the optimal transmission power. System level simulation results show that our proposed scheme can greatly improve the edge user throughput with slightly sacrificing the overall throughput. Meanwhile, it can improve the network energy efficiency (EE) dramatically.

In overlay cognitive radio network, the available spectrum for secondary users are dynamically grabbed from a wideband of hundreds megahertz by spectrum sensing, which leads to remarkably differential path loss among different frequencies according to propagation theory. Adjusting the global path loss of cognitive relay system through spectrum allocation can serve nontrivial increment to the performance of multi-relay selection. In this paper, we propose a novel scheme combining multi-relay selection with spectrum allocation in the cognitive radio relay system to obtain maximum signal to noise ratio (SNR) at the receiver, called SAMS (spectrum allocation and multi-relay selection). Given the available frequencies, we first search all the possible resolutions for spectrum allocation, then make multi-relay selection under each possible spectrum allocation resolution to optimize the SNR value at the receiver. Finally, we choose the largest SNR, achieve the corresponding spectrum allocation resolution and also figure out the selected relays. Compared with conventional multi-relay selection scheme (CMS), simulation results demonstrate that our proposed scheme has a 4dB-increment of SNR value at the receiver.

Full duplex systems are more spectrally efficient than conventional half duplex systems if the self-interference (SI) can be significantly mitigated. Digital cancellation is one of the lowest complexity SI cancellation techniques in full duplex systems. However, its mitigation capability is mainly limited by the power amplifier (PA) nonlinear distortion. In this paper, to cancel the SI induced by the PA nonlinear distortion, a SI cancellation scheme based on polarization mismatch (PMC) is proposed. The proposed scheme takes advantage of the characteristic that the polarization state of the SI is immune to the PA nonlinearity. And it cancels the SI in the digital baseband by a polarization mismatch matrix which has an orthogonal polarization state to that of the SI. Analysis and numerical results demonstrate that the proposed scheme can cancel the nonlinear SI induced by the PA efficiently. Besides, the desired signal to interfere and noise (SINR) gain will not deteriorate even if the power of the SI is high compared with the existing cancellation scheme. Further, the achievable rate of it can reach more than two times compared with the half duplex when the similarity coefficient of the polarization states is greater than 0.5.

Nowadays the rapid growth of user data rate requirement prompts dense deployment of small cell. However, the dense deployment strategy will bring in new interference problem due to the short distance between cells. Coordinated Multiple Point (CoMP) transmission is considered to be an effective way to mitigate Inter-Cell Interference (ICI). In this paper, we propose a novel CoMP transmission algorithm for dense deployment of small cell, which takes advantage of both Joint Transmission (JT) and Dynamic Point Blanking (DPB) CoMP technologies. Considering the capacity of backhaul link and the characteristics of these two categories of cooperation, JT scheme is used between macro base stations (which are relatively fewer than small cells) and small cells, and DPB technology is used among small cells. Simulation results shows our algorithm achieves better Energy Efficiency (EE) and edge user Spectrum Efficiency (SE) than JT transmission at the cost of little decrease in average SE per sector if proper parameter is set. The gain ratio obtained in dense small cell deployment is more obvious than in sparse deployment.

Compressed spectrum sensing (CSS) is proposed to detect spectrum opportunities efficiently over a wideband. However, most of existing CSS approaches will cause high computation costs for signal recovery when spectrum bandwidth goes large. As a result, it prolongs time for spectrum detection, which however runs counter to the original purpose of finding out spectrum opportunities over a wideband as rapidly as possible. To reduce the time consumed in signal reconstruction and realize real- time detection, we propose a novel decomposition compressed spectrum sensing (D-CSS) scheme. In D- CSS, a sparse sampling matrix is constructed first, and then it equivalently means a decomposition of the reconstructing process into two recovery subtasks. In doing so, we can scale down the overall problem and reduce the entire time for wideband spectrum detection compared with current CSS methods for a given desired sensing accuracy. Furthermore, the sparse character of our designed sampling matrix not only facilitates the operations of signal sampling and signal recovery, but also relieves the burden on random seeds generator and memory storage, which alleviates the overall implementation cost in CR practice.

In the research of green networks, considering the base station (BS) density from the perspective of energy efficiency is very meaningful for both network deployment and BS sleeping based power saving. In this paper, we optimize the BS density for energy efficiency in cellular networks by the stochastic geometry theory. First, we model the distribution of base stations and user equipment (UE) as spatial Poisson point process (PPP). Based on such model, we derive the closed-form expressions of the average achievable data rate, the network energy consumption and the network energy efficiency with respect to the network load. Then, we optimize the BS density for network energy efficiency maximization by adopting the Newton iteration method. Our study reveals that we can improve the network energy efficiency by deploying the suitable amount of BSs or switching on/off proportion of the BSs according to the network load. The simulation results validate the theoretical analysis, and show that when the right amount of BSs is deployed according to the network load, the network energy efficiency can be maximized and the maximum energy efficiency is a fixed value once the network parameters are given.

In this paper, we consider the problem of spectrum sensing in cognitive radios by exploiting Stokes subvector, which can completely describe energy and polarization information of the received vector signal captured by dual-polarized antennas. We first find that both component correlation between Stokes variables (i.e., the elements of Stokes subvector) and vector correlation between Stokes subvectors containing signal and noise are different from that of noise only with high probability. Therefore, two new blind detectors, namely, component-correlation-based energy-polarization detection (CCB-EPD) and vector-correlation-based energy-polarization detection (VCB-EPD), are proposed, respectively. The analysis results reveal that CCB-EPD and VCB-EPD are all constant false alarm rate detectors, and the VCB-EPD method achieves better performance than CCB-EPD when channel is low depolarized and vice versa. Simulations show that the proposed two methods exhibit better performances than other multiantenna-based detectors whether priori polarization information of primary user is known or not. We also show that the two proposed methods have performance improvement with respect to existing polarization-based detectors due to the exploitation of both energy and polarization information and the unaffectedness by noise uncertainty. The experimental results verify that the proposed two methods can satisfy the performance requirement specified by the IEEE 802.22 standard.

Multi-cell coordinated system is an attractive way of improving data rate, by serving one user through several base stations (BSs). BSs might share both data and their channel state information (CSI), but the demand on backhaul capacity makes it inflexible. In this paper, we consider the case with BSs sharing data dedicated to users but having only local CSI, and propose a decentralized nonlinear precoding algorithm for multi-cell coordinated system with multi-stream multi-antenna users. The proposed algorithm is designed to eliminate the inter-user interference and the inter-stream interference, and meanwhile to achieve equal performance for streams of each user. In the proposed algorithm, Tomlinson-Harashima precoding is applied to eliminate partial interference, and transmit space matrix is designed to eliminate the other interference. After the interference elimination, closed-form expression of the transmit and receive processing matrix are derived to maximize the minimum signal to interference plus noise ratio for streams of each user. Simulation results show that, compared with the decentralized linear precoding, the proposed algorithm achieves better performance.

As traditional wireless cellular networks are facing a rapid growth of traffic demand, future networks may consist of a large number of small cells which are densely deployed. However, networks in dense environment is exposed to strong inter-cell interference problem which is critical to networks capacity. To solve the problem, we use the Graph Coloring Algorithm (GCA) to divide the small cells into different clusters to mitigate the serious inter-cell interference between the dense small cells, and accordingly reducing the transmission power of small cell BSs in some subframes based on the proposed utility function. The existence of the optimal solutions to the problem is discussed, then the Differential Evolution (DE) is applied to search the optimal transmission power which maximizes the proposed utility function. Intensive simulations show that the proposed scheme can significantly mitigate the co-tier interference and maximize networks capacity.

Dense deployment of small cell in heterogeneous networks (HetNet) is considered as a promising way to cope with the exponential growth in mobile traffic demand. In such scenario, coverage optimization is a challenge due to the unplanned deployment and plug-and-play feature of small cell. In this paper, the coverage performance of the network is analyzed under different deployment density of small cells. Then the coverage problem is modeled as a cost function which includes the inappropriate coverage ratio of the network. After that, the ant colony algorithm (ACA) is applied to find the optimal pilot Tx power of each small cell through the minimization of the cost function. Finally, compared to the fixed coverage scheme, the proposed algorithm can not only reduce the ratio of coverage hole but also the proportion of coverage overlap as well.

Improving network Energy Efficiency (EE) in wireless communications has attracted increasing attention recently. The purpose of our research is to optimize the network power consumption and preserve the throughput of macrocells User Equipments (UEs) at the same time. We solve the problem by controlling the transmission power of macro base stations (BSs) in some subframes based on the proposed utility function, and accordingly scheduling picocells Cell Range Expansion (CRE) UEs in these subframes. The existence of the optimal solutions to the problem is discussed, then the Differential Evolution (DE) is applied to search the optimal transmission power which maximizes the proposed utility function. System level simulation results show that the proposed algorithm can not only optimize the network power consumption but also improve the throughput of the network, which will lead to the network EE improvement.

To adapt to variation of polarization dependent loss (PDL) in depolarization channel, an adaptive polarization modulation scheme which can improve the spectral efficiency of polarization modulation system is proposed. In the proposed scheme, the transmitter adapts the optimal constellation size to the variation of the channel due to PDL while fulfilling a fixed target BER constraint, which is based on the received feedback information of channel state information estimated at the receiver. Our simulation results and analysis show that, adaptive polarization modulation (APM) can achieve higher spectral efficiency (SE) than nonadaptive polarization modulation, subject to the same fixed target BER constraint.

A Joint Polarization-Amplitude-Phase Modulation (JPAPM) scheme in wireless communication is proposed to improve the Power Amplifier (PA) energy efficiency. The proposed scheme introduces the signal's Polarization State (PS), amplitude and phase as the information-bearing parameters. Thus, the data rate can be further enhanced on the basis of the traditional amplitude-phase modulation. Also, since the transmitted signal's PS completely manipulated by orthogonally dual-polarized antennas is unaffected by the PA, JPAPM can let PA work in its nonlinear region to acquire high PA conversion efficiency. Furthermore, to mitigate the polarization-based impairment to JPAPM caused by the wireless channel's polarization dependent loss effect, the optimal pre-compensation algorithm is also presented. Simulation under the same symbol error rate and channel state shows the JPAPM can improve the PA energy efficiency significantly compared with the traditional quadrature amplitude modulation.

In this paper, we investigate how picocells overlaid in macrocells expand their coverage areas under different Cell Range Expansion (CRE) strategies in a macro-pico heterogeneous network. Through system level simulations, the relationships among the presented CRE strategies are specified from both qualitative and quantitative perspective, in which network topologies using different CRE strategies are displayed, and load balancing performance is compared in terms of CRE user ratio. Relatively good CRE strategies are derived from statistics. Furthermore, as CRE will largely decrease network throughput due to unexpected interference on the downlink path from Macrocell Base Stations (MBSs) to CRE users, we then apply Coordinated Multi-Point (CoMP) transmission in heterogeneous deployment to deal with the inter-cell interference. Intensive simulations show that CoMP could significantly improve both cell average Spectrum Efficiency (SE) and 5% cell edge SE, and a recommended value of CoMP bias which decides CoMP user ratio is given at last.

In traditional cellular networks, User Equipments (UEs) always connect to the strongest Base Station (BS), which offers either the maximum Signal to Interference Plus Noise Ratio (SINR) or the best Reference Signal Received Power (RSRP). However, in heterogeneous networks, due to a large difference of transmit power between macrocells and low power nodes, the user association methods mentioned above may lead to unbalance of user distribution between the two kinds of BSs. The goal of our research is to balance the user association between macrocells and picocells while maintain the network performance. We solve the user association problem by introducing the topological potential between UEs and BSs, from which both the channel condition and the number of severing UEs in each cell are considered. Since maximizing the topological potential in network-wide is NP-hard, a heuristic method is proposed to reach a suboptimal. Finally, the tradeoff between association fairness and network throughput is discussed via system level simulation, and a better fairness user association is given compared to the range expansion method in the same condition of network throughput.

The energy efficiency (EE) optimization in downlink carrier aggregated networks is addressed in this paper. To maximize EE by joint component carrier (CC) configuration and power control, we first model the problem as a mixed integer non-linear programming problem, which is NP-hard. Then the existence of optimal solution is proved. Based on the properties of optimal solution obtained by analysis, an optimal algorithm is found. Finally, a low-complex suboptimal algorithm is proposed by exploiting inherent features of CC configuration and dividing power control into two subproblems, which can be solved optimally. Simulation results show that EE of proposed algorithms is significantly better than that of conventional algorithm, and the suboptimal algorithm can greatly reduce complexity with little loss of EE in comparison to the optimal algorithm.

Cooperative spectrum sensing (CSS) can improve the performance of spectrum sensing greatly in cognitive radio (CR), however, the energy consumption in CSS also increases because there are more second users taking part in spectrum sensing. To improve the energy efficiency while maintaining the sensing accuracy to a desired threshold, we propose a simple and practical CSS method called two-stage cooperative spectrum sensing. We seek to improve the energy efficiency by decreasing the average number of SUs carrying out spectrum sensing, so we divide the sensing procedure into two stages and let it stop at the first stage if the channel is sensed as occupied. Then particle swarm optimization (PSO) algorithm is introduced to maximize the energy efficiency. Simulation results show that the convergence performance of our method is quite good and the energy efficiency of our method is significantly improved compared with the single-stage CSS.

Heterogeneous Network (HetNet) in 3GPP is a promising way to increase network coverage and capacity compared to macro cellular-only network. In order to further increase network capacity and balance cell load, Cell Range Expansion (CRE) and time-domain Inter-Cell Interference Coordination (ICIC) have been introduced into LTE-Advanced. The goal of our research is to mitigate the interference towards the User Equipments (UEs) served by the picocells and maintain the throughput of macrocells at the same time. We solve the problem by reducing the transmission power of macro Base Stations (BSs) in some subframes based on the proposed utility function, and accordingly scheduling picocell CRE UEs in these subframes. The existence of optimal solutions to the problem is discussed, and the Differential Evolution (DE) is applied to find the optimal transmission power which maximizes the utility function. System level simulation results show that the proposed algorithm can not only increase the total network's capacity but also improve the load balance between macrocell and picocell.

We address the energy-efficient optimization for MU-MISO (multi user multi-input single-output) downlink. An energy efficiency (EE) optimization model under the SINR constraints is proposed, and the feasibility conditions of the proposed EE optimization model is derived. Base on the model, a novel strategy that separates EE optimization to the sum of rates maximization and total transmit power approximation is proposed. Following this strategy, a convex approximation and geometric programming based algorithm is further developed to adjust beamformers and powers jointly. The algorithm is good at EE performance and practicality. The comprehensive numerical results are provide to demonstrate the significant EE performance gain achieved by our algorithm, and the convergence of proposed algorithm is proved by both theory analysis and simulation.

A Polarization Mode Dispersion Tolerant Subcarrier-power Allocation (PMDTSA) scheme is proposed to improve the Power Amplifier (PA) energy efficiency. The proposed scheme is applied in the multiuser downlink system based on Joint Polarization-Amplitude-Phase Modulation (JPAPM). Dut to the wireless channel's polarization mode dispersion effect, the polarization based impairment to JPAPM on each subcarrier will be diverse. Through the optimal subcarrier-power allocation, such diversity can be utilized to make PA energy efficiency optimal under the constraint of each user terminal's data rate demand. To reduce the allocation's computationally complexity, the PMDTSA scheme performs the subcarrier allocation and power allocation separately. Firstly, assuming the equal power distribution, the subcarrier is allocated through Particle Swarm Optimization (PSO); then based the obtained subcarrier allocation method, a two-step allocation algorithm is presented to distribute the PA input power on each subcarrier. Through numerical calculation, the optimal parameters setting of PSO is examined. Our results show that the proposed PMDTSA scheme is able to achieve significant improvement in PA energy efficiency.

Per-cell codebook based precoding is an effective transmission method in coordinated multi-point (CoMP) systems with the limited feedback constraint. There are two codeword selection methods for per-cell codebook, one is joint codeword selection (JCS) and another is independent codeword selection (ICS). In this paper, the system throughput obtained by the JCS and the ICS is analyzed firstly, which shows that JCS has a better trade-off between system throughput and feedback overhead. Then a codebook compression scheme for the JCS is proposed, which decreases the selection complexity of JCS by reducing the size of the codebook. Simulation results show that the proposed scheme can decrease the selection complexity greatly with tiny loss on performance and no additional feedback overhead.

An Energy Efficient Subcarrier-power Allocation (EESA) scheme is proposed to improve the Power Amplifier (PA) energy efficiency. The proposed scheme is applied for the Polarization-Amplitude-Phase Modulation (PAPM) in the channel with Polarization Mode Dispersion (PMD). Considering a multiuser downlink system, utilizing the diversity of the polarization characteristic on each subcarrier caused by PMD, EESA scheme can make PA energy efficiency optimal under the constraint of each user terminal's data demand. To reduce the allocation's computationally complexity, the EESA scheme performs the subcarrier allocation and power allocation separately. Firstly, by assuming the equal power distribution, the subcarrier is allocated through Differential Evolution (DE); then based the obtained subcarrier allocation method, a two-step allocation algorithm is presented to distribute the PA input power on each subcarrier. Through numerical calculation, the optimal parameters setting of DE is examined. Our results show that the EESA scheme is able to achieve significant improvement in PA energy efficiency.

In wireless communications, wideband polarization dependent loss (PDL) has become significant with the development of signal processing in wideband polarization domain. Aiming at deriving PDL statistical characteristic, a polarized time-variant multipath channel transfer function matrix H which considers azimuth power spectra, power delay profile (PDP), polarization power imbalance and polarization correlations is proposed. Closed-form expressions have been derived for various autocorrelation functions and cross-correlation functions for the polarized channel to fully characterize its statistical property. PDL is defined as the ratio between maximum and minimum eigenvalues of HHHat frequency f. However, due to the difficulty to analytically derive PDL's statistical characteristic, i.e., the distribution of PDL, numerical sum-of-sinusoids simulator is realized to accurately emulate the channel correlations derived above. Inspired by Weibull distribution, the probability density function (PDF) of PDL is well curve fitted using raw data from numerical simulator. The function is parameterized by scale factor, shape factor and normalization factor which all relate to polarization power imbalance and polarization correlations of the polarized channel. Results show that the degree of attenuation unbalance towards eigen-polarizations is in the descending order as: NLOS macrocell > NLOS microcell > NLOS picocell.

In this paper, we propose a new blind spectrum sensing method based on the polarization characteristic of the received signal, which is completely represented by the orientation of a polarization vector. We first discuss a spectrum sensing model based on polarization vectors' orientation. Then we develop the directional statistics of polarization vectors that contain both the signal and noise or noise only. The distinctive difference between the two statistics can be used to decide whether the primary signal exists or not. Based on this, by using the well-known generalized likelihood ratio test (GLRT) paradigm, a new polarization sensing algorithm GLRT-polarization vector (GLRT-PV) is proposed. By applying directional statistics, we derive closed-form expressions for the probability of false alarm and the probability of detection under both dual-polarized additive white Gaussian noise (AWGN) and Rayleigh-fading channels. Our numerical simulation and experimental results show that the proposed method exhibits better performance than other existing methods in the case of unknown primary transmitter polarization and/or presence of noise power uncertainty.

We develop cognitive resource allocation scheme to mitigate co-tier and cross-tier interference in overlay femtocell networks. By exploiting statistical models for characterizing multitier interference, our scheme avoids prohibitive exchange of realtime interference statistics in the network. The proposed scheme is independently implemented at each femtocell and allocates resources distributedly in response to the probabilistic interference conditions in the network. This “self-organizing” framework can be useful to address interference management in dense, ad-hoc, and consumer-deployed femtocell networks. Simulation results show that the proposed scheme can improve the throughput of femtocell links while simultaneously reducing cross-tier interference.

In the LTE networks, the multicast services can be transmitted by single frequency network (SFN) mode and point-to-multipoint (PTM) mode, and the unicast services are delivered with point-to-point (PTP) mode. To avoid the network congestion in the LTE networks with the multicast and unicast mixed services, an analytic hierarchy process (AHP) in load balancing is proposed to minimize the demanded radio resources of the maximum load cell. The system model of the demanded radio resources in the maximum load cell is based on AHP. For the same multicast service, the AHP calculates the weight of the demanded radio resources with the three transmission modes of SFN, PTM and PTP. Then the selecting problem of the transmission mode is solved by means of simulated annealing (SA) heuristics. It selects the optimal mode between SFN and PTM for the multicast services. Simulation results show that the proposed AHP in load balancing achieves less demanded radio resources of the maximum load cell than SFN mode for all the multicast services.

Compressive sampling techniques can effectively reduce the acquisition costs of high-dimensional signals by utilizing the fact that typical signals of interest are often sparse in a certain domain. For compressive samplers, the number of samples Mrneeded to reconstruct a sparse signal is determined by the actual sparsity order Snzof the signal, which can be much smaller than the signal dimension N. However, Snzis often unknown or dynamically varying in practice, and the practical sampling rate has to be chosen conservatively according to an upper bound Smaxof the actual sparsity order in lieu of Snz, which can be unnecessarily high. To circumvent such wastage of the sampling resources, this paper introduces the concept of sparsity order estimation, which aims to accurately acquire Snzprior to sparse signal recovery, by using a very small number of samples Meless than Mr. A statistical learning methodology is used to quantify the gap between Mrand Mein a closed form via data fitting, which offers useful design guideline for compressive samplers. It is shown that Me≥ 1.2Snzlog(N/Snz+ 2) + 3 for a broad range of sampling matrices. Capitalizing on this gap, this paper also develops a two-step compressive spectrum sensing algorithm for wideband cognitive radios as an illustrative application. The first step quickly estimates the actual sparsity order of the wide spectrum of interest using a small number of samples, and the second step adjusts the total number of collected samples according to the estimated signal sparsity order. By doing so, the overall sampling cost can be minimized adaptively, without degrading the sensing performance.

In cognitive radio (CR) networks, multi-CR cooperation is required during spectrum sensing in order to cope with wireless fading effects and the hidden terminal problem. User cooperation offers not only channel diversity gain against fading, but also complexity gain in terms of reduced sampling costs per CR. The latter is particularly useful when the monitored spectrum has very wide bandwidth and yet individual CRs only have limited hardware capability. To jointly collect both diversity gain and complexity gain, this paper develops a novel cooperative spectrum sensing technique based on matrix rank minimization. Subject to sampling-rate limitations, CRs individually collect digital measurements from a segment of the wide spectrum via coordinated selective filtering, with optional compressive sampling to further reduce the sampling rates. The solutions representing the measurements of all users are modeled to possess a low-rank property, and the rank order is the same as the size of the nonzero support of the monitored wide spectrum. Accordingly, a nuclear norm minimization problem is formulated to jointly identify the nonzero support and hence the overall wideband spectrum occupancy. Both tradeoff evaluation and simulation results corroborate that the proposed cooperative sensing technique outperforms traditional averaging-based cooperative schemes given the same sampling costs, because the low-rank property enables efficient utilization and tradeoff of the user diversity in the absence of any channel knowledge.

In this paper, the energy efficiency of cooperative ARQ transmission in Amplify-and-Forward (AF) relay networks is discussed. The average total energy consumed per bit for cooperative ARQ under Quality of Service (QoS) constraints is formulated. With numerical method, the objective is optimized over the transmission data rate, the transmit power of the source and the relay, given fixed maximum retransmission number. Then, performances of various maximum retransmission numbers are analyzed. Simulation results demonstrate that cooperative ARQ with large maximum retransmission number has solid capability to save energy when the transmission energy plays a dominant role compared to circuit energy. However, for energy saving, small maximum retransmission number should be adopted on condition that tight maximum average transmission delay constraint is imposed.

In cognitive radio (CR) networks, multi-CR cooperation typically takes place during spectrum sensing, to cope with wire less fading effects and the hidden terminal problem. The user cooperation gain not only offers channel diversity against fading, but also allows for reduced sampling costs per CR, which is particularly relevant when the monitored spectrum has very wide bandwidth. To attain a desired tradeoff between channel diversity and sampling costs, this paper develops a new cooperative spectrum sensing technique based on matrix rank minimization. In the absence of channel knowledge, CRs individually collect digital measurements from a region of the wide spectrum via selective filtering, with optional compressive sampling to further reduce sampling rates. The solutions representing all the measurements are modeled to possess a low rank property, with the rank order being the same as the size of the nonzero support of the monitored wide spectrum. Accordingly, a nuclear norm minimization problem is formulated to jointly identify the nonzero support and hence the overall spectrum occupancy. Simulations show that the pro posed technique outperforms traditional averaging-based co operative schemes, given the same sampling costs.

For cognitive radios (CRs), compressive sampling (CS) techniques have been utilized for spectrum sensing in order to alleviate the high signal acquisition costs in the wideband regime. Given the desired sensing performance, the fundamental limit on the sampling rates is determined by the actual sparsity order Snzof the signal spectrum, which can be considerably lower than the Nyquist sampling rate. However, Snzis time-varying and hence unknown a priori for a dynamic CR network, and is typically available in the form of its statistical upper bound Smax. When the practical sampling rate is chosen according to Smaxin lieu of Snz, this rate is unnecessarily high for accurate spectrum sensing and hence wasteful of the sensing resources. To circumvent this problem, this paper develops a two-step compressed spectrum sensing (TS-CSS) scheme for efficient wideband sensing. The first step quickly estimates the actual sparsity order of the wide spectrum of interest using a small number of samples, and the second step adjusts the total number of samples collected according to the estimated signal sparsity order. By doing so, the overall sampling rate is minimized adaptively. The cornerstone of this work is the fact that the number of measurements required for estimating the signal sparsity order is (much) smaller than that for reconstructing the sparse signal itself. The gap between these two numbers is delineated in this paper in closed form, which offers valuable design guideline. Validated by simulations, the proposed TS-CSS scheme achieves the desired sensing performance at considerably reduced sensing costs, using a lowered average sampling rate compared with traditional one-step compressive sampling schemes.

A Virtual Polarization Detection (VPD) method based on the vector signal processing, is presented in this paper for the effective spectrum sensing of cognitive radios. The purpose of such VPD method is to exploit the orthogonal polarization component of primary signals besides the conjugate, in terms of vector information elements of signals. The spectrum sensing scenario of a single secondary user and multiple primary users is discussed, where the vector signals arrived at the secondary user are received by a pair of orthogonally polarized antennae. For the test of two-class problem (the primary user present class versus absent class), the secondary user optimizes the receiving polarization state to get the maximum primary signal to noise ratio by processing the received orthogonal polarization components. Specifically, it takes place in the processor of the secondary user virtually instead of antennae devices adaptation. The VPD method does not require any prior knowledge of the signal polarization states. The performance of our VPD method is compared with that of energy detection which uses scalar amplitude information only to sense the primary users. Simulation results show that the VPD method improves the spectrum sensing performance significantly.

Radio resource allocation is one of the key technologies in orthogonal frequency division multiplexing (OFDM) cellular systems, where subcarriers and power are schedulable resources. In this paper, we solve the fair resource allocation problem based on the idea of the Nash bargaining solution (NBS) from cooperative game theory, which not only provides the resource allocation of users that are Pareto optimal from the view of the whole system, but also are consistent with the fairness axioms of game theory. We develop a suboptimal solution of NBS via low-pass time window filter and first-order Taylor expansion, and then propose an efficient and practical dynamic subcarriers allocation algorithm. Simulation results show that the proposed dynamic subcarriers allocation algorithm providing utility fairness and improving system capacity.