With the advantages of low signaling overhead and latency, grant-free random access (GFRA) becomes a promising technology for supporting massive machine-type communications (mMTCs), but poses new challenges for active user detection (AUD) and channel estimation (CE), whose performance mainly depends on the preamble detection. In this article, we design the composite preamble based on differential phase rotations by aggregating orthogonal Zadoff-Chu (ZC) sequences and multiple root ZC sequences with differential phase rotations to reduce the probability of preamble collisions, thereby improving the performance of AUD and CE. In particular, differential phase rotations extend the preamble set size so that users colliding in orthogonal sequences can be distinguished by phase rotations. In addition, it also reduces nonorthogonal interference and thus reduces CE errors. The preamble detection algorithm and CE scheme are proposed, along with the theoretical analysis of AUD and CE performance to verify the effectiveness of the designed preamble. In addition, the proposed preamble is extended to combine phase rotations with cyclic shifts to further enlarge the preamble set size with low nonorthogonality. Simulation results show that the proposed composite preamble outperforms existing preambles in terms of the probability of detection and CE accuracy.

The novel concept of non-orthogonal multiple access (NOMA) aided joint radar and multicast-unicast communication (Rad-MU-Com) is investigated. Employing the same spectrum resource, a multi-input-multi-output (MIMO) dual-functional radar-communication (DFRC) base station detects the radar-centric user (R-user), while transmitting mixed multicast-unicast messages both to the R-user and to the communication-centric user (C-user). In particular, the multicast information is intended for both the R- and C-users, whereas the unicast information is only intended for the C-user. More explicitly, NOMA is employed to facilitate this double spectrum sharing, where the multicast and unicast signals are superimposed in the power domain and the superimposed communication signals are also exploited as radar probing waveforms. A beamformer-based NOMA-aided joint Rad-MU-Com framework is proposed for the system having a single R-user and a single C-user. Based on this framework, the unicast rate maximization problem is formulated by optimizing the beamformers employed, while satisfying the rate requirement of multicast and the predefined accuracy of the radar beam pattern. The resultant non-convex optimization problem is solved by a penalty-based iterative algorithm to find a high-quality near-optimal solution. Finally, our numerical results reveal that significant performance gains can be achieved by the proposed scheme over the benchmark schemes.

This paper proposes an improved version of concatenated code scheme in massive random access, with performance enhancement and low-complexity implementation. Under theoretical idealization, most existing solutions only consider improving energy efficiency, ignoring the cost of implementation complexity. However, both of them are vital when it comes to practical realization. Although the concatenated code system is simple to implement, its performance is relatively weak, and the equivalent mapping detection method adopted at the receiving end causes performance loss. Therefore, based on concatenated code system, this paper makes performance improvements in the following three aspects: (i) using multi-level decisions to avoid loss of information; (ii) a priori information aided detection utilizing the distribution of multi-level symbols to minimize the error probability of detection; (iii) soft information output to enhance soft decoders at the next stage. Meanwhile, in decoding complexity, the improved algorithm retains the low complexity feature of the concatenated code system and achieves linear implementation. This scheme successfully enhances energy efficiency at a lower complexity cost by improving the detection algorithm. We prove that the proposed method is an implementation-oriented massive random access with advantages in energy efficiency and performance by theory and simulation.

The novel concept of non-orthogonal multiple access (NOMA) aided joint radar and multicast-unicast communication (Rad-MU-Com) is investigated. Employing the same spectrum resource, a multi-input-multi-output (MIMO) dual-functional radar-communication (DFRC) base station detects the radar-centric users (R-user), while transmitting mixed multicast-unicast messages both to the R-user and to the communication-centric user (C-user). In particular, the multicast information is intended for both the R- and C-users, whereas the unicast information is only intended for the C-user. More explicitly, NOMA is employed to facilitate thisdouble spectrum sharing, where the multicast and unicast signals are superimposed in the power domain and the superimposed communication signals are also exploited as radar probing waveforms. First, abeamformer-basedNOMA-aided joint Rad-MU-Com framework is proposed for the system having a single R-user and a single C-user. Based on this framework, the unicast rate maximization problem is formulated by optimizing the beamformers employed, while satisfying the rate requirement of multicast and the predefined accuracy of the radar beam pattern. The resultant non-convex optimization problem is solved by a penalty-based iterative algorithm to find a high-quality near-optimal solution. Next, the system is extended to the scenario of multiple pairs of R- and C-users, where acluster-basedNOMA-aided joint Rad-MU-Com framework is proposed. A joint beamformer design and power allocation optimization problem is formulated for the maximization of the sum of the unicast rate at each C-user, subject to the constraints on both the minimum multicast rate for each R&C pair and on accuracy of the radar beam pattern for detecting multiple R-users. The resultant joint optimization problem is efficiently solved by another penalty-based iterative algorithm developed. Finally, our numerical results reveal that significant performance gains can be achieved by the proposed schemes over the benchmark schemes employing conventional transmission strategies.

The novel concept of simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surfaces (RISs) is investigated, where the incident wireless signal is divided into transmitted and reflected signals passing into both sides of the space surrounding the surface, thus facilitating a full-space manipulation of signal propagation. Based on the introduced basic signal model of `STAR', three practical operating protocols for STAR-RISs are proposed, namely energy splitting (ES), mode switching (MS), and time switching (TS). Moreover, a STAR-RIS aided downlink communication system is considered for both unicast and multicast transmission, where a multi-antenna base station (BS) sends information to two users, i.e., one on each side of the STAR-RIS. A power consumption minimization problem for the joint optimization of the active beamforming at the BS and the passive transmission and reflection beamforming at the STAR-RIS is formulated for each of the proposed operating protocols, subject to communication rate constraints of the users. For ES, the resulting highly-coupled non-convex optimization problem is solved by an iterative algorithm, which exploits the penalty method and successive convex approximation. Then, the proposed penalty-based iterative algorithm is extended to solve the mixed-integer non-convex optimization problem for MS. For TS, the optimization problem is decomposed into two subproblems, which can be consecutively solved using state-of-the-art algorithms and convex optimization techniques. Finally, our numerical results reveal that: 1) the TS and ES operating protocols are generally preferable for unicast and multicast transmission, respectively; and 2) the required power consumption for both scenarios is significantly reduced by employing the proposed STAR-RIS instead of conventional reflecting/transmiting-only RISs.

Orthogonal time-frequency space signaling (OTFS) is a novel modulation scheme that can exploit full time-frequency (TF) diversity when coupled with a proper receiver. Toward this end, this paper presents a joint channel estimation and equalization approach for OTFS based on the expectation propagation (JCEE-EP). First, We apply discrete prolate spheroidal basis expansion model (DPS-BEM) to describe the time-varying (TV) channel and obtain a Gaussian prior of BEM coefficients via LS estimator. Based on the delay-Doppler (DD) domain to time domain transformation, we then decompose the joint posterior probability into a couple of factors and approximate each one by Gaussian distributions according to the EP principle. As the moment-matching (MM) step in channel factor update is computationally intractable, we resort to the first-order conditional EP (CEP) method for an approximate solution. Finally, the bit error rate (BER) simulation results demonstrate the superiority of our proposed JCEE-EP over the existing approaches.

This paper investigates intelligent reflecting surface (IRS)-assisted systems, where an access point sends independent information to multiple users with the aid of one IRS. Our goal is to characterize the capacity region of the IRS-assisted multiuser communication systems. We jointly optimize the discrete phase-shift matrix of the IRS and resource allocation with the capacity-achieving non-orthogonal multiple access (NOMA) transmission scheme. The Pareto boundary of the capacity region is characterized by maximizing the average sum rate of all users, subject to a set of rate-profile constraints, total transmit power and discrete IRS phase shift constraints. Though the formulated problem is non-convex, we derive the globally optimal solutions by invoking the Lagrange duality method. It is shown that the optimal transmission strategy is alternating transmission among different user groups by dynamically adjusting the IRS phase shifts. We further propose a Hadamard codebook based scheme, which serves as a lower bound on the optimal performance gains. Numerical results demonstrate that: i) the IRS is capable of significantly improving the capacity region; ii) the capacity region achieved by the Hadamard codebook based scheme is close to that of discrete phase shifts for a small number of IRS elements.

As an enabling technology to address spectrum shortage, spectrum sensing has been investigated a lot. However, the uncertainties in the detection environment, including noise uncertainty and carrier frequency (CF) mismatch, still remain as the main challenges of spectrum sensing, which greatly degrades the sensing performance of typical sensing methods, such as energy detection and cyclostationary detection. To this end, this paper proposes two robust spectrum sensing schemes by leveraging the difference between the phase difference (PD) distribution of noise-perturbed signal and that of Gaussian noise. Specifically, the compact approximation of the PD distribution is first derived to enable the extraction of the features of PD distributions, which are robust to noise uncertainty and CF mismatch. Based on these features, two sensing schemes based on the deep neural network (DNN), referred to as DNN-based PD distribution detection (PDD) and blind PDD (BPDD), are proposed to detect spectrum holes in cases with known CF and unknown CF, respectively. Simulation results show that our proposed schemes are more robust to CF mismatch and noise uncertainty in comparison with the existing sensing schemes. Furthermore, when the CF of the sensed signal is unknown, the proposed BPDD significantly outperforms existing blind sensing schemes.

In this article, we propose the Raptor coded random access (RCRA) scheme to enable reliable transmission in the decentralized unmanned aerial vehicle (UAV) network. The considered network is composed of several overlapped random access systems with interference nodes, and the proposed RCRA scheme reduces bit-error ratio (BER) of the random access systems by three steps. First, we choose the number of slots based on a derived lower bound, which is necessary for reliable random access. Second, error-correcting codes are incorporated as the precode before random access, and then the access probability is optimized to achieve the minimum BER. Third, by correlating two consecutive slots, an idle-slot-filling approach is designed to further improve the efficiency of the random access systems. Numerical results show that the proposed RCRA scheme reduces significantly both block-error ratio (BLER) and BER at moderate- and high-signal-to-noise ratio (SNR) region. With$E_{s}/N_{0}$equal to 0 dB, the RCRA scheme saves 20% slots, compared with the existing frameless ALOHA scheme, to achieve a target BER of 10−4.

Intelligent reflecting surface (IRS) enhanced multi-unmanned aerial vehicle (UAV) non-orthogonal multiple access (NOMA) networks are investigated. A new transmission framework is proposed, where multiple UAV-mounted base stations employ NOMA to serve multiple groups of ground users with the aid of an IRS. The three-dimensional (3D) placement and transmit power of UAVs, the reflection matrix of the IRS, and the NOMA decoding orders among users are jointly optimized for maximization of the sum rate of considered networks. To tackle the formulated mixed-integer non-convex optimization problem with coupled variables, a block coordinate descent (BCD)-based iterative algorithm is developed. Specifically, the original problem is decomposed into three subproblems, which are alternately solved by exploiting the penalty-based method and the successive convex approximation technique. The proposed BCD-based algorithm is demonstrated to be able to obtain a stationary point of the original problem with polynomial time complexity. Numerical results show that: 1) the proposed NOMA-IRS scheme for multi-UAV networks achieves a higher sum rate compared to the benchmark schemes, i.e., orthogonal multiple access (OMA)-IRS and NOMA without IRS; 2) the use of IRS is capable of providing performance gain for multi-UAV networks by both enhancing channel qualities of UAVs to their served users and mitigating the inter-UAV interference; and 3) optimizing the UAV placement can make the sum rate gain brought by NOMA more distinct due to the flexible decoding order design.

The fundamental capacity limits of intelligent reflecting surface (IRS)-assisted multi-user wireless communication systems are investigated in this article. Specifically, the capacity and rate regions for both capacity-achieving non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) transmission schemes are characterized by jointly optimizing the IRS reflection matrix and wireless resource allocation under the constraints of a maximum number of IRS reconfiguration times. In NOMA, all users are served in the same resource blocks by employing superposition coding and successive interference cancelation techniques. In OMA, all users are served by being allocated orthogonal resource blocks of different sizes. For NOMA, the ideal case with an asymptotically large number of IRS reconfiguration times is firstly considered, where the optimal solution is obtained by employing the Lagrange duality method. Inspired by this result, an inner bound of the capacity region for the general case with a finite number of IRS reconfiguration times is derived. For OMA, the optimal transmission strategy for the ideal case is to serve each individual user alternatingly with its effective channel power gain maximized. Based on this result, a rate region inner bound for the general case is derived. Finally, numerical results are provided to show that: i) a significant capacity and rate region improvement can be achieved by using IRS; ii) the capacity gain can be further improved by dynamically configuring the IRS reflection matrix.

Integrating robots into cellular networks creating connected robotic users has emerged as a promising technology for future smart cities and smart factories due to their low cost and high maneuverability. However, the requirement of establishing stable and high-quality communication links to the robotic users greatly restricts their applicability, especially in indoor environments where obstacles may block the wireless link. To tackle this challenge, in this paper, an indoor robot navigation system is investigated, where an intelligent reflecting surface (IRS) is employed to enhance the connectivity between the access point (AP) and robotic users. Both single-user and multiple-user scenarios are considered. In the single-user scenario, one mobile robotic user (MRU) communicates with the AP. In the multiple-user scenario, the AP serves one MRU and one static robotic user (SRU) employing either non-orthogonal multiple access (NOMA) or orthogonal multiple access (OMA) transmission. The considered system is optimized for minimization of the travelling time/distance of the MRU from a given starting point to a predefined final location, while satisfying constraints on the communication quality of the robotic users. To this end, a radio map based approach is proposed to exploit location-dependent channel propagation knowledge. For the single-user scenario, a channel power gain map is constructed, which characterizes the spatial distribution of the maximum expected effective channel power gain of the MRU for the optimal IRS phase shifts. Based on the obtained channel power gain map, the communication-aware robot path planing problem is solved by exploiting graph theory. For the multiple-user scenario, a communication rate map is constructed, which characterizes the spatial distribution of the maximum expected rate of the MRU for the optimal power allocation at the AP and the optimal IRS phase shifts subject to a minimum rate requirement for the SRU. The joint optimization problem is efficiently solved by invoking bisection search and successive convex approximation methods. Then, a graph theory based solution for the robot path planning problem is derived by exploiting the obtained communication rate map. Our numerical results show that: 1) the required travelling distance of the MRU can be significantly reduced by deploying an IRS; 2) NOMA yields a higher communication rate for the MRU than OMA; 3) the IRS performance gain is significantly more pronounced for NOMA than for OMA.

The fundamental intelligent reflecting surface (IRS) deployment problem is investigated for IRS-assisted networks, where one IRS is arranged to be deployed in a specific region for assisting the communication between an access point (AP) and multiple users. Specifically, three multiple access schemes are considered, namely non-orthogonal multiple access (NOMA), frequency division multiple access (FDMA), and time division multiple access (TDMA). The weighted sum rate maximization problem for joint optimization of the deployment location and the reflection coefficients of the IRS as well as the power allocation at the AP is formulated. The non-convex optimization problems obtained for NOMA and FDMA are solved by employing monotonic optimization and semidefinite relaxation to find a performance upper bound. The problem obtained for TDMA is optimally solved by leveraging thetime-selectivenature of the IRS. Furthermore, for all three multiple access schemes, low-complexity suboptimal algorithms are developed by exploiting alternating optimization and successive convex approximation techniques, where alocal region optimizationmethod is applied for optimizing the IRS deployment location. Numerical results are provided to show that: 1) near-optimal performance can be achieved by the proposed suboptimal algorithms; 2)asymmetricandsymmetricIRS deployment strategies are preferable for NOMA and FDMA/TDMA, respectively; 3) the performance gain achieved with IRS can be significantly improved by optimizing the deployment location.

This paper investigates ground-aerial uplink non-orthogonal multiple access (NOMA) cellular networks. A rotary-wing unmanned aerial vehicle (UAV) user and multiple ground users (GUEs) are served by ground base stations (GBSs) by utilizing the uplink NOMA protocol. The UAV is dispatched to upload specific information bits to each target GBSs. Specifically, our goal is to minimize the UAV mission completion time by jointly optimizing the UAV trajectory and UAV-GBS association order while taking into account the UAV's interference to non-associated GBSs. The formulated problem is a mixed integer non-convex problem and involves infinite variables. To tackle this problem, we efficiently check the feasibility of the formulated problem by utilizing graph theory and topology theory. Next, we prove that the optimal UAV trajectory needs to satisfy the fly-hover-fly structure. With this insight, we first design an efficient solution with predefined hovering locations by leveraging graph theory techniques. Furthermore, we propose an iterative UAV trajectory design by applying successive convex approximation (SCA) technique, which is guaranteed to coverage to a locally optimal solution. We demonstrate that the two proposed designs exhibit polynomial time complexity. Finally, numerical results show that: 1) the SCA based design outperforms the fly-hover-fly based design; 2) the UAV mission completion time is significantly minimized with proposed NOMA schemes compared with the orthogonal multiple access (OMA) scheme; 3) the increase of GUEs' quality of service (QoS) requirements will increase the UAV mission completion time.

Grant-free non-orthogonal multiple access (NOMA) is considered as one of the most important methodologies for the machine-type communications (MTC). In the field of MTC, compressed sensing based multi-user detection (CS-MUD) has been recognized as an excellent candidate for joint user activity and data detection, since many users sporadically transmit short-size data packets at low rates. This article focuses on the CS-MUD problem in the up-link grant-free NOMA scenario, where users are (in)-active randomly in each time slot yet with high temporal correlation. First, we investigate the CS framework to fully extract the underlying side information in the temporal correlation and propose a novel CS-MUD algorithm. Then, to mitigate the performance degradation due to the imperfect channel estimation in practice, the proposed algorithm is further extended by utilizing the perturbed CS, where the impact of channel estimation errors is modeled as certain perturbation in the measurement matrix. Different from most of the state-of-the-art CS-MUD algorithms, both proposed algorithms can apply even in the absence of prior knowledge on the number of active users. Simulation results indicate that the proposed algorithms achieve better performance than the existing CS-MUD methods. Their convergence and complexity issues are also discussed theoretically and numerically.

This paper investigates a downlink multiple-input single-output intelligent reflecting surface (IRS) aided non-orthogonal multiple access (NOMA) system, where a base station (BS) serves multiple users with the aid of IRSs. Our goal is to maximize the sum rate of all users by jointly optimizing the active beamforming at the BS and the passive beamforming at the IRS, subject to successive interference cancellation decoding rate conditions and IRS reflecting elements constraints. In term of the characteristics of reflection amplitudes and phase shifts, we consider ideal and non-ideal IRS assumptions. To tackle the formulated non-convex problems, we propose efficient algorithms by invoking alternating optimization, which design the active beamforming and passive beamforming alternately. For the ideal IRS scenario, the two subproblems are solved by invoking the successive convex approximation technique. For the non-ideal IRS scenario, constant modulus IRS elements are further divided into continuous phase shifts and discrete phase shifts. To tackle the passive beamforming problem with continuous phase shifts, a novel algorithm is developed by utilizing the sequential rank-one constraint relaxation approach, which is guaranteed to find a locally optimal rank-one solution. Then, a quantization-based scheme is proposed for discrete phase shifts. Finally, numerical results illustrate that: i) the system sum rate can be significantly improved by deploying the IRS with the proposed algorithms; ii) 3-bit phase shifters are capable of achieving almost the same performance as the ideal IRS; iii) the proposed IRS-aided NOMA systems achieve higher system sum rate than the IRS-aided orthogonal multiple access system.

This paper investigates ground-aerial uplink non- orthogonal multiple access (NOMA) cellular networks. A unmanned aerial vehicle (UAV) user and ground users (GUEs) are served by ground base stations (GBSs) by utilizing uplink NOMA protocol. The goal is to minimize the UAV mission completion time by jointly designing the UAV trajectory and UAV-GBS association vectors while considering the interference of UAV to other non-associated GBSs. The formulated problem is a mixed integer non-convex problem and involves infinite number of variables, which is difficult to be directly solved. To tackle this challenge, we first prove the optimal UAV trajectory satisfies \emph{fly-hover-fly} communication policy. With this insight, we propose an efficient algorithm to solve the original problem based on a properly constructed graph by invoking graph theory and convex optimization techniques. Numerical results show that the UAV mission completion time is significantly minimized with proposed NOMA scheme compared with conventional orthogonal multiple access (OMA) communication and reveal a tradeoff between the UAV mission completion time and GUEs' quality-of-service (QoS) requirements.

Orthogonal time frequency space modulation (OTFS) which is a novel modulation scheme, aims at offering reliable communications to users under high mobility conditions. Since each symbol in OTFS experiences the same fading over the doubly selective channel, OTFS is able to exploit full time-frequency (TF) diversity when coupled with a proper equalizer. In this paper, we study the low complexity iterative equalizer design for the coded OTFS system. In order to jointly combat the two-dimensional interference, we adopt linear minimum mean squared error based parallel interference cancellation (LMMSE-PIC) as the equalizer for OTFS. The first order Neumann series is used to approximate the matrix inversion involved in LMMSE-PIC for OTFS, which reduces the equalizer complexity to quasi-linear to the total number of transmitted symbols. Extrinsic information transfer (EXIT) charts and coded bit error rate (BER) simulation results show that our proposed LMMSE-PIC equalizer outperforms the recently proposed PIC approach for OTFS and also achieves considerable gain compared to orthogonal frequency division multiplexing (OFDM).

Spectrum efficiency can be greatly improved through high-accuracy spectrum sensing in cognitive unmanned aerial vehicle (UAV) networks. However, the traditional centralized cooperative spectrum sensing (CCSS) methods are not applicable to the spectrum sensing of cognitive UAV networks, since the mobility of nodes and the dynamicity of network topology make it challenging to gather all the sensing information into a fusion center (FC) quickly enough. To overcome the challenge, this paper proposes a clustering-based distributed cooperative spectrum sensing (c-DCSS) scheme. Specifically, the considered cognitive UAV network is first clustered based on Max-Min distance clustering methods by jointly taking the position, velocity, and moving direction of UAVs in account, and then a two-stage fusion scheme is adopted to execute hierarchical sensing information fusion. Simulation results show that compared to the unclustered DCSS (u-DCSS) scheme, the proposed scheme significantly enhances the spectrum detection performance of cognitive UAV networks, especially when the number of UAV nodes is relatively large.

UAV has attracted a significant amount of attention for its low-cost and diverse applications like video surveillance, auxiliary communication, etc. In this paper, the UAV fire surveillance network is proposed and the maximization of the UAV-centric energy efficiency (EE) is investigated by jointly taking the source/channel rate control and flow routing into account. The design is cast into a cross-layer optimization problem, which is proven to be difficult to solve. In light of it, a parametric transformation approach is adopted to convert the original problem into a tractable form and further decouple it into two independent subproblems. An efficient algorithm consisting of a two-layer iterative algorithm with an inner loop and an outer loop is proposed to solve the transformed problem. Simulation results show the impact of the network configuration on the network-wide EE and the performance of the proposed algorithm.

The mobility load balancing (MLB) in self-organizing networks (SONs) is designed to automatically resolve the mismatch between network resource distribution and network traffic demand. In this paper, we propose an off-policy deep reinforcement learning (DRL) based MLB framework to balance the load distribution among all the cells. Our main contribution is three-fold. First, we propose to use off-policy RL with multiple behavior policies to autonomously learn the optimal MLB policy without any prior knowledge over the underlying wireless environments. Second, we propose a corresponding DRL-based MLB model by using deep neural networks as the function approximators to improve the generalization ability over complex system states. Third, we propose an asynchronous parallel learning framework for MLB to improve the training efficiency in a collaborative manner. Experimental results show that our proposed DRL-based MLB model can outperform the existing approaches considerably.

Distributed Gaussian Process (GP) is a scalable Bayesian method that is promising for handling big data. Our contribution in applying GP for traffic prediction is two-fold. First, to the best of our knowledge, this paper is the first to empower GP regression with the alternating direction method of multipliers (ADMM) for distributed hyper-parameter optimization in the training phase, where the ADMM training framework well balances local estimation and information consensus in a principled way. Second, in the prediction phase, we fuse local predictions obtained from distributed computing units via a cross-validation based optimal strategy, which demonstrates itself to be reliable and robust for general regression tasks. Moreover, the cross-validation based optimal fusion strategy is built upon a well acknowledged probabilistic model to retain the valuable closed-form GP prediction properties. Experimental results show that our proposed distributed GP model can outperform the state-of-the-art distributed GP models considerably, in terms of wireless traffic prediction performance.

Fountain codes are promising for unmanned aerial vehicle (UAV) communications with intermittent transmission links caused by high UAV mobility. However, it is challenging to estimate the transmission delay of UAV communication systems with fountain codes due to the uncertainty of the coding rate and the dynamic channel quality. In this paper, we propose a delay estimation method based on a joint buffer-decoder queuing model for UAV communication systems with LT codes, and show that the complexity of the proposed delay estimation method can be reduced from O(n3) to O(n2). Simulation results validate the effectiveness of the proposed delay estimation method.

In this paper, a novel three timeslots frame structure is proposed for Energy-Harvesting Cognitive Radio Networks, where the frame structure includes spectrum sensing, energy harvesting and non-orthogonal multiple access uplink transmission. Our goal is to maximize the sum-capacity of secondary users (SUs) by jointly optimizing spectrum sensing duration, energy harvesting duration and data transmission duration. To solve the challenging problem, we first derive the closed form expressions for optimal energy harvesting and data transmission durations by fixing the spectrum sensing duration, and then optimize the spectrum sensing duration by golden section search method. Finally, we obtain a sub-optimal solution through the alternate iteration of two previous steps. Simulation results show that the sum-capacity of SUs under the proposed scheme significantly increases compared to the time division multiple access (TDMA) uplink transmission protocol, especially when the transmitted power of cognitive base station (CBS) increases and the number of SUs is large enough.

In this article, we propose a deep reinforcement learning (DRL)-based mobility load balancing (MLB) algorithm along with a two-layer architecture to solve the large-scale load balancing problem for ultradense networks (UDNs). Our contribution is threefold. First, this article proposes a two-layer architecture to solve the large-scale load balancing problem in a self-organized manner. The proposed architecture can alleviate the global traffic variations by dynamically grouping small cells into self-organized clusters according to their historical loads, and further adapt to local traffic variations through intracluster load balancing afterwards. Second, for the intracluster load balancing, this article proposes an off-policy DRL-based MLB algorithm to autonomously learn the optimal MLB policy under an asynchronous parallel learning framework, without any prior knowledge assumed over the underlying UDN environments. Moreover, the algorithm enables joint exploration with multiple behavior policies, such that the traditional MLB methods can be used to guide the learning process thereby improving the learning efficiency and stability. Third, this article proposes an offline-evaluation-based safeguard mechanism to ensure that the online system can always operate with the optimal and well-trained MLB policy, which not only stabilizes the online performance but also enables the exploration beyond current policies to make full use of machine learning in a safe way. Empirical results verify that the proposed framework outperforms the existing MLB methods in general UDN environments featured with irregular network topologies, coupled interferences, and random user movements, in terms of the load balancing performance.

The cloud radio access network (C-RAN) is a promising paradigm to meet the stringent requirements of the fifth generation (5G) wireless systems. Meanwhile, the wireless traffic prediction is a key enabler for C-RANs to improve both the spectrum efficiency and energy efficiency through load-aware network managements. This paper proposes a scalable Gaussian process (GP) framework as a promising solution to achieve large-scale wireless traffic prediction in a cost-efficient manner. Our contribution is three-fold. First, to the best of our knowledge, this paper is the first to empower GP regression with the alternating direction method of multipliers (ADMM) for parallel hyper-parameter optimization in the training phase, where such a scalable training framework well balances the local estimation in baseband units (BBUs) and information consensus among BBUs in a principled way for large-scale executions. Second, in the prediction phase, we fuse local predictions obtained from the BBUs via a cross-validation-based optimal strategy, which demonstrates itself to be reliable and robust for general regression tasks. Moreover, such a cross-validation-based optimal fusion strategy is built upon a well acknowledged probabilistic model to retain the valuable closed-form GP inference properties. Third, we propose a C-RAN-based scalable wireless prediction architecture, where the prediction accuracy and the time consumption can be balanced by tuning the number of the BBUs according to the real-time system demands. The experimental results show that our proposed scalable GP model can outperform the state-of-the-art approaches considerably, in terms of wireless traffic prediction performance.

This paper studies an energy beamforming and time duration joint optimization problem for maximizing the sum-rate of a battery-free-multi-antenna-relay-assisted wireless powered communication network (WPCN). The considered problem is non-convex due to the strongly coupled optimization variables. By introducing new optimization variables and reformulating the problem into a convex problem, the optimal solution is obtained based on convex optimization methods. Furthermore, an alternate iteration algorithm is proposed by decoupling the problem into two subproblems, and a semi-closed form solution of the optimal energy beamforming matrix is derived with given time allocation. Simulation results indicate that the proposed battery-free multi- antenna relay architecture can deliver a significant sum-rate gain for the WPCN. In addition, the proposed suboptimal algorithm only has 5% sum-rate loss compared to the optimal solution.

Person Re-Identification (ReID) refers to the task of verifying the identity of a pedestrian observed from nonoverlapping surveillance cameras views. Recently, it has been validated that re-ranking could bring extra performance improvements in person ReID. However, the current re-ranking approaches either require feedbacks from users or suffer from burdensome computation cost. In this paper, we propose to exploit a density-adaptive kernel technique to perform efficient and effective re-ranking for person ReID. Specifically, we present two simple yet effective re-ranking methods, termed inverse Density-Adaptive Kernel based Re-ranking (inv-DAKR) and bidirectional Density-Adaptive Kernel based Re-ranking (bi-DAKR), which are based on a smooth kernel function with a density-adaptive parameter. Experiments on six benchmark data sets confirm that our proposals are effective and efficient.

An efficient codebook optimization algorithm is proposed to maximize mutual information in sparse code multiple access (SCMA). At first, SCMA signal model is given according to superposition modulation structure, in which the channel matrix is column‐extended. The superposition model can well describe the relationship between the codebook matrix and received signal. Based on the above model, an iterative codebook optimization algorithm is proposed to maximize mutual information between discrete input and continuous output. This algorithm can efficiently adapt to multiuser channels with arbitrary channel coefficients. The simulation results show that the proposed algorithm has good performance in both AWGN and non‐AWGN channels. In addition, message passing algorithm (MPA) works well with the codebook optimized according to the proposed algorithm.

In this paper, a three‐dimensional polar code (3D‐PC) scheme is proposed to improve the error floor performance of parallel concatenated systematic polar code (PCSPC). The proposed 3D‐PC is constructed by serially concatenating the PCSPC with a rate‐1 third dimension, where only a fraction λ of parity bits of PCSPC are extracted to participate in the subsequent encoding. It takes full advantage of the characteristics of parallel concatenation and serial concatenation. In addition, the convergence behavior of 3D‐PC is analyzed by the extrinsic information transfer (EXIT) chart. The convergence loss between PCSPC (λ = 0) and different λ provides the reference for choosing the value of λ for 3D‐PC. Finally, the simulation results confirm that the proposed 3D‐PC scheme lowers the error floor.

Wireless traffic prediction can effectively reduce the uncertainty in network demand and supply, and thus is a key enabler of smart management in next-generation wireless networks. To the best of our knowledge, this paper is the first to establish a wireless traffic prediction model by applying the Gaussian Process (GP) method based on real 4G traffic data. Our work is two-fold: First, based on the observed wireless traffic patterns, the kernel in our proposed GP model is designed accordingly to capture both the periodic trend and dynamic deviations; second, by leveraging the Toeplitz structure in the covariance matrix, the computational complexity of hyperparameter learning is significantly reduced from O(n3) to O(n2) and that of inference is reduced from O(n3) to O(n \log n), without any loss of prediction accuracy. Experimental results show that the proposed GP model can attain up to 97% prediction accuracy, and outperform the state-of-the-art algorithms considerably.

Polar coded modulation (PCM) is one of the most promising approaches towards high spectral efficiency. It is able to provide excellent performance comparing to the conventional turbo coded modulation schemes. In this paper, we consider the design of the PCM schemes with high modulation order, e.g., 256QAM or 1024QAM etc. Different with the legacy PCM of low modulation order, the coded bits in high-order PCM with all-zero frozen-sequence will demonstrate obvious correlations which lead to catastrophic performance loss. In order to evaluate this loss, we introduce a quantitative metric, named the bit conditional mutual information (BCMI), to analyze the correlation. Then a new PCM scheme based on the constrained frozen-sequence is proposed to eliminate the correlations among the coded bits. Theoretical analysis and simulation results show that the proposed scheme can ensure the performance of PCM and significantly outperform that of the turbo coded modulation schemes.

In this paper, we study the throughput performance of LTE-licensed-assisted access (LAA) networks coexisting with wireless local area networks (WLAN) in the presence of imperfect spectrum sensing. By considering the false-alarm probability and miss-detection probability of widely-used energy detection, we analyze the potential impact of imperfect spectrum sensing on the access performance of unsaturated LTE-LAA networks along with binary slotted exponential backoff. The access probabilities of LTE-LAA networks and WLAN systems are derived based on the discrete-time Markov chain (DTMC) model. Furthermore, the throughput of LTE-LAA networks is maximized by jointly optimizing the sensing duration and threshold. Numerical results confirm the great impact of imperfect spectrum sensing on the LTE-LAA system throughput, and indicate the optimized sensing duration and threshold can achieve a significant performance gain compared to the fixed ones.

In this paper, the non-orthogonal multiple access technology is integrated into cognitive orthogonal frequency division multiplexing (OFDM) systems, referred to as NOMA-OFDM, to boost the system capacity as well as the number of accessible users. The considered problem is formulated as jointly optimizing the sensing duration, user selection, and power allocation under the constraints of maximum transmitted power and maximum allowable interference. In order to overcome the non- convexity, we decompose the formulated problem into three subproblems, i.e., the sensing duration optimization, user selection optimization and power allocation optimization. By exploiting the individual characteristic of each subproblem, three efficient algorithms, i.e., bisection search method, matching-theory-based user selection and difference of convex (DC) programming, are proposed to solve the corresponding subproblems, respectively. Moreover, an alternate iteration algorithm is also provided to perform joint optimization of three subproblems. Simulation results validate the fast convergence and considerable performance gain of the proposed algorithms.

This paper studies the spectrum efficiency (SE) maximization problem for cooperative multi-antenna power beacon (PB)-enabled wireless powered communication networks (WPCN), where each transmitter harvests energy from surrounding PBs and then transmits data to the corresponding receiver within its allocated duration. The considered problem is formulated as jointly optimizing the energy beamforming vectors of PBs, the transmission duration, and the transmit power of users to maximize the total SE. In order to derive an efficient algorithm, the SE maximization problem is decomposed into two subproblems: the SE maximization problem for data transmission and the energy consumption minimization problem for energy transfer.We prove that the optimal SE is concave with the harvested energy of each user, and based on this concavity, an efficient algorithm is proposed to achieve the optimal solution. Finally, simulation results validate the optimality of the proposed algorithm, and verify the superiority of the proposed scheme-more than 150% performance gain is obtained, compared with the scheme of single PB with single antenna.

Massive multiple input multiple output (MIMO) is a promising technology that can enhance the wireless communication capacity due to increased degrees of freedom. To fully utilize the spatial multiplexing gains of massive MIMO, accurate channel state information (CSI) is required for coherent detection. Due to the overwhelming pilot overhead of conventional CSI estimation methods, compressed sensing technology is adopted as an effective method to reduce pilot overhead. In this paper, we consider the channel estimation problem in FDD multi-user massive MIMO systems. By exploiting the block sparsity of channel matrices in virtual angular domain among different users, we propose a joint block orthogonal matching pursuit (JBOMP) algorithm to estimate CSI at the base station. The performance of JBOMP is evaluated by simulation, which shows the advantages over existing algorithms.

In this study, zero‐forcing matrix decomposition polynomial expansion update (ZF‐MDPE‐update) and zero‐forcing successive over relaxation update (ZF‐SOR‐update) algorithms are proposed to update a zero‐forcing detector quickly without requiring complicated matrix inversion recomputations when massive multiple‐input multi‐output systems channel estimates contain a small perturbation. To further accelerate the convergence rate and to maximise date throughput, a new method of calculating the optimal coefficients for the matrix polynomial that can significantly improve the accuracy of the initial input inverse matrix approximation is considered by the ZF‐MDPE‐update algorithm. On the other hand, the ZF‐SOR‐update algorithm with an optimal iterative initial solution and an optimal relaxation parameter is devised, which achieves excellent detection performance. Results demonstrate that when the ratio of base station (BS) antennas to user terminal (UT) antennas, , is small, the proposed update detection algorithms, with only a few operations, achieve a significant improvement in the average achievable rate of the UTs compared to the recently proposed update algorithm. Therefore, more UTs can be served in a cell with a fixed number of BS antennas. At the same time, the authors' algorithms are shown to facilitate easy memory transfer and are low cost.

In this paper, an energy-incentive cooperative transmission (EICT) scheme is proposed for wireless ad hoc networks, where a node uses energy as reward to seek for cooperative transmission from neighboring nodes and then the cooperative node adopts a decode-and-forward (DF) protocol to relay data. An optimal time slot and power allocation algorithm is proposed to maximize the sum-rate under the constraints of peak power, energy consumption, and individual data rate when the channel state information (CSI) is perfectly known at transmitters. Furthermore, the scenario that only the statistical CSI is available at transmitters is investigated, and an alternative algorithm is presented to optimize time slot and power allocation. Simulation results confirm the superiority of the proposed scheme over existing ones, demonstrating a more effective mechanism to stimulate cooperation in wireless ad hoc networks.

In this paper, polar codes are first applied in non-orthogonal multiple access (NOMA) and the channel polarization idea is extended to NOMA, which is a major multiple access technique in 5G systems. The polar coded NOMA (PC-NOMA) scheme is proposed, whereby the NOMA channel is decomposed into a series of binary-input channels under a two-stage channel polarization transform. In the first stage, the NOMA channel is divided into a group of user synthesized channels by using the multi-level coding structure. In the second stage, based on the structure of bit-interleaved code modulation, user synthesized channels are further decomposed into binary polarized channels. Then, a joint successive cancellation decoding scheme is given to construct the multiuser receiver of PC-NOMA. Finally, a low complexity search algorithm is proposed to schedule the NOMA decoding order which improves the error performance by enhanced polarization among user synthesized channels. The block error ratio performances over additive white Gaussian noise channels indicate that the proposed PC-NOMA obviously outperforms the turbo coded NOMA scheme due to the advantages of the two-stage polarization.

In this paper, we investigate power allocation for simultaneous wireless information-and-energy multicast in cognitive OFDM systems. Our objective is to maximize the energy efficiency (EE) subject to the maximum power constraint at cognitive base station (CBS), maximum receiver interference constraint at each primary user (PU) and minimum harvested energy constraint at each energy receiver (ER). Due to the non-convexity of objective function, fractional programming is adopted to transform the nonconvex problem to a convex one. However, the complexity of the traditional optimization method, i.e., interior point method, is still too high to solve the transformed problem. To this end, a bisection-search-based suboptimal algorithm is proposed. Simulation results show that the proposed algorithm can greatly reduce the complexity (up to 1/12 at most) at the cost of tiny performance loss (less than 2%) compared with traditional convex optimization algorithms.

Compressed sensing (CS) has attracted a lot of research interests in data transmission, since it can significantly reduce the redundancy of the data while still holding the information completely. Generally, in order to deal with the experienced time-varying channel, the data transmission scheme based on CS needs to estimate the channel frequently. In this paper, we propose a novel CS-based data transmission scheme for slowly time-varying channel, which estimates channel and reconstructs data jointly. Specifically, the scheme consists of a data reconstruction part and a channel state information (CSI) update part. The former reconstructs the data with inaccurate CSI by modeling the problem as a perturbed CS reconstruction problem. The latter updates the CSI with the reconstruction result as a semi-blind channel estimation problem. Comparing with the classical scheme, the proposed one enjoys a reduced cost by avoiding inserting pilots into data frame frequently. Simulation results demonstrate that our proposed scheme works robustly in slowly time-varying channel and the performance is comparable to that of the scheme reconstructing the data with perfect CSI.

Compressive sensing (CS) is an emerging theory in that it is possible to reconstruct sparse signals from far fewer measurements than traditional methods use. Existing studies utilizing CS in the field of cognitive radio network (CRN) mainly focus on spectrum sensing in interweave mode. However, few works concern about the application of CS in overlay CRNs. In this paper, we study the overlay CRN, which applies CS technology as a joint source-channel code. The secondary user (SU) not only sends its own message, but also employs decode-and-forward relaying strategy to help with primary transmission, where the primary message is obtained in a causal manner. Dirty paper coding is used to pre-cancel the interference of the primary message at the secondary receiver. We discuss the coding schemes when one SU and two SUs are in the CRN. To either case, we formulate the corresponding system optimization problem, which maximizes the secondary rate while satisfying the primary rate requirement. The performance of the proposed scheme is evaluated by numerical simulations and compared with the nonoptimal causal scheme and the non-causal scheme.

In this paper, we consider an overlay cognitive radio network that transmits discrete-time analog sources over additive white Gaussian noise channels. Our focus is on the case where the primary and secondary sources are correlated. The secondary user (SU) knows the primary message non-causally, and allocates part of its power for transmitting the primary message. We study a hybrid digital-analog coding scheme for the SU, which is the superposition of two analog parts of the sources and a digital part of the secondary source. This coding scheme not only exploits the correlation between the sources, but also helps with the primary transmission. We derive the signal-to-noise ratio (SNR) of both PU and SU, and formulate the optimization problem as achieving maximum SNR of the SU while protecting the PU's SNR from being affected. The simulation results are shown to be consistent with our derivation.

This paper investigates an energy efficiency optimization problem in cognitive orthogonal frequency division multiplexing systems. The goal is to maximize the energy efficiency by adapting the sensing duration, detection threshold, and transmit power to the constraints of the energy consumption of the secondary network and the interference to the primary network in a statistical manner. First, the case of identical detection threshold for all subcarriers is considered. In order to circumvent the intractability of the resulting problem, an alternate iteration framework is proposed to iteratively solve the three decoupled subproblems: sensing duration optimization, detection threshold optimization, and power allocation optimization. By exploiting the characteristics of each subproblem, the proposed framework is proved to be convergent. Then, the case with individual detection threshold for each subcarrier is explored. By proving that the optimal detection threshold is the root of a quadratic equation with one unknown variable, the proposed framework can be applied with minor modification. Simulation results show that the proposed alternating optimization framework can approach rapidly to the optimal solution, with less than 1% gap. Compared with the existing schemes, both the cases with identical and individual detection thresholds can achieve a considerable energy efficiency gain, with the latter further outperforming the former.

In this paper, we analyze the outage probability of a three-node decode-and-forward (DF) relay network, where the relay adopts a power-splitting protocol to harvest energy from the received signal, and utilizes the harvested energy to forward information. First of all, the theoretical expression of outage probability is derived, and then the closed forms of the optimal amount of harvested energy and the best relay location are achieved analytically in order to minimize the outage probability. Furthermore, the condition that the outage performance with energy harvesting (EH) surpasses that without EH is also deduced to figure out when EH is indispensable to relay enhancements. Numerical results verify the correctness of our theoretical derivation, and validate that there exist the optimal amount of harvested energy and the best relay location to reach the minimum outage probability in the energy harvesting relay network. The work of this paper will provide valuable insights into the effect of harvested energy and relay location on outage probability, and be instrumental in how to deploy relays with RF energy harvesting functions.

This paper considers a cognitive network that comprises one multi-input multi-output (MIMO) primary link and one MEMO secondary link for the underlay and overlay modes. In the underlay mode, instead of using water-filling algorithm to maximize the primary rate selfishly, we propose adaptive number of eigenmodes beamforming (ANEB) algorithm for the primary user (PU), which can adjust the number of PU's eigenmodes to meet its rate requirement The secondary user (SU) coexists with the PU using interference alignment (IA) scheme. In the overlay mode, the secondary transmitter has non-causal knowledge of the primary message and helps with the primary transmission. We combine IA algorithm with power allocation scheme to maximize the SU's rate as well as meet the PU's rate requirement Finally, numerical simulation results show that our scheme is win-win for both the PU and SU.

In this paper, we focus on whether and how the quality-of-service (QoS) requirements can be guaranteed in radio frequency (RF)-harvesting-powered cognitive orthogonal frequency division multiplexing (OFDM) relay systems, where the available energy is extremely limited by power transfer capability, and the available spectrum is greatly restricted by primary networks. Thus, the joint power splitting ratio, subcarrier assignment and power allocation problem is formulated under the interference constraints and QoS requirements. In addition, in order to maximize the capacity of the secondary network, an optimal power splitting and resource allocation scheme is proposed based on dual decomposition as well as time sharing conditions. Simulation results show that the proposed scheme can perfectly satisfy the QoS requirements in spite of limited energy and spectrum, and outperforms conventional methods in terms of capacity and QoS satisfaction.

The theory of compressed sensing (CS) is very attractive in that it is possible to reconstruct sparse signals with a sub-Nyquist sampling rate. Recently many researches have applied CS as the channel code, and show its great potential in communication systems. This paper studies the compressed cooperation in an amplify-and-forward (CC-AF) relay channel, where CS is used to compress the source data and AF relay is used to forward the received signals. When different deployments of measurement matrices are considered, we design different decoding strategies and analyze the corresponding achievable rates. With the derived rates, numerical calculations show that CC-AF outperforms the direct transmission without relay. In addition, the performance of CC-AF and the existing compressed cooperation with decode-and-forward relay is also compared.

In this paper, we investigate resource allocation in OFDM based CRNs with cyclic prefix power transfer (CPPT). In this system, the secondary receiver (SR) can extract power from the cyclic prefix (CP) of the received signal and use the harvested energy for its own energy supply. Our objective is to find the optimal power allocation and CP size that maximize the energy efficiency (EE) of the secondary system, under the maximum primary user (PU) interference and the minimum harvested energy constraints. As the CP size will impact the interference constraints, the relationship between optimal power allocation and CP size is hard to be expressed by a function. In this paper, we solve the problem by two steps. First, we propose an efficient iterative algorithm to achieve the optimal power allocation for the EE maximization problem with a certain CP size, and then find the optimal CP size through a full-search method. The influence caused by the CP size on power allocation is illustrated by simulations and numerical results prove CPPT can improve the system utilization greatly compared with traditional methods.

Nowadays energy efficiency (EE) of wireless communication systems has become a hot issue, yet the nonlinear effect and inefficiency of power amplifier (PA) have posed practical challenges for system designs to achieve high EE. However, most of previous work only considered linear PA. In this paper, we studies EE optimization concerning with I-way Doherty PA which has widespread use with I = 1 and I = 2 in orthogonal frequency division multiplex (OFDM)-based cognitive radio (CR) systems. The aim is to maximize EE with nonlinear PA subject to the total power budget, the interference constraint and the minimum rate requirement. Other than traditional methods, the problem is hard to solve directly due to the nonlinearity of PA, and a bisection search method tailored for nonlinear PA is adopted to achieve the sub-optimal solution. Numerical results demonstrate that if the PA's nonlinearity is not considered, the EE performance of OFDM-based CR systems can be severely overestimated by 34% at P max out = 80 W for 1-way Doherty PA. Meanwhile, the EE performance can be enhanced by approximate 32% if 2-way Doherty PA is utilized instead of 1-way Doherty PA.

Analog-to-information conversion (AIC) is an efficient way to obtain the compressed samples directly from an analog signal. Modulated wideband converter (MWC) proposed by M. Mishali and Y. C. Elda is a successful hardware architecture of AIC that measures the analog signal by K branches of mixers and integrators (BMI). However, MWC has high implementation complexity owing to the fact that its measurement matrix is dense. To reduce the complexity of MWC, in this paper we propose the diagonal modulated wideband converter (D-MWC), where each BMI only works within a partial time period that is non-overlapping instead of each BMI working all the time in MWC. The measurement matrix of D-MWC is sparse that can significantly simplify the sampling structure. Simulations verify the effectiveness of D-MWC. The proposed D-MWC offers a good tradeoff between complexity and sampling performance.

Unlicensed spectrum, around 5 GHz, will be introduced to Long Term Evolution (LTE) systems, referred to as LTE-Unlicensed (LTE-U), to combat the explosive growth of traffic volume in next 10 years. In this paper, the interference-controlled power allocation problem is studied for LTE-U systems, which can be inherently classified as orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems, where optimal power allocation algorithms are currently available by resorting to computation-intensively numerical iterations with a moderate risk of divergence. In order to satisfy the rigorous algorithm requirements, i.e., convergent outputs of power allocation and running time of milliseconds, for practical LTE-U deployments, a cap-limited waterfilling method is proposed to regulate the interference to primary users one by one successively, by which not only a near-optimal solution can be obtained, but also the intractable iteration divergence and computation complexity issues can be excluded completely. Simulation results indicate the capacity performance of the proposed low-complexity method approaches to the optimal solution with a slight loss less than 5%, and is remarkably superior to the existing suboptimal methods.

This paper investigates the energy-efficient power loading with intercarrier and intersymbol interference considerations for OFDM-based cognitive systems. The objective is to maximize the energy efficiency (EE) as well as to balance the tradeoff between intercarrier interference (ICI) and intersymbol interference (ISI) by jointly optimizing the subcarrier bandwidth and power allocation in a mobile scenario, under the power budget and the interference constraint. First, the primal problem is converted into a convex optimization problem by fractional programming. Then, the Lagrange dual function and the sub-gradient method are adopted to achieve the optimal power allocation and the golden section method is employed to search for the optimal subcarrier number (i.e, subcarrier bandwidth). Numerical results show that the proposed algorithm can realize ICI control by choosing an optimal subcarrier number, and simultaneously the EE can be significantly improved by 139%.

This paper investigates the achievable rate of cognitive radio network when one primary user (PU) and one secondary user (SU) are present, where there is partial channel state information at the transmitter (CSIT). On one hand, SU learns primary message causally in the first phase and then relays it with decode-and-forward strategy in the second phase. On the other hand, SU employs linear assignment Gel'fand-Pinsker coding (LA-GPC) on its own message to cancel the interference from the primary transmitter. We formulate an optimization problem to maximize SU's achievable rate while keeping PU's rate unaffected. An algorithm is proposed to jointly determine the fraction of channel uses in the first phase, the percentage of SU's transmit power used for relaying, and the filter factors in LA-GPC method. The performance of the proposed algorithm is evaluated through numerical simulations.

In this letter, cooperative multicast in cognitive relay systems is investigated, where multiple multi-antenna relay nodes help forward the transmitted message from different sources to the corresponding destinations. Our objective is to maximize the global transmission rate scaling factor by cooperatively optimizing the forwarding matrix for each relay node. To solve the problem effectively, we first prove that the optimal forwarding matrix at each relay node is the combination of a multi-stream matched-filter receiver and a multi-stream adaptive beamformer, and then demonstrate that the beamformer design problem can be further transformed into the cognitive multi-group multicast beamforming problem. Finally, a bisection-based algorithm is proposed to find the optimal beamforming vector. Compared to the existing cooperative multicast schemes, the proposed scheme can achieve higher transmission rate and provide better protection for primary nodes.

Simultaneous wireless information and power transfer (SWIPT), which processes information and scavenges energy from the same ambient radio frequency signals, has recently drawn significant attention. In addition, the integration of cognitive radio and two-way relay transmission has emerged as a promising approach for improving spectral efficiency. In this paper, we consider a cognitive two-way relaying network, where the SWIPT-enabled-relay helps two secondary nodes exchange information with the energy harvested from the received signals. The rate maximization problem for the relaying network is formulated under the constraint that interference to the primary user resulting from the transmission of the secondary relaying network cannot exceed the threshold. Based on the mathematical analysis of the problem, we propose a suboptimal joint relay selection and power allocation scheme with the Bisection Search method. Numerical simulations confirm the near-optimality of the proposed scheme by comparing with the optimal solution. Specially, the best two-way relay location is also revealed in simulations.

In this paper, we propose a dirty paper coding (DPC) scheme for the cognitive radio channel where multiple cognitive users coexist with a primary user. The cognitive transmitter is assumed to know the primary signals non-causally and is equipped with two antennas, of which each transmits to a single-antenna receiver. In our proposal, zero-forcing DPC is employed to cancel the interference not only from the primary user but also from other cognitive users. The transmit power constraint is satisfied by the application of the multiuser trellis shaping. The optimal values of the power allocation parameter and the scaling factor are derived. The performance of this scheme is compared using simulations with that employing conventional trellis shaping.

In this paper, we propose a dirty paper coding (DPC) scheme that uses trellis shaping for the overlay cognitive radio channel, where a cognitive user and a primary user transmit concurrently in the same spectrum. Interference of the primary user is assumed to be known at the cognitive transmitter non-causally. Based on this knowledge, the shaping code selection, as a key feature of the proposal, is introduced which enables the constellation to be self adaptively changed. The performance of our proposed scheme is compared using simulations with that based on the conventional trellis shaping and it achieves excellent tradeoff between performance and complexity.

We propose a parallel and distributed algorithm for the problem of beamformer design and power allocation in multi-cell massive multiple-input multiple-output (MIMO) networks. Inter-cell coordination and overlapping clusters are considered to enhance the system performance. To overcome the tight coupling induced by inter-cell coordination and overlapping clusters, we formulate the system into a two-layered framework. In this framework, the problem is decomposed into independent subproblems which are naturally solvable in a parallel and distributed manner. For each subproblem, an efficient parallel algorithm is proposed to speed up the convergence rate. The simulation results show that our inter-cell coordination strategy can approach the performance achieved by a high-quality limited inter-cell coordination strategy at low signal-to-noise ratio (SNR), and outperform it at high SNR. Moreover, the proposed parallel algorithm enjoys great parallelization speedup with moderate transmit power consumption.

In this paper, a cognitive radio network with multiple licensed users is considered. A dirty paper coding (DPC) based cooperation scheme for the Gaussian multiple-in-multiple-out (MIMO) cognitive radio network (CRN) with partial transmitter side information is studied. The problem of maximizing the sum-rate of MIMO CRN with multiple primary users over the transmitter covariance matrices, which is formulated as an optimization problem, is dealt with. Such an optimization, which needs to be performed jointly with the inflator factors under the DPC-based strategy, is solved by an iterative suboptimal solutions. Simulation results show that the proposed scheme is able to greatly improve the achievable rate performance in CRN.

In this paper, the exact closed-form outage probability expression is derived for cognitive relay network with incremental decode-and-forward (IDF) protocol in independent non-identically distributed (i.n.i.d.) Nakagami-m fading channels. The outage performance comparisons are made between IDF and DF protocols. Besides, the impact of channel fading parameters is investigated for both secondary transmission links and interference links. The results show that a significant gain can be made by using IDF protocol, especially when the secondary direct link is in good channel condition. Moreover, the outage performance is dominated by the channel quality of the secondary transmission links and is also impacted by the channel quality of the interference links.

Prior information is easily obtained in many applications of compressed sensing. This paper considers the sparse signal recovery using certain types of prior information. Our major contribution is proposing two novel reconstruction algorithms with prior information named as logit weight simultaneous orthogonal matching pursuit (LW-SOMP) and logit weight simultaneous orthogonal matching pursuit with amplitude information (LW-SOMP-A) for joint sparsity model of distributed compressed sensing. Simulation results demonstrate improved performance of the proposed algorithms (with respect to the conventional algorithm).

This paper investigates the achievable rate of MIMO cognitive radio network when one primary user (PU) and multiple secondary users (SU) are present, where the latter adopt dirty paper coding (DPC) to cancel the interference of PU's transmission at their receivers. Perfect channel state information is assumed at receivers, while only the statistic information of channels are known at the transmitters. We formulate an optimization problem to maximize the achievable rate of the system under the constraints of power limits of each transmitter, where the requirement of not affecting PU's transmission rate is also incorporated. An algorithm is proposed to jointly determine the inflation factors in DPC method and the input covariance matrix of each SU. Simulations show that the proposed problem achieves better achievable rate when compared with the existing results without compromising PU's transmission rate.

A hybrid automatic repeat request scheme with Chase combing (HARQ-CC) of polar codes is proposed. The existing analysis tools of the underlying rate-compatible punctured polar (RCPP) codes for additive white Gaussian noise (AWGN) channels are extended to Rayleigh fading channels. Then, an approximation bound of the throughput efficiency for the polar coded HARQ-CC scheme is derived. Utilizing this bound, the parameter configurations of the proposed scheme can be optimized. Simulation results show that, the proposed HARQ-CC scheme under a low-complexity SC decoding is only about 1.0dB away from the existing schemes with incremental redundancy (HARQ-IR). Compared with the polar coded HARQ-IR scheme, the proposed HARQ-CC scheme requires less retransmissions and has the advantage of good compatibility to other communication techniques.

Distributed beamforming (DBF) is a cooperative technology that several nodes transmit a common message to a receiver efficiently. The key to DBF is to ensure every node's signal adds coherently at the receiver by synchronizing node's carrier phase. In this paper, we present a closed-loop deterministic phase synchronization algorithm in order to pursue less synchronization time, less feedback information, less energy consumption. We analyze the performance of this algorithm and make a comparison between the new and the previous closed-loop phase synchronization algorithms.

Recent advances in network coding have shown great potential for efficient information transfer. In this paper, exploiting inter-layer and inter-session network coding, we address the distributed control problem in heterogeneous-service networks (HSNs) with booming multi-rate multicast (MRM) and unicast (UC) services. Different from the literatures on inter-layer/inter-session schemes, heterogeneity and fairness among MRM users and between different services are jointly considered. With the Lagrangian and subgradient method, a decentralized rate control algorithm is developed with little coordination among intermediate nodes, in which only local information is needed to achieve rate, congestion, and fairness balance control. Numerical examples are provided to verify the effectiveness and convergence of the proposed algorithm. Furthermore, we demonstrate the performance improvement and implementation advantages of the proposed algorithm compared with the previous solutions considering layered coding for an MRM service or inter-session coding limited for UC services.

We consider a spectrum sharing protocol in which a secondary system can operate on the same spectrum with a primary user. In the protocol, the secondary system helps the primary system achieve its target rate by acting as a relay for the primary system. As a reward, the remaining subcarriers can be used for the secondary transmission. In order to alleviate the disadvantages of amplify-and-forward (AF) and decode-and-forward (DF) relaying techniques, an adaptive relaying scheme is proposed. In this paper, we study energy efficient resource allocation for the cognitive cooperation system. The considered problem is modeled as a non-convex optimization problem which takes into account the total transmit power of the secondary system and the minimum required data rate of the primary system. The optimal set of subcarriers used for cooperation, subcarrier power allocation and relaying technique are derived for maximization of the energy efficiency (EE) of the secondary system by using fractional programming and dual theory. Simulation results demonstrate that significant performance gains can be achieved by the devised scheme.

This paper investigates an energy-efficient transmission scheme in cognitive radio networks, where primary users (PUs) may reoccupy the spectrum when secondary users (SUs) is transmitting data. We aim to maximize the energy efficiency by jointly optimizing the transmission power, the fusion rule threshold and the sensing/transmission durations. Firstly, it is derived that for a given fusion rule threshold, the objective function is unimodal while only one optimization parameter varies. Furthermore, we provide the corresponding closed-form expressions of the optimal data transmission duration and transmission power. Finally, the globally unimodal property is proven, and hence, the globally optimal point can be easily found with the proposed algorithm based on the alternating direction method (ADM). Numerical simulation results show that our proposed scheme is much better than the existing ones.

While a cognitive radio network sharing the spectrum with a primary network, one of the major concerns is that harmful interference to the primary users should be avoided. In this paper, we propose three low-complexity algorithms for distributed beamforming in cognitive radio networks which opportunistically select a subset of cognitive source nodes whose phases may be compensated, so that the received signals in cognitive destination can combine in a quasi-coherent manner and the interference to the primary users is not destructive. Only a few bits feedback from cognitive destination is required per cognitive source node. Simulation results demonstrate the good performance of the proposed algorithms.

Recent work in the area of compressed sensing mainly focuses on the perfect recovery of the entire support for sparse signals. However, partial support recovery, where a part of the signal support is correctly recovered, may be adequate in many practical scenarios. In this study, in the high‐dimensional and noisy setting, the authors develop the probability of partial support recovery of the optimal maximum‐likelihood (ML) algorithm. When a large part of the support is available, the asymptotic mean‐square‐error (MSE) of the reconstructed signal is further developed. The simulation results characterise the asymptotic performance of the ML algorithm for partial support recovery, and show that there exists a signal‐to‐noise ratio (SNR) threshold, beyond which the increase of SNR cannot bring any obvious MSE gain.

This letter investigates the throughput optimization of multiple description coding multicast (MDCM) in cognitive radio networks (CRNs) by taking into account both statistical channel state information (CSI) and the interference from the primary network. A statistical-CSI-based MDCM scheme with low complexity is proposed, which is shown to be able to approach the perfect-CSI performance with large multicast group size. For comparison, conventional multicast (CM) is also analyzed. A tight upper bound is derived, which decays to zero rapidly when the multicast group size grows. Numerical results are also presented to validate the proposed scheme.

CRC (cyclic redundancy check) concatenated polar codes are superior to the turbo codes under the successive cancellation list (SCL) or successive cancellation stack (SCS) decoding algorithms. But the code length of polar codes is limited to the power of two. In this paper, a family of rate-compatible punctured polar (RCPP) codes is proposed to satisfy the construction with arbitrary code length. We propose a simple quasi-uniform puncturing algorithm to generate the puncturing table. And we prove that this method has better row-weight property than that of the random puncturing. Simulation results under the binary input additive white Gaussian noise channels (BI-AWGNs) show that these RCPP codes outperform the performance of turbo codes in WCDMA (Wideband Code Division Multiple Access) or LTE (Long Term Evolution) wireless communication systems in the large range of code lengths. Especially, the RCPP code with CRC-aided SCL/SCS algorithm can provide over 0.7dB performance gain at the block error rate (BLER) of 10-4with short code length M = 512 and code rate R = 0.5.

A new bit-interleaved polar coded modulation (BIPCM) scheme is proposed. To reduce the construction complexity, the simplest kernel matrix for channel polarization is adopted. And auxiliary virtual channels with zero-capacities are introduced to adapt different modulation orders. The underlying polar codes are efficiently constructed by calculating the Bhattacharyya parameters of equivalent binary-input erasure channels (BECs) rather than computing-expensive density evolution. Further, to avoid exhaustive searching, an empirically good channel mapping scheme is provided. Compared with the existing BIPCM schemes, the proposed scheme has a lower construction complexity and can achieve a better performance. With cyclic redundancy check (CRC) aided decoding algorithms, the block error rate (BLER) performance of our proposed BIPCM scheme can outperform the turbo coded modulation scheme used in WCDMA (Wideband Code Division Multiple Access) wireless communication system by up to 0.5dB.

This paper investigates the energy efficient resource allocation strategy for OFDM-based cognitive radio (CR) networks with imperfect spectrum sensing. The interference model taking the sensing errors into account is formulated at first. And the objective is to maximize the energy efficiency of the multiuser CR system subject to the total transmission power budget and each primary user's (PU) interference constraints. As the primal problem is a mixed integer nonlinear programming issue, we will separate the resource allocation scheme into two steps, i.e., subcarrier assignment and power allocation. After the suboptimal subcarrier assignment, an optimal power allocation algorithm is proposed based on fractional programming and sub-gradient method. The simulation results show that the proposed resource allocation scheme can achieve higher energy efficiency than the one maximizing the capacity of the CR networks. Meanwhile, it can protect the normal communication of each PU compared to the scheme without considering sensing errors.

In this paper, cooperative multicast with the help of short-range data sharing is studied in the cognitive radio networks (CRNs). The original multicast data is split into many segments, and the transmission of each segment is divided into two stages. In the first stage, cognitive base station transmits each segment to the corresponding cooperative user, and in the second stage, the cooperative user decodes the received data and broadcasts it to other multicast users. Based on this transmission model, cooperative multicast is formulated as an optimization problem with the aim of maximizing the total transmission rate. Afterwards, we first propose a cooperative user selection strategy, which is meaningful when the multicast size is large, and then implement the resource allocation with dual translation and subgradient updating. The simulation results show that the proposed cooperative multicast scheme can achieve much higher spectrum efficiency than both conventional multicast scheme and multiple description coding multicast scheme.

This paper proposes an energy-efficient multicast scheme for downlink orthogonal frequency division multiplexing (OFDM) system in which the base station (BS) is equipped with multiple antennas. We employ the multiple description coding multicast (MDCM) model and beamforming technique to maximize the energy efficiency (EE) with the constraint on total transmit power. In MDCM, the transmission rate is not limited by the user with the minimum channel quality any more. And the beamforming technique can enhance the signal strength of the weakest user. In this paper, a two-step suboptimal scheme is studied. Firstly, the beamforming weighted vector (BWV) is obtained by a vector splitting algorithm, and then the power and subcarrier allocation is realized by fractional programming and subgradient method. Numerical results reveal that the proposed scheme can achieve near optimal EE and greatly improve the EE compared with two spectrum-efficient schemes. In addition, the energy-efficient scheme can render a good performance in EE as well as throughput when the total transmit power is small.

Polar codes are the first constructive and provable capacity-achieving codes. In finite code length cases, successive cancellation list (SCL) decoding algorithm is reported to have performance very close to maximum-likelihood (ML) decoding. In this paper, a reduced-complexity version of SCL decoding algorithm is proposed to boost the finite- length performance of polar codes. By regarding the SCL decoding algorithm as a path searching procedure in a code tree representation, a tree-pruning technique is used to avoid unnecessary path searching operations. With only a negligible loss of performance, the computational complexity of pruned SCL decoder can be very close to that of the successive cancellation (SC) decoder in the moderate and high signal-to- noise ratio (SNR) regime.

The transmission capacity (TC) of the secondary (SR) network in cognitive radio networks (CRNs) is investigated in this paper. Mathematically, the TC of the SR network is defined as the maximum transmitter density of the SR network that satisfies the outage probability constraints of the primary (PR) network and the SR network, multiplied by the communication rate and the successful reception probability. In order to effectively control interference in CRNs to obtain a higher TC, we propose a spectrum sharing scheme with preservation regions to avoid excessive interference from the secondary users to the primary users. Closed-form expression for the TC of the SR network is derived under such a scheme. We optimize the transmitter density, the transmit power and the preservation range, which maximize the TC of the SR network. Numerical results show that our scheme outperforms traditional schemes.

With the continuous growth of the wireless communication business, energy issues and environmental problems are becoming increasingly grim. Therefore, this paper investigates the energy efficient transmission scheme with cooperative sensing in cognitive radio networks, in which AND fusion rule is introduced to determine the presence of the primary user. It is proved that the energy efficiency is a quasi-concave function with sensing time when the number of cooperative users satisfies certain constraints. Aiming at maximizing the energy efficiency, the transmission power is selected at first, then a scheme of jointly optimizing sensing time, energy detector threshold and the number of cooperative users is proposed based on the related theory analysis. From the simulations, it can be found that the optimal sensing time is only about half of that consumed in single user sensing, and the proposed scheme has significant improvement in energy efficiency.

The transport capacity of cognitive radio ad hoc networks is investigated in this paper. Compared with the related works in the literature which mainly give order sense results like scaling laws, we derive an expression of the single-hop transport capacity for the secondary (SR) network under the outage constraint of the primary (PR) network. The Aloha medium access control (MAC) protocol and the nearest neighbor routing protocol are considered. Through stochastic geometric analysis, it is shown that the outage probability of the PR network and the transport capacity of the SR network are mainly dominated by the node density of the SR network and the medium access probability p of the MAC protocol. Besides, the value p is optimized to maximize the transport capacity of the SR network.

In this paper, resource allocation problem with imperfect channel information in cognitive radio networks (CRNs) is studied concerning the multiple description coding (MDC) multicast transmission. The traditional unicast model is extended to MDC multicast in CRNs, which aims to maximize the total received rate of all cognitive radio (CR) users. Primarily, a new auxiliary variable, named as normalized channel power gain in this paper, is introduced to substitute the transmission rate as the optimization variable. Then a two-stage method is proposed to conduct the resource allocation: first the multicast group (MG) selection and the normalized channel power gain setting, second the optimal power allocation. Meanwhile, as only estimated channel gain for the interference channel gain is obtained, the primary users' interference can not be accurately estimated when we carry out the power allocation. Consequently, we suitably enlarge the estimated channel gain for the purpose of interference control. It has been verified in the simulation results that the proposed scheme can improve the system performance apparently in terms of both throughput maximization and interference control.

In this paper, we introduce multiple description coding multicast (MDCM) into orthogonal frequency division multiplexing based (OFDM-based) multi-cell cognitive radio networks (CRNs) and investigate the resource allocation problem aiming to maximize the weighted sum rate (WSR). An efficient distributed scheme including subcarrier assignment and power allocation is proposed. During subcarrier assignment, each cell heuristically selects the multicast group (MG) and the associated set of scheduled cognitive radio users (CRUs) for each subcarrier. During power allocation, each cell allocates power to subcarriers by a proposed iterative scheme considering pricing to enhance the efficiency of selfish iteration. The distributed scheme does not require global network information and each cognitive radio (CR) cell performs its own resource allocation through limited interaction with other CR cells. The effectiveness of the proposed scheme is illustrated by extensive simulation results.

This paper investigates the resource allocation problem for multiple description coding (MDC) multicast in OFDM-based cognitive radio (CR) systems, where secondary users (SUs) share the primary spectrum under the interference constraints of primary users (PUs). The previous multicast model is usually based on the full buffer traffic in which there are sufficient data for multicast groups (MGs) to receive. However, it does not consider the nature of limited traffic in practical systems. Taking this case into consideration, saturation rate (SR) is introduced to describe the characteristic of non-full buffer traffic. Aiming at maximizing the weighted sum rate (WSR), a modified resource allocation algorithm based on the Lagrangian dual decomposition is proposed. Simulation results show that the proposed scheme significantly outperforms the conventional multicast. Furthermore, our scheme is meaningful in non-full buffer traffic scenarios, because it can avoid the resource redundancy for users with good channel conditions and the resource starvation for users with bad channel conditions, and hence will reduce the resource wasting.

Channel polarisation results are extended to the case of communications over parallel channels, where the channel state information is known to both the encoder and decoder. Given a set of parallel binary‐input discrete memoryless channels (B‐DMCs), by performing the channel polarising transformation over independent copies of these component channels, we obtain a second set of synthesised binary‐input channels. Similar to the single‐channel case, we prove that as the size of the transformation goes infinity, some of the resulting channels tend to completely noised, and the others tend to noise‐free, where the fraction of the latter approaches the average symmetric capacity of the underlying component channels. For finite‐length polar coding over parallel channels, performance is found to be relied heavily on the specific channel‐mapping scheme. To avoid exhaustive searching, an empirically good scheme that is called equal‐capacity partition channel mapping is proposed and numerical results show that the proposed scheme significantly outperforms random mapping. Further, utilising the above results, a polar coding method for arbitrary code length is proposed, which has potential applications in practical systems.

As improved versions of the successive cancellation (SC) decoding algorithm, the successive cancellation list (SCL) decoding and the successive cancellation stack (SCS) decoding are used to improve the finite-length performance of polar codes. In this paper, unified descriptions of the SC, SCL, and SCS decoding algorithms are given as path search procedures on the code tree of polar codes. Combining the principles of SCL and SCS, a new decoding algorithm called the successive cancellation hybrid (SCH) is proposed. This proposed algorithm can provide a flexible configuration when the time and space complexities are limited. Furthermore, a pruning technique is also proposed to lower the complexity by reducing unnecessary path searching operations. Performance and complexity analysis based on simulations shows that under proper configurations, all the three improved successive cancellation (ISC) decoding algorithms can approach the performance of the maximum likelihood (ML) decoding but with acceptable complexity. With the help of the proposed pruning technique, the time and space complexities of ISC decoders can be significantly reduced and be made very close to those of the SC decoder in the high signal-to-noise ratio regime.

We study a repeated auction for the resource allocation problem in OFDM-based cognitive radio networks (CRNs), in which secondary users (SUs) share the primary spectrum under the interference constraints of primary users (PUs). With the inter-cell interference and mutual interference between PUs and SUs, the resource allocation problem is formulated as a non-convex optimization problem. Auction performs well in solving non-convex problems, therefore the interference auction with cooperative bidding is proposed. Moreover, with the theoretical analysis of equilibrium, an implementation algorithm for the auction is developed and the convergence is proved. Simulation results show that the interference auction obtains a good spectrum efficiency improvement and a rapid convergence rate.

Handover Parameter Optimization (HPO) and Load Balancing (LB) are two Self-Organization network (SON) aspects which aim at improving LTE system handover performance and user's satisfaction respectively. However, there is often counteraction between LB and HPO, because LB would increase the frequency of inter-cell handover and correspondingly increase the possibility of handover problems. Furthermore, most of the LB and HPO jointly optimization methods don't consider the network allowed maximum radio link failure (RLF) ratio, which would increase the possibility of call dropping although the cell loading is balanced. In this paper we introduce the network allowed maximum RLF ratio as a key indicator and a dynamic hysteresis-adjusting (DHA) method to harmonize the two aspects. Furthermore, we take the realistic network situations into account to obtain a more reliable result. The proposed method is evaluated by a series of system-level simulation which witnesses an improvement in handover performance and number of satisfied users in LTE networks.

As a substantial means for improving throughtput, network coding has recently attracted much attention to apply in wireless multicast services. To guarantee the reliable multicast, packet retransmission schemes are employed. With one source, network coding has been well studied for the reliable multicast. But for the multicast cell with two sources, the presented schemes are not good enough to approach an impressive effect with the multiple receivers, demands and link situations. In this paper, for the 2-1-(D1,D2) multicast cell, which has two sources, one relay and two groups users with the receivers numbers of D1and D2, we propose three schemes to reduce the number of retransmission, which combine the lost packets and retransmit them with network coding, and obtains the least number of retransmissions. Some theoretical results are derived on the bandwidth efficiency of the traditional automatic repeat-request scheme (ARQ), network coding with ARQ scheme (XOR-ARQ) and the improved network coding with ARQ scheme (iXOR-ARQ). Compared with ARQ and XOR-ARQ, iXOR-ARQ is more advantageous on the bandwidth efficiency and the coding gain, which has been confirmed by both simulations and theoretical analysis.

This paper investigates the joint design of relay and receive beamforming vectors in cognitive relay networks with the secondary network (SN) using the same frequency band allocated to the primary network (PN). To guarantee the QoS of the primary user (PU), the interference from source and relays in the SN to PU must be lower than what the PU can tolerate. A hybrid relay strategy is adopted by relays that can use amplify-and-forward (AF) or decode-and-forward (DF) strategies to retransmit signal according to signal to interference pulse noise ratio (SINR). The capacity of the source-destination in the SN is maximized with the transmit power and the interference at PU constraints. The simulation results show that the maximum relay transmit power will affect the gain of both the joint beamforming and the hybrid relay strategy, while the maximum interference power at PU will only affect that of the hybrid relay strategy.

In this paper, the estimation of doubly selective channel is considered for amplify-and-forward (AF) relay networks. The complex exponential basis expansion model (CE-BEM) is chosen to describe the time-varying channel, from which the infinite channel parameters are mapped onto finite ones. Since direct estimation of these coefficients encounters high computational complexity and large spectral cost, we develop an efficient estimator targeting at some specially defined channel parameters. The training sequence design that can minimize the channel estimation mean-square error is also proposed.

This paper is concerned with a simplified hard decision feedback equalizer (S-HDFE) for single carrier modulation with cyclic prefix. In this paper, we focus on the coded system in which the feedback symbols are reproduced from hard decisions of channel decoding results. Cholesky decomposition is the key technique for simplifying the feedback filter calculation of S-HDFE. With the same matched filter bound (MFB), the maximum number of the feedback filter taps in S-HDFE is smaller than that in the existing hard decision feedback equalizer with noise prediction (DFE-NP). Therefore, the computational complexity of S-HDFE is lower than DFE-NP with the same MFB. Furthermore, the simulation result shows that if the feedback filter tap number is the same for both equalizers, the BER performance of S-HDFE is better than DFE-NP.

We investigate a decode and forward (DF) scheme of Asymmetric modulation suited for two-way relay (TWR) with network coding. The considering network coding consists of two time-slots: two users transmit wireless signal to the relay in time slot 1, and the processed signal is broadcasted in time-slot 2 by the relay. With the received asymmetric modulated signals by relay in time-slot 1, the judging, coding and power allocating problems are investigated. A judging region (JR) scheme is proposed to solve the issues and the symbol error ratio (SER) is derived. We also give the optimized power allocation method of JR scheme according to different asymmetric modulations. The performance evaluations show correctness of SER expression and efficiency of power allocation method.

Distributed video coding (DVC) has recently been proposed to reduce the complexity of the encoder, whereas it suffers from the sampling cost of huge amount of image data. To relax such sampling burden, this paper develops a novel sub-sampling distributed video coding (SuDVC) by utilizing compressive sensing (CS) technique. Due to the inherent sparsity in video sources, the video frames are compressively sampled at the encoder. On the other hand, by exploiting the correlation between CS measurements and side information and by performing sparsity recovery, the video frames are recovered at the decoder. When compared with the traditional fully-sampling equivalence, SuDVC enjoys the reduction of transmission rate, the reduction of implementation complexity and the robustness to channel losses, which are verified in the simulations.

In this letter, the authors analyze the stability and throughput of a cognitive wireless communication system with cognitive link using same antennas structure as primary link. With the assumption that a possibility of detection error pe will occur due to misty and indetermination of wireless channels, the cognitive link and primary link are interacting queue system in real circumstance. We do stability analysis of an equivalent dominant system to case of interaction queues with Loynes' stability criteria. Based on the studies, we derive throughput and optimal power allocation schemes for two links. The proposed mathematical analysis is complemented by various performance evaluation results, which demonstrates the accuracy of the theoretical approach.

We present the end-to-end performance of dual-hop wireless communication systems with non-regenerative fixed gain relays operating over independent not necessarily identically generalized-K (KG) fading channels. New closed-form expressions are derived for the moments of the end-to-end signal-to-noise ratio (SNR), while the corresponding moment- generating function (MGF) is accurately approximated with the aid of Pade approximants theory. Useful performance criteria are studied; the average end-to-end SNR and the amount of fading, which are expressed in closed form, the average bit-error probability for several coherent, noncoherent, and multilevel modulation schemes, and the outage probability, which are both accurately approximated using the well-known MGF approach. Furthermore, novel closed-form expression is obtained for the gain of semi-blind relays over KGfading channels. The proposed mathematical analysis is complemented by various performance evaluation results, which demonstrate the accuracy of the theoretical approach.

In this work we consider the rate control issue for network coding based multicast among multiple sessions, which can be formulated as a network utility maximization problem. To solve the problem we propose a distributed optimization decomposition approach, which is different from the previous work in the literature in that (1) it is a hierarchical decomposition approach where the primal problem is decomposed recursively, until an independent rate control module is obtained and the decomposed subproblems can be solved by the distributed max-flow algorithm and we emphasize the layered functionality allocation of decomposed subproblems following the framework of "Layering as Optimization Decompositions"; (2) we first separate the primal problem by relaxing the capacity constraint among sessions; (3) to implement end-to-end control, we separate the independent rate control module at end node from the operation in the interior of the network. In this work we intend to propose not only a rate control algorithm but also a possible choice of network architecture for network coding based communication with rate control capability.

End-to-end performance of multihop wireless communication systems with non-regenerative channel state information (CSI)-assisted relays operating over independent not necessarily identically Weibull fading channels is presented. With the aid of the inequality between harmonic and geometric means, the end-to-end signal-to-noise ratio (SNR) is bounded. By considering the product of rational powers of N Weibull random variables, novel expressions are derived for the moment-generating, probability density, and cumulative distribution functions in closed form. Using these results, convenient closed-form bounds are obtained for the average end-to-end SNR, the channel capacity and the average bit-error probability of multihop wireless communication systems. The tightness of the proposed bounds is verified by performing comparisons between numerical evaluation and computer simulations results.

In this paper, we consider the design of constellation labeling for iteratively decoded bit-interleaved space-time coded modulation (BI-STCM-ID) over fast Rayleigh-fading MIMO channels. Based on the largest asymptotic coding gain criterion, we show that, for some known space-time coding scheme, such as threaded algebraic space-time code (TAST), linear dispersion space-time code (LDC), orthogonal space-time block code (OSTBC), V-BLAST, etc., multidimensional constellation labeling design can be reduced to maximizing the power mean of the complete set of squared Euclidean distances associated with all "error-free feedback" events in the constellation.