Deep learning-based energy-efficient relay precoder design in MIMO-CRNs

This paper investigates the problem of energy efficient relay precoder design in multiple-input multiple-output cognitive relay networks (MIMO-CRNs). This is a non-convex fractional programming problem, which is traditionally solved using computationally expensive optimization methods. In this paper, we propose a deep learning (DL) based approach to compute an approximate solution. Specifically, a deep neural network (DNN) is employed and trained using offline computed optimal solution. The proposed scheme consists of an offline data generation phase, an offline training phase, and an online deployment phase. The numerical results show that the proposed DNN provides comparable performance at significantly lower computational complexity as compared to the conventional optimization-based algorithm that makes the proposed approach suitable for real-time implementation.     

Relay selection scheme based on deep reinforcement learning in wireless sensor networks

Cooperative communication technology has realized the enhancement in the wireless communication system’s spectrum utilization rate without resorting to any additional equipment; additionally, it ensures system reliability in transmission, increasingly becoming a research focus within the sphere of wireless sensor networks (WSNs). Since the selection of relay is crucial to cooperative communication technology, this paper proposes two different relay selection schemes subject to deep reinforcement learning (DRL), in response to the issues in WSNs with relay selection in cooperative communications, which can be summarized as the Deep-Q-Network Based Relay Selection Scheme (DQN-RSS), as well as the Proximal Policy Optimization Based Relay Selection Scheme (PPO-RSS); it further compared the commonly used Q-learning relay selection scheme (Q-RSS) with random relay selection scheme. First, the cooperative communication process in WSNs is modeled as a Markov decision process, and DRL algorithm is trained in accordance with the outage probability, as well as mutual information (MI). Under the condition of unknown instantaneous channel state information (CSI), the best relay is adaptively selected from multiple candidate relays. Thereafter, in view of the slow convergence speed of Q-RSS in high-dimensional state space, the DRL algorithm is used to accelerate the convergence. In particular, we employ DRL algorithm to deal with high-dimensional state space while speeding up learning. The experimental results reveal that under the same conditions, the random relay selection scheme always has the worst performance. And compared to Q-RSS, the two relay selection schemes designed in this paper greatly reduce the number of iterations and speed up the convergence speed, thereby reducing the computational complexity and overhead of the source node selecting the best relay strategy. In addition, the two relay selection schemes designed and raised in this paper are featured by lower-level outage probability with lower-level energy consumption and larger system capacity. In particular, PPO-RSS has higher reliability and practicability.     

Error rate analysis of AF-relay wireless networks under different SNR levels

This paper presents a new method for analyzing the Bit Error Rate (BER) performance of two-hop Amplify-and-Forward Multiple Relay (AF-MR) networks. This paper considers, a flat-fading channel and a relay selection scheme to select a relay with the highest Signal-to-Noise Ratio (SNR). The method aims to unify the BER calculation under low, high and optimal SNR levels. Asymptotic BER (ABER) performance at high SNR value is calculated first, and standard expressions for exact BER (EBER) performance at low and optimal SNRs are then derived. The analytic method depends on the conventional BER (CBER) approach of one-hop communication systems. The optimal SNR is obtained by balancing energy efficiency and spectral efficiency. The proposed method is found to be effective for calculating the BER of AF-MR network performance under any SNR conditions. Moreover, it improves the accuracy of ABER performance by reducing disparity computation errors between ABER and EBER performances and this allows the BER of AF-MR networks to be accurately calculated using either ABER or EBER. The outcome expressions for the method are validated by simulation results.     

Distributed spatial media-based modulation for relay networks with RF mirrors

A novel distributed spatial media-based modulation scheme is proposed in this paper by cleverly utilizing distributed spatial modulation (DSM) and media-based modulation (MBM) principles. This proposed scheme is referred to as distributed channel modulation (DCM) for relay networks. In this scheme, decode-and-forward relaying protocol is adopted, and the channel states are exploited for transmitting extra information bits by using a number of radio frequency (RF) mirrors that are placed near each relay. To provide a fair comparison with the conventional state-of-the-art schemes, the symbol error rate (SER) performance of DSM scheme is evaluated. Besides, a low complexity detection technique known as iterative maximum ratio combining (i-MRC) is used in order to reduce the receiver complexity of the proposed scheme. Simulation results demonstrate that the proposed DCM scheme significantly outperforms DSM scheme for the same average rate. It is also shown that there is a negligible degradation in the SER performance of the proposed DCM scheme when i-MRC detection is used as compared to the performance with maximum likelihood (ML) detection. Furthermore, a significant reduction in the receiver complexity is achieved by using i-MRC detection technique in contrast to the results with ML detector. It has been also revealed that the proposed DCM scheme shows a performance drop of about 3 dB when the availability of an imperfect channel state information (CSI) is assumed with the presence of channel estimation errors (CEEs). Finally, simulation results have confirmed the analytical findings.     

Asynchronous code-division random access using convex optimization

Many applications in cellular systems and sensor networks involve a random subset of a large number of users asynchronously reporting activity to a base station. This paper examines the problem of multiuser detection (MUD) in random access channels for such applications. Traditional orthogonal signaling ignores the random nature of user activity in this problem and limits the total number of users to be on the order of the number of signal space dimensions. Contention-based schemes, on the other hand, suffer from delays caused by colliding transmissions and the hidden node problem. In contrast, this paper presents a novel pairing of an asynchronous non-orthogonal code-division random access scheme with a convex optimization-based MUD algorithm that overcomes the issues associated with orthogonal signaling and contention-based methods. Two key distinguishing features of the proposed MUD algorithm are that it does not require knowledge of the delay or channel state information of every user and it has polynomial-time computational complexity. The main analytical contribution of this paper is the relationship between the performance of the proposed MUD algorithm in the presence of arbitrary or random delays and two simple metrics of the set of user codewords. The study of these metrics is then focused on two specific sets of codewords, random binary codewords and specially constructed algebraic codewords, for asynchronous random access. The ensuing analysis confirms that the proposed scheme together with either of these two codeword sets significantly outperforms the orthogonal signaling-based random access in terms of the total number of users in the system.     

Relay selection and nonlinear energy harvesting in full-duplex multi-relay cooperative network

Energy harvesting (EH) is a promising technique to extend the lifetime energy-constrained devices in wireless networks. This paper analyses the significance of relay selection and non-linear EH in evaluating the performance of Full-duplex (FD) decode-and-forward (DF) multi-relay cooperative network. The relay selection is considered under three scenarios based on channel state information (CSI) availability. We compare the EH performance of a hybrid power-time-splitting (PTS)-based non-linear energy harvester with time-spitting (TS) and power-splitting (PS) EH at the relay node. We derive the expression of outage probability (OP) of FD DF multi-relay cooperative network over independent and identically distributed Rayleigh fading channels. Numerical results show that the outage performance of the system is significantly enhanced by deploying an ideal relay selection scheme. This paper also gives insights about the combined effect of saturation threshold of non-linear EH and residual self-interference (RSI) on the system performance. Unlike linear EH systems, the non-linear EH causes the outage floor even when the RSI is significantly small. The response of the system is also analysed for the impact of TS and PS ratios. Furthermore, we also investigate the system’s performance in terms of other system parameters like the number of relays and data transfer rate. Monte Carlo simulations are used to verify the analytical expressions.     

Analysis and performance of network decoding strategies for cooperative network coding

In this work, we present two new low-complexity network decoding strategies for cooperative network coding in a multiple-access relay channel scenario. For these two strategies, Selection and Soft Combining and Majority Vote Network Decoding, along with the optimal joint network decoding, we derive expressions for bit error probability performance as a function of the signal-to-noise ratio (SNR) of the different Rayleigh fading links, and show the tightness of the derived bounds through simulation results. The two proposed schemes provide a similar bit error probability (BEP) performance compared to the optimal scheme, while having significantly lower complexity. Further, we study the effect of user pairing on the error performance by considering different SNRs on the user and relay links towards the destination. It is also shown that the error performance of the different schemes follows the same trend for a given user pairing strategy.     

Splitting algorithm for DMT optimal cooperative MAC protocols in wireless mesh networks

A cooperative protocol for wireless mesh networks is proposed in this paper. The protocol implements both on-demand relaying and a selection of the best relay terminal so only one terminal is relaying the source message when cooperation is needed. Two additional features are also proposed. The best relay is selected with a splitting algorithm. This approach allows fast relay selection within less than three time-slots, on average. Moreover, a pre-selection of relay candidates is performed prior to the splitting algorithm. Only terminals that are able to improve the direct path are pre-selected. So efficient cooperation is now guaranteed. We prove that this approach is optimal in terms of diversity-multiplexing trade-off. The protocol has been designed in the context of Nakagami-m fading channels. Simulation results show that the performance of the splitting algorithm does not depend on channel statistics.     

Physical layer security of vehicular networks with cooperative jamming helpers

As an important part of the intelligent transportation system (ITS), vehicular networks can provide drivers and passengers with more comfortable and convenient services such as efficient traffic management and infotainment. However, the security threats on data exchanges over vehicular networks have become increasingly severe. Different from conventional cryptographic technologies, the application of physical layer security (PLS) to vehicular networks has been investigated to prevent the security of exchanging data from the eavesdropper and measure precisely the leaked information to the eavesdropper, due to its low complexity and communication overhead. In this work, we are concerned with the PLS of cooperative vehicular networks consisting of a source vehicle, a destination vehicle, an eavesdropping vehicle and a cooperative jamming vehicle. First, to improve the secrecy performance, the cooperative jamming helper emits jamming signals to degrade the eavesdropping channel without harming the legitimate channel. Then, based on the Rayleigh fading channel models and the traffic models, the closed-form expressions of the secrecy outage probability (SOP) and the average secrecy capacity (ASC) of the considered vehicular networks are derived, which deliver more implications of various system parameters on SOP and ASC performances and can be computed without simulations at a lower complexity. Second, a definition of the optimal jamming vehicle is introduced and then the cooperative jamming vehicle selection strategy is presented. The existence of the optimal jamming vehicle is measured in probability, which is explored analytically. Third, the optimal power allocation that maximizes the secrecy capacity is found analytically for the source vehicle and the cooperative jamming helper. Finally, simulations are also presented to demonstrate the validation of these analytical results and confirm the advantages of the cooperative jamming strategy and the optimal power allocation. From the numerical results, more observations on the effects of the main system parameters on secrecy performances are obtained, which provides some useful guides for practice.     

Outage probability and ergodic capacity analysis of satellite–terrestrial NOMA system with mixed RF/mmWave relaying

This paper investigates the performance of hybrid radio frequency/millimeter wave (RF/mmWave) satellite–terrestrial relay network with non-orthogonal multiple access (NOMA) scheme. In particular, the satellite network operates at the Ka-band while the mmWave band is adopted at decode-and-forward (DF) relay–destination link. The blockage effect in mmWave communication is considered to deduce the ordered probability density function (PDF) of the user equipment (UE) distance. However, the randomness of the UEs’ location and the random beamforming design cause the difficulty of the performance analysis. Herein, we provide a closed-form approximation for the outage probability. Moreover, the system diversity order is derived from the asymptotic expressions of the outage probability at high signal-to-noise ratio (SNR) slopes. Besides, approximate ergodic capacity performances of the multi-UEs are evaluated with numerical integration. Simulations are applied to demonstrate the accuracy of the theoretical analysis and show that the performance of the ordered selection NOMA scheme outperforms the orthogonal multiple access scheme and random selection scheme.     

An Efficient Routing Scheme Based on Node Attributes for Opportunistic Networks in Oceans

Along with the fast development of the marine economy and ever-increasing human activities, handy and reliable marine networking services are increasingly required in recent years. The ocean faces challenges to support cost-effective communication due to its special environments. Opportunistic networks with easy deployment and self-curing capability are expected to play an important role to adapt to such dynamic networking environments. In the literature, routing schemes for opportunistic networks mainly exploit node mobility and local relaying technologies. They did not take into account the impact of node behaviors on encountering opportunities and in case of no further relaying, network performance would be greatly degraded. To solve the problem, we propose an efficient routing scheme based on node attributes for opportunistic networks. We first construct delivery competency to predict the further relay nodes. Then a forwarding willingness mechanism is introduced to evaluate the relaying probability combining device capacity and movement behaviors of nodes. Finally, the utility metric is used to make decisions on message forwarding. The results show that the proposed scheme improves network performance in terms of delivery ratio, average latency, and overhead ratio as compared to other schemes.     

An optimal protection and restoration scheme (OPARS) for optical networks

Protection and restoration are critical network design issues for optical networks since even a single failure for a short duration may result in huge data loss due to the large capacity of optical fibers. However, few studies have been done on these issues for optical burst switching (OBS) networks. Protection and restoration are essential mechanisms for guaranteeing more reliable traffic delivery services. But it is not easy to apply existing mechanisms to optical burst switching (OBS) networks due to its one-way reservation signaling and the statistical burst multiplexing. Thus, to achieve the high transmission performance and reliability simultaneously, unique properties of OBS must be considered in the design of protection scheme. In this paper, an optimal protection and restoration scheme (OPARS) has been introduced that not only optimizes the number of provisioned protection wavelengths adaptively based on the traffic load as well as the quality of service (QoS) requirements of bursts in high speed networks but also minimizes burst loss rates. In addition, the proposed scheme has been used as an efficient contention resolution technique. The simulation results verify that the proposed scheme improve the network resource and channel utilization while guaranteeing the targeted protection reliability and QoS requirements of bursts.     

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.     

A spectrum handoff scheme for optimal network selection in Cognitive Radio vehicular networks: A game theoretic auction theory approach

The recent strides in vehicular networks have emerged as a convergence of multi radio access networks having different user preferences, multiple application requirements and multiple device types. In future Cognitive Radio (CR) vehicular networks deployment, multiple radio access networks may coexist in the overlapping areas having different characteristics in terms of multiple attributes. Hence, it becomes a challenge for CR vehicular node to select the optimal network for the spectrum handoff decision. A game theoretic auction theory approach is interdisciplinary effective approach to select the optimal network for spectrum handoff. The competition between different CR vehicular node and access networks can be formulated as multi-bidder bidding to provide its services to CR vehicular node. The game theory is the branch of applied mathematics which make intelligent decision to select the optimal alternative from predetermined alternatives. Hence, this paper investigates a spectrum handoff scheme for optimal network selection using game theoretic auction theory approach in CR vehicular networks. The paper has also proposed a new cost function based multiple attribute decision making method which outperforms other existing methods. Numerical results revel that the proposed scheme is effective for spectrum handoff for optimal network selection among multiple available networks.     

An optimal burst assembly approach employing traffic shaping (OBATS) for OBS

Optical burst switching (OBS) aims at combining the strengths of packet and circuit switching and is considered as a promising technology for implementing the next generation optical Internet, required to cope with the rapid growth of Internet traffic and the increased deployment of new services. In this paper, an optimal burst assembly approach employing traffic shaping (OBATS) for OBS networks has been proposed in order to improve network performance in terms of reduced blocking probability, congestion control and better utilization of bandwidth. Particularly, the proposed scheme aims at reducing the average delay experienced by the packets during the burstification process in optical burst switched (OBS) networks, for a given average size of the bursts produced. Reducing the packet burstification delay, for a given average burst size, is essential for real-time applications; correspondingly, increasing the average burst size for a given packet burstification delay is important for reducing the number of bursts injected into the network and the associated overhead imposed on the core nodes. Simulation results show that the proposed burst assembly approach gives better network performance in terms of burst drop, resource contention and delay as compared to conventional burst assembly approaches.     

Wavelength conversion in multihop wavelength division multiplexed transport networks

In this paper, we have analyzed the wavelength conversion for a multihop wavelength division multiplexed (WDM) transport networks to minimize the blocking probability. The analytical model is presented for a static-routing for a multihop paths wavelength conversion. The results have been validated for three cases, without wavelength conversion, with wavelength conversion and limited wavelength conversion. We show that by using wavelength conversion, there is significant improvement in performance of these transport networks. Each of the three cases of wavelength conversion is discussed for different degrees of conversions. It is observed that the higher the degree of conversion is, the lower is the blocking probability. Also as the degree of conversion is increased further, the curve for limited range conversion moves towards the no conversion.     

Game theory-based radio resource allocation in NOMA vehicular communication networks supported by UAV

Vehicular communication networks are emerging as a promising technology to provide high-quality internet service such as entertainment for road users via infrastructure-to-vehicle (I2V) communication, and to guarantee road users’ safety via vehicle-to-vehicle (V2V) communication. Some technical issues that impact the performance of these networks are the lack of or poor communication paths between vehicles, and the limitation of radio resources. Unmanned aerial vehicles (UAVs) as promising solutions for supporting vehicular networks could provide communication coverage in hazardous environments and areas with no capacities for installation or maintenance of ground base stations (BSs). Also, non-orthogonal multiple access (NOMA) methods can improve spectral and energy efficiency and thereby allow more users to be connected to the desired network. In this paper, exploring the NOMA, we develop a scheme for optimum resource allocation in presence of a UAV that supports vehicular communications. Resource allocation for this scenario is formulated as a mixed-integer non-linear programming (MINLP) problem. Due to the high complexity of such problems, we propose two low-complexity near-optimal methods. First, we apply difference-of-concave-functions (DC) approximations to solve the problem in an iterative process. Next, we use Stackelberg game-based method for efficient solving, and then, closed-form expressions of optimal power allocations using KKT-conditions are derived. Simulations illustrate the effectiveness of the proposed scheme along with the Stackelberg game-based method.     

Half-duplex relaying with serially concatenated low-density generator matrix (SCLDGM) codes

Recently, the integration of cooperation with coding has proven to be a very useful technique to enhance relay system performance. Relay systems based on low-density parity-check (LDPC) codes have the potential to approach the theoretical information limits very closely. However, LDPC codes have a disadvantage in that the encoding complexity is high. To solve this problem, serially concatenated low-density generator matrix (SCLDGM) codes are attracting attention owing to their low encoding complexity. However, the performance of SCLDGM coded relaying has not been investigated. In this paper, we apply SCLDGM codes to relay channel with the motivation of lowering the encoding complexity. Since SCLDGM codes are concatenated codes, we cannot use the same method as LDPC coded relaying. We propose a new relaying system and three cooperation protocols suited for SCLDGM codes. Computer simulation results show that the bit error rate (BER) performance of the proposed system is equal to that of conventional LDPC coded relaying system.     

High SNR approximation for performance analysis of two-way multiple relay networks

Signal-to-Noise Ratio (SNR) is the key parameter in the performance evaluation of Two-Way, Amplify-and-Forward, Multiple-Relaying (TAF-MR) networks. This paper introduces new methods that use high SNR (HSNR) level approximations to analyze accurately the Bit Error Rate (BER), optimize the Relay Location (RL), and balance the energy efficiency (EE) and spectral efficiency (SE) of such networks. We consider a flat-fading channel and a strategy for selecting a relay with the highest SNR. Standardized BER expressions are obtained for a wide range of SNRs (low, high, and optimal) to yield more accurate predictions of the BER performance. Thus, by using HSNR, a unified analysis for calculating the Asymptotic BER (ABER) and the exact BER (EBER) performance is developed. The optimal SNR level is obtained by optimizing the power of network sources, which include the relay and user powers. Further, we propose a method for integrating the RL with the balancing of the EE and SE optimally to achieve the best EE improvement. The derived expressions for the methods are validated by simulation.     

Social-aware relay selection and energy-efficient resource allocation for relay-aided D2D communication

How to improve the flexibility of limited communication resources to meet the increasing requirements of data services has become one of the research hotspots of the modern wireless communication network. In this paper, a novel social-aware motivated relay selection method is put forward to allocate the energy efficiency (EE) resources for the device-to-device (D2D) communication. To improve system flexibility, a D2D user is selected to act as a relay named D2D-relay instead of the traditional cellular relay. The optimal relay selection strategy is formulated by searching the maximum trust value that is obtained by assessing the link stability and social connections between two users. Then, the resource allocation problem, which turns out to be a mixed-integer nonlinear fractional programming (MINLFP) problem, is solved by maximizing the total EE under physical constraint and social constraint synthetically. To improve the solution efficiency, a novel iterative algorithm is proposed by integrating the Dinkelbach theory and Lagrange dual decomposition method. Simulation results demonstrate the effectiveness of the proposed scheme. Compared with the existing social-blind and social-aware schemes, it significantly improves the probability of successful relay selection and total EE of the D2D pairs.