Performance evaluation of improved double-threshold energy detector over Rayleigh-faded sensing and imperfect reporting channels

Cognitive radio (CR) has been viewed as a promising solution to spectrum scarcity. In order to design a reliable CR system, many improvements have been proposed to enhance spectrum sensing performance of secondary users (SUs) in a CR network (CRN). Sensing reliability and transmission throughput of SUs are two important performance criteria, which should be optimized to enhance signal protection of primary user (PU) as well as spectrum utilization rate. In this paper, we consider Rayleigh-faded sensing channels and SUs use improved energy detector (IED) to make their local decisions. The final decision is made in a fusion center (FC) through the cooperative spectrum sensing (CSS) scheme with erroneous reporting channels. We show that the improved double-threshold energy detector (IDED) outperforms the conventional energy detector (CED) in terms of the total error rate. Furthermore, we evaluate the transmission throughput of the CRN through various ED schemes with detection constraints over both perfect and imperfect reporting channels. We show that the IDED has the highest achievable throughput among different ED schemes over imperfect reporting channels.     

Spectrum efficient power allocation schemes for OFDM cognitive radio with statistical interference constraints

In this paper, we study the power allocation problem for an orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) system. In a departure from the conventional power allocation schemes available in the literature for OFDM-based CR, we propose power allocation schemes that are augmented with spectral shaping. Active interference cancellation (AIC) is an effective spectral shaping technique for OFDM-based systems. Therefore, in particular, we propose AIC-based optimal and suboptimal power allocation schemes that aim to maximize the downlink transmission capacity of an OFDM-based CR system operating opportunistically within the licensed primary users (PUs) radio spectrum in an overlay approach. Since the CR transmitter may not have the perfect knowledge about the instantaneous channel quality between itself and the active PUs, the interference constraints imposed by each of the PUs are met in a statistical sense. We also study an optimal power allocation scheme that is augmented with raised cosine (RC) windowing-based spectral shaping. For a given power budget at the CR transmitter and the prescribed statistical interference constraints by the PUs, we demonstrate that although the on-the-run computational complexity of the proposed AIC-based optimal power allocation scheme is relatively higher, it may yield better transmission rate for the CR user compared to the RC windowing-based power allocation scheme. Further, the AIC-based suboptimal scheme has the least on-the-run computational complexity, and still may deliver performance that is comparable to that of the RC windowing-based power allocation scheme. The presented simulation results also show that both the AIC-based as well as the RC windowing-based power allocation schemes lead to significantly higher transmission rates for the CR user compared to the conventional (without any spectral shaping) optimal power allocation scheme.     

MaReSPS for energy efficient spectral precoding technique in large scale MIMO-OFDM

Spectral pre-coding is a capable method to restrain Out-Of-Band Emission (OOBE) and act in accordance with leaking parameters over neighboring frequency channels while masking unnecessary emissions. Nevertheless, spectral pre-coding might deform the real data vector that is articulated as the Error Vector Magnitude (EVM), which shows a harmful effect on the performance of Multiple-Input Multiple-Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM)-oriented schemes. In this research, a new Mapper Reducer for spectral pre-coded signal (MaReSPS) for energy-constrained signal receiver is proposed for energy efficient spectral precoding in the MIMO-OFDM system. This model involves Mapper Reducer (MR) framework for detecting the received signal, which renders an error rate, and graceful degradation is observed in the throughput under channel uncertainty. The proposed scheme alleviates the resultant Transmit EVM (TxEVM) observed at the receiver by capitalizing on the massive MIMO system, and as a result the throughput is improved. The comparison is done with respect to Block Error Rate (BLER), throughput, and Power Spectral Density (PSD) for proving the betterment of the proposed precoding model for MIMO-OFDM. In particular, the normalized throughput for conventional No OOBE Reduction (OOBER), Mask Compliant Spectral Pre-coder (MSP), Notching Spectral Pre-coder + Zero Forcing (NSP + ZF), P1/P2: CVX and P1/P2: Top- Alternating Direction Method of Multipliers (ADMM) models, as well as proposed MaReSPS model, is lower at a Signal to Noise Ratio (SNR) from 0 dB to 15 dB. With an increase in SNR, the normalized throughput increases and when SNR =40 dB, the normalized throughput values reach their peak values. However, compared to existing models, the proposed MaReSPS model showed high normalized throughput.     

一种基于授权信道特性的认知无线电频谱检测算法

针对认知无线电系统中频谱检测的频率直接影响系统容量以及与授权用户产生冲突的概率问题,分析了授权用户频谱使用的特性, 对授权用户行为进行统计建模, 提出一种自适应频谱检测算法. 引入控制因子, 在保证认知无线电系统稳定性的约束下, 自适应调整频谱感知的频率从而提高频谱利用率并减小系统冲突概率和检测开销, 进而降低了系统的能量消耗. 仿真结果表明, 该算法在保证不对授权用户产生干扰和一定的系统稳定性条件下, 有效地提高了系统的容量,并且具有良好的实用性和灵活性. 关键词： 认知无线电 自适应频谱检测 绿色通信 最大似然     

Two-stage deep learning-based hybrid precoder design for very large scale massive MIMO systems

Wireless networking is approaching a new era, which necessitates new frequency ranges and novel strategies. With recent circuit growth, communications over the Terahertz (THz) band is proving to be a viable option because of the tremendous bandwidth and low cost. On the other hand, THz signals suffer from significant direction loss, necessitating the use of precoding. In this paper, Deep Learning (DL) based precoding techniques for upcoming 6G networks were examined, along with their complexities. Based on the signal-to-noise ratio (SNR) and spectral efficiency (SE), the proposed DL-based precoding scheme is compared to traditional model-based precoding schemes. The proposed DL-based precoding technique is ideal for 6G networks, according to simulation results. Furthermore, the proposed DL-based precoding technique has lower computational complexity, making it suitable for parallel processing and high-speed data transmission.     

CEAR: A cooperation based energy aware reward scheme for next generation green cognitive radio networks

Cognitive Radio (CR) networks are envisioned as a key empowering technology of the fifth-generation (5G) wireless communication networks, which solves the major issues of 5G, like high-speed data transmission, seamless connectivity, and increased demand for mobile data. Another significant characteristic of the 5G network is green communications, as energy consumption from the communication field is predicted to rise remarkably by the year 2030. In this work, we are concerned about energy-related issues and propose a cooperation-based energy-aware reward scheme (CEAR) for next-generation green CR networks. The proposed CEAR scheme is based on the antenna and temporal diversity of the primary users (PUs). For providing the service to the PUs, the users of another network called cognitive users (CUs) work as a cooperative relay node, and, in return, they get more spectrum access opportunities as a reward from the primary network. The CUs with delay-tolerant data packets take a cooperative decision by recognizing the availability and traffic load of PUs, channel state information, and data transmission requirements. We utilize the optimal stopping protocol for solving the decision-making problem and use the backward induction method to obtain the optimal cooperative solution. The simulation results reveal notable enhancements in energy efficiency (EE) of CUs compared with other cooperative schemes. The proposed CEAR scheme is more energy-efficient for ultra-dense network deployment because results show that the CU’s EE, spectral efficiency (SE), and throughput improved with the increase of PUs.     

Delay-Doppler domain decision feedback turbo equalization for OTFS modulation

Emerging evidence indicates that orthogonal time–frequency space (OTFS) modulation is a potential candidate modulation scheme for high mobility wireless communications. However, OTFS may experience significant inter-symbol interference (ISI) and inter-Doppler interference (IDI) in the receiver. In this paper, we propose a soft decision feedback turbo equalization for OTFS transmission over delay-Doppler channels to jointly combat both interferences. A novel block decision feedback equalization (BDFE) algorithm is constructed using the band feature of the channels in the delay-Doppler domain. The feedforward and feedback filters are designed by the delay-time channels coefficients. According to the designed filter, an equivalent system model is employed to allow turbo equalization. The posterior probability is established using the soft prior information and feedback filter, and then fed back to the channel decoder as external information. Both theoretical analysis and simulation results demonstrate the effectiveness of the proposed scheme in improving the bit error rate (BER) performance and combat various interference. Numerical simulations are finally provided to justify the validity of the proposed scheme in improving the bit error rate (BER) performance and combating various interference.     

Analysis of the method of division of spatial channels with successive interference cancellation in modern MIMO-OFDM cellular systems

We consider the problem of parallel data transmission via several spatial channels in modern high-throughput cellular systems employing the OFDM (Orthogonal Frequency Division Multiplexing) and antenna arrays at both ends of the communication system. Parallel data transmission in such MIMO (Multiple Input Multiple Output) systems is achieved by using the beamforming schemes in the transmitter and the special methods of the spatial-channel division in the receiver. Interference immunity of the scheme of the spatial-channel division by the maximum-likelihood criterion using the method of successive interference cancellation is analyzed. Probability of implementation of the stage of successive interference cancellation for the case of two spatial channels and various combinations of the coding schemes and modulations is obtained. We analyze the efficiency of a cellular communication system using horizontal coding and successive interference cancellation. Practical recommendations on choosing modulation and the code speed for each spatial channel, which ensure maximum interference immunity of a receiver with successive interference cancellation, are made.     

Route selection for interference minimization to primary users in cognitive radio ad hoc networks: A cross layer approach

An opportunistic routing problem in a cognitive radio ad hoc network is investigated with an aim to minimize the interference to primary users (PUs) and under the constraint of a minimum end-to-end data rate for secondary users (SUs). Both amplify-and-forward (AF) and decode-and-forward (DF) relaying techniques are considered for message forwarding by SU nodes in the network. Unlike popular transmit power control based solutions for interference management in cognitive radio networks, we adopt a cross layer approach. The optimization problem is formulated as a joint power control, channel assignment and route selection problem. Next, closed form expression for transmission power is derived and corresponding channel selection scheme and routing metric are designed based on this solution. The proposed route selection schemes are shown to depend not only on gains of the interference channels between SUs and PUs but also on the values of the spectrum sensing parameters at the SU nodes in the network. Two distributed routing schemes are proposed based on our analysis; (i) optimal_DF and (ii) suboptimal_AF. The routing schemes could be implemented using existing table driven as well as on demand routing protocols. Extensive simulation results are provided to evaluate performance of our proposed schemes in random multihop networks. Results show significant reduction in PUs’ average interference experience and impressive performance as opportunistic routing schemes can be achieved by our schemes compared to traditional shortest path based routing schemes. Performance improvement is also reported over prominent recent schemes.     

Cross-layer design based hybrid MAC protocol for cognitive radio network

A minimal interrupted communication link setup is the primary objective of the MAC layer. The MAC layer is responsible for accessing the communication channel. At MAC, a control channel is used in the selection of collision free paths for data transfer. Therefore, the design of the control channel plays a pivotal role in achieving desired QoS in cognitive radio (CR) technology. Various schemes of control channel design help the CR network (CRN) to obtain better performance. The reported work focuses on a hybrid MAC protocol. The novelty of the scheme lies in the process of hybridization. A cross-layer framework is proposed for hybridization. The cross-layering has been done between network and MAC layer to achieve hybridization between different control channel design approaches. The broad categorization of control channel designs is between licensed in-band and dedicated unlicensed out-band approaches. In the in-band control channel design approach, the opportunistic use of data channel as control channel fulfills the decorum of CR technology. As soon as the primary user activity rises in the data channels, the in-band approach suffers from poor performance. On the other hand, the dedicated unlicensed out-band control channel design approach provides global coverage and all-time availability but suffers from channel saturation and intruder attacks. Interference in the control channel limits the use of out-band design. This motivates authors to develop a hybrid MAC protocol that can float between licensed in-band design and unlicensed out-band design to access the control channel. The hybridization is possible by sharing a primary user free channel list (PCL) among CR nodes. In conventional hybrid MAC protocols, the PCL is shared as a control beacon in the channel. Extra packet requirement as control beacon affects the performance of CR scenario. The proposed cross-layer design based hybrid MAC protocol avoids the need of an extra control beacon for PCL transmission. Further, the hybridization helps in achieving advantages of both in-band and out-band control channel design approaches. The simulation results show that the proposed hybrid MAC protocol performs satisfactorily in terms of packet delivery ratio, average throughput, average delay and control overhead. The performances are also tested in the worst scenarios.     

Design of bitstream-based adaptive-connected array architecture for mmWave Multiuser MIMO systems

We investigate a bitstream-based adaptive-connected massive multiple-input multiple-output (MIMO) architecture that trades off between high-power full-connected and low-performance sub-connected hybrid precoding architectures. The proposed adaptive-connected architecture which enables each data stream to be computed independently and in parallel, consists of fewer phase shifters (PS) and switches than the other adaptive-connected architectures. With smaller array groups, the proposed architecture uses fewer PS and switches, so that its power consumption gradually decreases in millimeter wave (mmWave) Multiuser MIMO (MU-MIMO) system. To fully demonstrate the performance of the proposed architecture in mmWave MU-MIMO system with practical constraints, we combine the connection-state matrix with the hybrid precoders and combiners to maximize energy efficiency (EE) of the system equipped with the proposed architecture. We then propose the hybrid precoding and combining (HPC) scheme suitable for multi-user and multi-data streams which utilizes the SCF algorithm to obtain the constant modulus of the analog precoder at convergence. In the digital precoding and combining stage, the digital precoder and combiner are designed to reduce the amount of computation by utilizing the singular value decomposition (SVD) of corresponding equivalent channel. In the mmWave MU-MIMO-OFDM system equipped with the proposed architecture, with the increase of the total number of data streams, simulation results demonstrate that we can exploit the proposed HPC scheme to achieve better EE than the traditional hybrid full-connected architecture exploiting some existing schemes.     

Spectral efficiency maximization for IRS-assisted wireless communication in cognitive radio networks

In this paper, we consider the Intelligent reflecting surface (IRS)-assisted cognitive radio (CR) network, in which the IRS is applied to improve the spectral efficiency of secondary users. In specific, the advantages of applying IRS is double: it can not only improve the transmission rate of the secondary users, but also reduce the interference from the secondary users to the primary users, which allow the secondary users to increase the transmission power correspondingly. The original problem is formulated by simultaneously consider the maximum power limitation of the secondary users and the Quality of Service (QoS) requirement of the primary users, which expressed by the interference temperature (IT). The formulated problem is non-convex and difficult to solve directly. We apply the alternating optimization (AO) algorithm to split the original problem into two sub-problems. For the first sub-problem, we transform it into a convex problem and for the second sub-problem, we use the successive convex approximation method to obtain the corresponding results. The simulation experiments show that the performance of our proposed scheme is better than existing schemes.     

Novel Multi-AP Coordinated Transmission Scheme for 7th Generation WLAN 802.11be

The demand for high-data-rate and time-sensitive applications, such as 4k/8k video streaming and real-time augmented reality (AR), virtual reality (VR), and gaming, has increased significantly. Addressing the inefficiency of distributed channel access and the fairness problem between uplink and downlink flows is crucial for the development of wireless local area network (WLAN) technologies. In this study, we propose a novel transmission scheme for IEEE 802.11be networks that addresses the fairness problem and improves the system throughput. Utilizing the concept of multi-AP coordinated OFDMA introduced in the 7th-generation WLAN IEEE 802.11be, the proposed transmission scheme allows an AP to share a granted transmission opportunity (TXOP) with nearby APs. A mathematically analysis of the throughput performance of the proposed schemes was performed using a Markov chain model. The simulation results verify that the scheme effectively improves the downlink fairness and the system throughput. Combined with the advanced multiuser (MU) features of IEEE 802.11ax, such as TUA, MU cascading sequence, and MU EDCA, the proposed scheme not only enhances downlink AP transmission, but also guarantees improved control over the medium. The scheme is carefully designed to be fully compatible with conventional IEEE 802.11 protocols, and is thus potentially universal.     

Deep learning-driven MIMO: Data encoding and processing mechanism

Large-scale Multiple-Input Multiple Output (MIMO) is the key technology of 5G communication. However, dealing with physical channels is a complex process. Machine learning techniques have not been utilized commercially because of the limited learning capabilities of traditional machine learning algorithms. We design a deep learning hybrid precoding scheme based on the attention mechanism. The method mainly includes channel modeling and deep learning encoding two modules. The channel modeling module mainly describes the problem formally, which is convenient for the subsequent method design and processing. The model design module introduces the design framework, details, and main training process of the model. We utilize the attention layer to extract the eigenvalues of the interference between multiple users through the output attention distribution matrix. Then, according to the characteristics of inter-user interference, the loss minimization function is used to study the optimal precoder to achieve the maximum reachable rate of the system. Under the same condition, we compare our proposed method with the traditional unsupervised learning-based hybrid precoding algorithm, the TTD-based (True-Time-Delay, TTD) phase correction hybrid precoding algorithm, and the deep learning-based method. Additionally, we verify the role of attention mechanism in the model. Extensive simulation results demonstrate the effectiveness of the proposed method. The results of this research prove that deep learning technology can play a driving role in the encoding and processing of MIMO with its unique feature extraction and modeling capabilities. In addition, this research also provides a good reference for the application of deep learning in MIMO data processing problems.     

Performance of multi-user transmitter preprocessing assisted MIMO system over correlated frequency-selective channels

In this contribution we present the performance of a multi-user transmitter preprocessing (MUTP) assisted multiple-input multiple-output (MIMO) space division multiple access (SDMA) system, aided by double space time transmit diversity (DSTTD) and space time block code (STBC) processing for downlink (DL) and uplink (UL) transmissions respectively. The MUTP is invoked by singular value decomposition (SVD) which exploits the channel state information (CSI) of all the users at the base station (BS) and only an individual user’s CSI at the mobile station (MS). Specifically, in this contribution, we investigate the performance of multi-user MIMO cellular systems in frequency-selective channels from a transmitter signal processing perspective, where multiple access interference (MAI) is the dominant channel impairment. In particular, the effects of three types of delay spread distributions on MUTP assisted MIMO SDMA systems pertaining to the Long Term Evolution (LTE) channel model are analyzed. The simulation results demonstrate that MUTP can perfectly eliminate MAI in addition to obviating the need for complex multi-user detectors (MUDs) both at the BS and MS. Further, SVD-based MUTP results in better achievable symbol error rate (SER) compared to popularly known precoding schemes such as block diagonalization (BD), dirty paper coding (DPC), Tomlinson–Harashima precoding (THP) and geometric mean decomposition (GMD). Furthermore, when turbo coding is invoked, coded SVD aided MUTP results in better achievable SER than an uncoded system.     

Network capacity of cognitive radio relay network

After successful dynamic spectrum access, cognitive radio (CR) must be able to relay the message/packets to the destination node by utilizing existing primary system(s) (PS) and/or cooperative/cognitive radio nodes in the cognitive radio network. In this paper, we pioneer the exploration of the fundamental behaviors of interference between CRs and PS in such a relay network via network coding. Interference on PS’s network capacity is shown to be unavoidable and unbounded in the one-hop relay network. Extending to the tandem structure, interference is unbounded but avoidable by appropriate constraints. In cooperative relay network, interference is bounded and avoidable. Moreover, parallel cooperative relay network can accommodate more CR transmission pairs. Such an analysis can be generalized to arbitrary networks. We derive that interference is avoidable when at least one route from CR’s source to the sink bypasses the bottlenecks of PS. Then under the constraint of no interference to PS, we derive CR’s maximum network capacity in such a network. Link allocation to achieve the maximum network capacity can be formulated and solved as a linear programming problem. Consequently, given any network topology, we can determine whether CR’s interference is avoidable, and maximize CR’s network capacity without interfering PS’s network capacity. Simulation results on randomly generated network topologies show that CR’s network capacity achieves on average 1.3 times of PS’s network capacity with interference avoidance constraint, and demonstrates spectrum efficiency at networking throughput and high availability.     

Optimal resource scheduling algorithm for cell boundaries users in heterogenous 5G networks

Deployment of small cells over the existing cellular network is an effective solution to improve the system coverage and throughput of fifth generation (5G) mobile communication networks. The arrival of the 5G mobile networks have demonstrated the importance of advanced scheduling techniques to manage the limited frequency spectrum available while achieving 5G transmission requirements. Cellular networks of the future necessitate the formulation of efficient resource allocation schemes that mitigate the interference between the different cells. In this research work, we formulate an optimization problem for heterogenous networks (HetNets) for resource allocation to maximize the system throughput among the cell center users (CCUs) and cell edge users (CEUs). We solve the optimization problem by effective utilization of the weight factors distribution for resource allocation. A novel Utility-based Resource Scheduling Algorithm (URSA) optimizes the resource sharing among the users with better delay budget of each application. The designed URSA ameliorates fairness along with reduced cross layer interference for real and non-real time applications. Performance of the URSA has been evaluated and compared most relevant state of art algorithms using the matlab based simulators. Furthermore, simulation results validate the superiority of the proposed scheduling scheme against conventional techniques in terms of throughput, fairness, and spectral efficiency.     

Joint optimal fair cooperative spectrum sensing and transmission in cognitive radio

Since the sensing power consumption of cooperative spectrum sensing (CSS) will decrease the throughput of secondary users (SU) in cognitive radio (CR), a joint optimal model of fair CSS and transmission is proposed in this paper, which can compensate the sensing overhead of cooperative SUs. The model uses the periodic listen-before-transmission method, where each SU is assigned a portion of channel bandwidth, when the primary user (PU) is estimated to be free by the coordinator. Then, a joint optimization problem of local sensing time, number of cooperative SUs, transmission bandwidth and power is formulated, which can compensate the sensing overhead of cooperative SUs appropriately through choosing suitable compensating parameter. The proposed optimization problem can be solved by the Polyblock algorithm. Simulation results show that compared with the traditional model, the total system throughput of the fairness cooperation model decreases slightly, but the total throughput of the cooperative SUs improves obviously.     

“Orthogonalized” sparsity enhanced mismatch models for wireless heterogeneous networks

Performance of MIMO precoder for heterogeneous networks can be hindered by a lack of accurate channel state information. The sparsity enhanced mismatch model (SEMM) has been proposed recently to account for the channel estimate mismatch problem by exploiting the inherent sparse characteristics of MIMO interference channels. When (single user-MIMO) SU-MIMO precoder design takes into account the SEMM, it was shown to have better transmission performance compared to the conventional norm ball mismatch model (NBMM) in a single-user multi-victims scenario. However, when communicating and interference channels are highly correlated, which can happen frequently in ultra-dense heterogeneous networks, performance of the SEMM precoder degrades and in some cases, underperforms the NBMM precoder. An “orthogonalized” SEMM (OSEMM) is proposed herein to modify the SEMM such that it is better suited for correlated channels. The concept of orthogonalization of channels is not new but this work uses it to enhance the SEMM, which creates synergy between transmission performance and robustness toward channel mismatch error. Two variants of the OSEMM are proposed, namely the OSEMM-LQ and OSEMM-SVD, to modify the basis expansion model that is an integral part of the SEMM. The resulting mismatch model influences the design of the SU-MIMO precoder that aims to maximize certain transmission criterion. Even though SU-MIMO precoding is considered, a new channel correlation definition, which acts as a metric for the OSEMM, is given that allows for user selection in a multiuser scenario such that optimal performance can be attained for the targeted user. Analytical and simulation results are given that highlight the difference in performance between the two variants of the OSEMM.     

无线多径信道中基于时间反演的物理层安全传输机制

宽带无线通信用户大多处在复杂的环境中,其时变多径传播和开放特性将严重影响通信系统的性能.针对物理层安全研究中的窃听信道问题,提出了一种适用于宽带无线多径信道的联合时间反演技术和发端人工噪声的物理层安全传输机制.首先,在一个典型窃听信道模型中采用时间反演技术,利用其时空聚焦性来提高信息在传输过程中的安全性;其次,由于时间反演的时空聚焦性,信息在聚焦点附近容易被窃听,通过在发送端加入人工噪声来扰乱窃听用户对保密信息的窃听,由于合法用户采用零空间人工噪声法,人工噪声对合法用户没有影响.理论分析和仿真结果表明,与已有物理层安全机制相比,所提机制可以有效地提高系统的保密信干噪比和可达保密速率,降低合法用户的误比特率,系统的保密性能得到提升.