ABSTRACT Federated learning (FL) enables collaborative model training across multiple parties while preserving data privacy. However, FL remains vulnerable to privacy leakage through model updates. Differential privacy (DP) has been incorporated into FL by adding noise to model updates to ensure robust privacy protections. Traditional DP methods set a fixed sensitivity limit, resulting in excessive noise addition and performance degradation, especially in complex models such as RS, where the high‐dimensional parameters of the model pose a major challenge to effective noise addition. The higher the parameter dimension of the model, the greater the effect of including all the noise will be on the performance of the recommendation system. This study presents an adaptive local differential privacy technique grounded in Fisher information for reinforcement learning, which dynamically modifies the privacy budget by assessing the significance of model parameters during each training iteration. Specifically, Fisher information is used to assess the significance of each parameter layer, with more noise added to less important layers and less noise added to more critical layers. This approach optimizes the noise allocation while maintaining model performance under the DP guarantee. At the same time, we decouple the federated recommendation system (FRS) from the DP mechanism, enabling seamless integration with a variety of recommendation models. We assess the effectiveness of the proposed method through theoretical analysis and experiments on multiple benchmark datasets. The results demonstrate that the Fisher‐based adaptive DP method significantly improves model performance compared to traditional fixed‐sensitivity DP methods in FL environments, particularly addressing the challenges posed by the complexity of RS parameters.

The widespread deployment of dual-camera systems has laid a solid foundation for practical applications of infrared (IR)-RGB cross-modality person reidentification (ReID). However, the inherent modality differences between RGB and IR images cause significant intra-class variances in the feature space for individuals of the same identity. Current methods typically employ various network architectures for the image style transfer or extracting modality-invariant features, yet they overlook the information extraction from the most fundamental spectral semantic features. Based on the existing approaches, we propose a multi-spectral semantic alignment (MSSA) architecture aimed at aligning fine-grained spectral semantic features across both intra-modality and inter-modality perspectives. Through modality center semantic alignment (MCSA) learning, we comprehensively mitigate differences in identity features of different modalities. Moreover, to attenuate the discriminative information unique to a single modality, we introduce the modality reliability intensification (MRI) loss to enhance the reliability of identity information. Finally, to tackle the challenge that inter-modality intra-class disparities surpass inter-modality inter-class differences, we leverage the dynamic discriminative center (DDC) loss to further bolster the discriminability of reliable information. Through an extensive experiments conducted on SYSU-MM01, RegDB, and LLCM datasets, we demonstrate the substantial advantages of the proposed MSSA over other state-of-the-art methods.

Nonorthogonal multiple access (NOMA) can facilitate simultaneous data transmissions towards multiple users by using the superposition coding and successive interference cancelation techniques, which can greatly improve the spectrum efficiency. Cooperative relaying and space-time coding can promisingly improve the communication robustness of poor-quality links by achieving the space-diversity gain. Energy harvesting (EH) can prolong the lifetime of energy-limited terminals and make them work continuously. In order to enhance the spectrum efficiency as well as the communication quality, we enable a cluster of EH relays to assist the data transmissions from a base station (BS) to two far-users by using the Alamouti coding based cooperative NOMA strategy. The relays are capable of harvesting wireless energy from a power beacon as well as BS by using time-switching or power-splitting method. According to the energy status and the data decoding status, one relay is selected in a distributed manner according to either Max-min, Max-sum, or Random criterion. We analyze the transmission success probability and the system throughput performance. Extensive simulations are performed to compare the performance of different EH-based space-time coded cooperative NOMA with various relay selection schemes as well as the counterpart orthogonal transmission schemes.

We propose a partial cooperative zero-forcing (PCZF) decoding scheme for the uplink cell-free massive MIMO system, wherein the neighboring access points (APs) around each user equipment (UE) share the channel state information (CSI) and jointly suppress the interference using the zero-forcing technique. Using asymptotic analysis, we derive a closed-form asymptotic expression for a lower bound on the achievable rates. Considering the unique and complex form of the achievable rates, we propose power control schemes according to two criteria. The first criterion is to maximize the minimum achievable rate. For this criterion, we propose a target-SINR-tracking (TST)-based bisection algorithm. Since the power control update functions are standard interference functions, the TST-based bisection method always converges to the optimal solution. The second criterion is to maximize the sum rate, for which we propose two power control algorithms: 1) randomization and scaling algorithm (RSA) and 2) fractional programming algorithm (FPA). In each iteration of the RAS algorithm, we first exploit the randomization technique to transform the sum-rate maximization problem into a series of power minimization problems, and then improve the sum rate by scaling. In the FP algorithm, we derive a lower bound on the sum rate, and then propose an iterative approach based on the Lagrangian dual transform and fractional programming to maximize the sum-rate lower bound. Numerical results validate the theoretical analysis and verify the efficiency of the proposed power control algorithms.

We investigate the activity detection and channel estimation issues for cell-free Internet of Things (IoT) networks with massive random access. In each time slot, only partial devices are active and communicate with neighboring access points (APs) using non-orthogonal random pilot sequences. Different from the centralized processing in cellular networks, the activity detection and channel estimation in cell-free IoT is more challenging due to the distributed and user-centric architecture. We propose a two-stage approach to detect the random activities of devices and estimate their channel states. In the first stage, the activity of each device is jointly detected by its adjacent APs based on the vector approximate message passing (Vector AMP) algorithm. In the second stage, each AP re-estimates the channel using the linear minimum mean square error (LMMSE) method based on the detected activities to improve the channel estimation accuracy. We derive closed-form expressions for the activity detection error probability and the mean-squared channel estimation errors for a typical device. Finally, we analyze the performance of the entire cell-free IoT network in terms of coverage probability. Simulation results validate the derived closed-form expressions and show that the cell-free IoT significantly outperforms the collocated massive MIMO and small-cell schemes in terms of coverage probability.

In this article, we propose an uplink cell-free Industrial Internet of Things (IIoT) framework to support a large number of devices with random data arrivals. By adopting nonorthogonal random pilots and the large-scale fading decoding technique, we derive the closed-form expression of the transmission capacity for each terminal. Considering different statistics of random data arrivals, we formulate a long-term stochastic optimization problem to maximize the minimum time average transmission success ratio (TATSR) through jointly determining the power control coefficients and the combining coefficients for each time period. We reformulate the long-term max–min problem into a sequence of subproblems to minimize the Lyapunov drift plus penalty in each time period. We approximate each mixed integer subproblem as a sigmoid optimization problem, and propose an iterative algorithm by the aid of quadratic transform-based fractional programming and the sequential convex programming to solve it. Simulation results show that our proposed scheme can boost the TATSR.

We consider a cognitive radio-based Internet-of-Things (CR-IoT) network consisting of one primary IoT (PIoT) system and one secondary IoT (SIoT) system. The IoT devices of both the PIoT and the SIoT, respectively, monitor one physical process and send randomly generated status updates to their associated access points (APs). The timeliness of the status updates is important as the systems are interested in the latest condition (e.g., temperature, speed, and position) of the IoT device. In this context, two natural questions arise: 1) how to characterize the timeliness of the status updates in CR-IoT systems? 2) which scheme, overlay or underlay, is better in terms of the timeliness of the status updates? To answer these two questions, we adopt a new performance metric, named the age of information (AoI). We analyze the average peak AoI of the PIoT and the SIoT for overlay and underlay schemes, respectively. Simple asymptotic expressions of the average peak AoI are also derived when the PIoT operates at high signal-to-noise ratio (SNR). Based on the asymptotic expressions, we characterize a critical generation rate of the PIoT system, which can determine the superiority of overlay and underlay schemes in terms of the average peak AoI of the SIoT. Numerical results validate the theoretical analysis and uncover that the overlay and underlay schemes can outperform each other in terms of the average peak AoI of the SIoT for different system setups.

Integrating energy harvesting into cognitive radio networks can promisingly sustain the operations of energy-limited terminals and improve the spectral efficiency. Traditionally, secondary users (SUs) are normally assumed to harvest energy from the transmissions of primary users (PUs). In contrast, we consider an alternative scenario with a PU harvesting energy from its access point (AP) and SUs, and propose a cognitive relaying scheme. Each time block is divided into two periods. In the first period, AP transfers wireless energy to PU, and meanwhile, an SU and its relay cooperatively transmit secondary data using peak powers, where the resultant interference can help boost the amount of energy harvested by PU. In the second period, PU transmits data to AP using the harvested energy, and meanwhile, SU and its relay continue to cooperatively transmit secondary data using controlled powers under the interference constraint at AP. We analyze the throughput of both the two systems and reveal the impacts of key parameters. Numerical results show that our scheme can greatly improve the system spectral efficiency while satisfying the interference constraint of PU.

Cognitive radio and full duplex transmissions are regarded as two effective techniques to improve the spectral efficiency. By combining the two promising techniques, the problem of power allocation for bi‐directional full duplex underlay cognitive spectrum sharing is studied. To protect the primary transmission, consider both the instantaneous and statistical channel state information to adjust the transmit powers of secondary users to avoid causing intolerable interference to the primary users. The corresponding quality‐of‐service (QoS) constraints are determined for the primary system. On this basis, propose optimal power allocation schemes to facilitate the secondary transmission while guaranteeing the primary QoS constraint. Simulation results are provided to verify the efficiency of proposed schemes in improving the system capacity.

We consider underlay spectrum sharing with wireless energy transfer in a large-scale cognitive radio network, where each primary user (PU) can harvest radio frequency energy from its associated access point (AP). An energy cooperation zone is applied around each PU, wherein the secondary user (SU) with the best channel quality toward PU is selected to cooperatively transfer wireless energy. With SUs' assistance, each PU can harvest a predefined amount of energy in a shorter time, so there will be more concurrent primary links in the network. We analyze the transmission probability of PUs by properly modeling their energy statuses using Markov chain. Furthermore, a guard zone is applied around each active AP to prohibit the nearby SU transmissions to avoid strong interference, and SUs outside the guard zones of all the active APs can access the spectrum opportunistically. Under the constraint that the area throughput of primary system should be improved by at least a certain degree, the area throughput of the secondary system is maximized by jointly determining the SUs' density and the guard zone radius. Numerical results show that our scheme can well accommodate SUs' transmissions while guaranteeing PUs' performance requirement.

In this study, the authors propose an in‐band full‐duplex massive multi‐user multiple‐input multiple‐output system which exploits the separate‐antenna arrays at base station and the shared‐antenna at users. First, the channel state information of the two antenna arrays is estimated using the minimum mean‐square‐error method according to the channel reciprocity, and then the matched‐filter and zero‐forcing linear processing methods are adopted to analyse the system spectral efficiency (SE). The authors derive the lower and upper bounds of the uplink and downlink achievable rates in succinct forms to measure the system performance. The selection of full/half‐duplex mode is also discussed with respect to the number of users. Numerical and simulation results show that the optimal downlink SE can be achieved with the increase of active users, and the full/half‐duplex mode selection can help improve the system SE with the variation of traffic loads.

In this study, the authors propose a non‐orthogonal multiple access (NOMA) relaying protocol with truncated automatic repeat request (ARQ), where the source simultaneously transmits two signals to the relay and the destination using superposition coding technique in the power domain. The successive interference cancelation technique is adopted by the relay, who should first decode its own data and then cancel it for the decoding of destination data. If either the data intended for the relay or the destination is erroneously received by the relay, it will be retransmitted by the source using full power. After correctly decoding the source data intended for the destination, the relay will forward it and the retransmission is performed when the data is erroneously received by the destination. The authors derive the system throughput and investigate the impacts of several parameters, such as power allocation factor, target rate, and distance between terminals etc. In terms of throughput, the authors’ proposed scheme can greatly outperform the conventional relaying scheme with truncated ARQ.

The authors consider the multiuser cognitive radio network, where a primary link coexists with multiple secondary transmitters (STs) which intend to communicate with an access point (AP). All the STs can harvest the radio frequency energy from the AP, while the primary terminals have the continuous power supply. The primary data transmission and the ST energy harvesting can be simultaneously performed to improve both the spectral and the energy efficiencies. The STs that have correctly decoded the primary data are classified as potential relays. Each potential relay will calculate the channel quality between itself and the primary receiver and the best one is selected to relay the primary data using the harvested energy. Thanks to the cooperation from STs, the primary data can be more reliably transmitted and the throughput requirement of primary system can be more easily satisfied in a shorter time, and as a reward all the STs except the selected one can transmit their own data to the AP in the remaining time. To facilitate the primary data cooperation, the energy transfer and the secondary data transmission, the optimal time allocation can be determined through maximising the STs’ throughput while guaranteeing the throughput of primary system.

We propose a simultaneous wireless information and power transfer scheme based on the power-splitting technique for the downlink of multi-user massive antenna-array systems. The base station (BS) can transmit both the wireless energy and information simultaneously to the user equipments (UEs). Using the harvested energy, each UE can transmit its pilot signal to the BS for the downlink channel estimation by exploiting the channel reciprocity of the time division duplexing system. When the antenna scale is large enough, the ergodic achievable rates of UEs are derived in closed-form. To maximize the minimum achievable rate among all the UEs, an iterative algorithm with low-complexity is proposed to jointly optimize the power allocation coefficients of the BS and the power-splitting ratios of the UEs. In each iteration of the proposed algorithm, the optimal power-splitting ratios can be determined according to the closed-form expressions for a given power allocation coefficient. The convergency, optimality, and complexity of our proposed algorithm are analyzed theoretically. The equal power allocation is shown to be optimal when the transmit power of BS is large enough. Furthermore, the optimal number of antennas is determined to maximize the energy efficiency. Simulation results are provided to validate our closed-form approximations and verify the efficiency of our proposed algorithm.

In this paper, we study a multi-pair two-way relay network consisting of two groups of user equipments (TIEs), who want to exchange information through a common relay equipped with massive antennas. In the multiple access phase, the TIEs transmit information to the relay using the energy harvested in the last time block, and the relay decodes information using zeroforcing (ZF) or maximal ratio combining (MRC) technique. In the broadcasting phase, the relay performs simultaneous wireless information and power transfer (SWIPT), and each TIE receives energy and information using the power-splitting scheme. Due to the channel hardening effect of the large-scale antenna array, the harvested energy of each TIE in each time block is asymptotically constant. Based on the derived achievable rates of TIEs, a multi-objective optimization problem (MOOP) is formulated to maximize the achievable rates of all the pairs. Through solving the MOOP, the power-splitting ratios of TIEs can be determined in closed-form for the ZF-based relaying. For the MRC-based relaying, a two-stage iterative Pareto improvement algorithm is proposed to achieve the Pareto optimality. Simulation results are presented to validate our theoretical analysis and verify the efficiency of our proposed algorithm in improving the system rate.

In this paper, we propose an energy harvesting (EH) based spectrum leasing protocol in the cognitive radio network, where multiple secondary transmitters (STs) can simultaneously harvest the radio frequency energy from the primary user (PU) transmission. After the EH period, the PU transmits its data with the assistance from the STs. Among all the STs that can correctly decode the primary data and harvest enough energy, the one with the best channel status towards the primary receiver is selected to relay the primary data. Thanks to the STs' cooperation, the primary data can be more easily delivered using less time, and as a reward, all the STs except the selected one can transmit their own data in the TDMA manner. The data success probabilities of both primary and secondary systems are analyzed by considering the EH and information decoding statuses of STs. Numerical results show that our protocol can well accommodate the STs' transmissions while guaranteeing the PU performance.

We investigate the uplink achievable rate and its approximation in the shared-antenna full-duplex massive MU-MIMO system. The proposed concise approximation depends on the channel distribution information rather than the instantaneous channel state information. We further analyze the spectral efficiency of this model based on the approximate results. In the discussion, we focus on the ZF and the MRT/MRC linear processing techniques which are commonly used in the massive MIMO systems. Monte- Carlo simulations show that our approximation is very close to the real result, and the shared- antenna full-duplex massive MU-MIMO system significantly outperforms the half-duplex system in terms of spectral efficiency when the scale of antenna array is large enough.

This study investigates the downlink error‐rate performance for a multicell distributed antenna system (DAS) over Nakagami‐ m fading channels, where arbitrary number of remote antenna units are uniformly deployed within each macrocell. An antenna selection transmission scheme is presented to reduce the power consumption and the number of interfering signals from outside the target macrocell. Unlike most previous studies in which the interference plus noise is assumed to be Gaussian distributed with constant variance, the co‐channel interference here is considered as a random variable. The authors first derive a closed‐form bit error rate (BER) expression by adopting the commonly used Gaussian‐ Q ‐function approximation. Furthermore, consider the desired signal and the interfering signals undergo different fading severities, they derive a precise BER expression in terms of single‐integral by applying the characteristic function. For the special case of Rayleigh fading, an exact closed‐form BER expression is obtained. Finally, these error‐rate expressions which can be evaluated efficiently indicate some significant insights into the characteristics of the antenna selection transmission for DAS. Simulation results are provided to validate their theoretical analysis.

Spectrum sharing and energy harvesting are promising techniques to improve the bandwidth and energy efficiencies to meet the ever-growing demand of wireless data transmissions. In this paper, we propose an efficient relay-based spectrum sharing protocol in the cognitive radio network, where the secondary user (SU) can implicitly harvest the radio frequency (RF) energy from the primary user (PU) transmissions. Using the harvested energy, the SU can assist the PU transmission to exchange for the opportunity of spectrum access. Both the Alamouti coding and the superposition coding techniques are adopted by the transmitters to facilitate the primary data relaying and the secondary data transmission simultaneously. The joint decoding and the interference cancelation techniques are adopted by the receivers to retrieve the desired signals. The more power allocated for the primary data relaying, the higher throughput can be achieved for the PU, while the less throughput is available for the SU with less energy remained. The optimal power allocation is numerically determined by maximizing the SU throughput while guaranteeing the PU throughput. Numerical results show that our protocol can greatly improve the system throughput and the impacts of various system settings are revealed for the network deployment.

The spectrum sharing is able to meet the increasing data transmission requirements over the limited spectrum bands, while the energy harvesting is a promising technique to make the network sustainable by self-recharging the terminals. For the cognitive radio network with one primary link and one secondary link, we propose a time-domain spectrum sharing scheme based on the wireless energy transfer (WET). The secondary transmitter (ST) can wirelessly transfer energy along with the primary base station (PB) to the primary user equipment (UE). The UE can forward its primary data to the PB over the uplink using the harvested energy, while the ST is allowed to access the spectrum and transmit its own data for a time. The main idea is that the secondary system supplies energy to the primary system and acquires the spectrum for the secondary data transmission. Considering the dependence of the energy and data transfer, we analyze the throughput of both systems under the peak power constraint. The time allocations are determined by maximizing the throughput of secondary system while guaranteeing the throughput requirement of primary system. Simulation results are provided to validate our theoretical analysis and the numerical results reveal the impacts of various parameter configurations to the system performance.

Spectrum sharing between cellular and ad-hoc networks is studied in this work. Weak signals and strong interferences at the cell-edge area usually cause severe performance degradation. To improve the cell-edge users' performance quality while keeping high spectrum efficiency, in this paper, we propose a cooperative spectrum sharing scheme. In the proposed scheme, the ad-hoc users can actively employ cooperative diversity techniques to improve the cellular network downlink throughput. As a reward, a fraction of the cellular network spectrum is released to the ad-hoc network for its own data transmission. To determine the optimal spectrum allocation, we maximize the ad-hoc transmission capacity subject to the constraints on the outage probability of the ad-hoc network and on the throughput improvement ratio of the cellular network. Both the transmission capacity of the ad-hoc network and the average throughput of the cellular network are analyzed using the stochastic geometry theory. Numerical and simulation results are provided to validate our analytical results. They demonstrate that our proposed scheme can effectively facilitate ad-hoc transmissions while moderately improving the cellular network performance.

In this paper, we propose a cooperative spectrum sharing scheme between cellular network downlink and mobile ad-hoc network based on the analysis using stochastic geometry theory. The licensed spectrum belongs to the cellular network and the strong interference at cell-edge becomes a bottleneck to guarantee the quality of service requirement. In this case, the secondary ad-hoc users can assist the transmission between the base station and cell-edge mobile users in exchange for spectrum usage. Through maximizing the transmission capacity of secondary system under the constraint of throughput improvement of primary system, an optimal spectrum allocation can be obtained. Numerical and simulation results are provided to validate the analysis and verify the efficiency of the proposed scheme.

A spectrum sharing scheme is proposed for overlaid wireless networks based on the decode-and-forward two-path successive relaying technique. In this scheme, two secondary transmitters are used to transmit secondary information alternately to their respective receivers while relaying the primary signal at the same time. The transmission of the primary system is continuous, and the secondary system can opportunistically help the primary transmission in exchange for the spectrum sharing. Superposition coding is used at the secondary transmitters, where the primary signal and secondary signal are linearly combined. Successive interference decoding and cancelation is performed by secondary users to extract their desired signals. For the primary system, joint decoding is performed by treating the secondary signal as noise at the receiver side. The optimal power allocation is determined to maximize the success probability of the secondary system without violating the outage performance of the primary system. Numerical and simulation results show that the proposed scheme can conservatively protect the primary performance while realizing the secondary transmission requirement.

In this paper, an adaptive spectrum-leasing scheme is proposed for multiuser cognitive radio networks. Based on two-path successive relaying and decode-and-forward protocols, each cognitive user intelligently switches the spectrum leasing protocol to cooperate in the primary data transmission. As a reward, the spectrum is then leased to the cognitive user for its own data transmission. For given m cognitive users, the best one that can achieve the largest secondary rate while satisfying primary rate target is scheduled for the spectrum leasing. It is shown that the cooperative diversity gain of m+1 is achieved for the primary system and the selection diversity gain of m is obtained for the secondary system. Moreover, multiuser diversity of secondary system is studied and the throughput scales as log(log m) when m is large. Performance results are provided to verify the efficiency of the proposed adaptive spectrum leasing scheme compared with the decode-and-forward based scheme and two-path successive relaying based scheme.

A spectrum leasing scheme is proposed for overlaid wireless networks based on the spectral efficient relaying. The potential secondary transmitters compete to assist the primary transmission using the one-path alternate or two-path successive relaying in exchange for the opportunity of spectrum access. The cognitive relaying mode is dynamically chosen based on the availability of secondary receiver for the cooperation. The secondary user that can support the primary transmission and has the highest secondary rate is selected as the best to join the spectrum leasing. The rate and outage performance of both systems are studied. Numerical results show that the primary performance is greatly improved compared with direct transmission, and the secondary requirement is also well satisfied.

A spectrum sharing scheme is proposed for overlaid wireless networks based on the two-path successive relaying technique. In this scheme, two secondary transmitters are used to alternately transmit secondary information to their respective receivers whilst relaying the primary signal at the same time. By using superposition coding at the secondary transmitters and successive decoding at the receivers, a diversity gain of 2 is obtained for the primary system without losing the bandwidth efficiency. Outage probabilities of the primary system and secondary system are analyzed. The optimal power allocation factor of the secondary system is determined to minimize the outage probability of the secondary system while maintaining the performance of the primary system. Numerical results show that the proposed scheme can indeed enhance the outage performance of the primary and secondary systems simultaneously.

In this paper, we propose two uncoordinated cooperative truncated ARQ schemes based on node position or local SNR information. If the original transmission between a source and a destination fails, the source and all the potential relays that have correctly received the data packet will contend for the channel to retransmit the packet without coordination. The competition for the channel access is governed by the retransmission probability which is computed in a distributed fashion using the stochastic geometry theory. Theoretical system success probabilities of both schemes are derived. Compared with the scheme in which only the source retransmits, the uncoordinated schemes are shown to achieve better performance, particularly when the distance between the source and the destination is large.

In traditional cooperative communications, coordination is required among relay nodes to help data transmission through distributed signal transmission or coding techniques. For large cooperative networks, the overhead for coordination is huge and the synchronization among relays is very difficult. In this paper, we propose uncoordinated cooperative communication schemes in a large wireless network that do not need the coordination among relays while realizing cooperative diversity for the source-destination link. Without a central controller, all relays that are spatially randomly placed contend for the channel to relay the packet from the source to the destination in a distributed fashion. The competition for the channel access is governed by the retransmission probability that is independently calculated by the relays according to the location or channel quality information. Three schemes of uncoordinated cooperative communications are proposed to determine the retransmission probabilities of the potential relays based on the local distance, direction, and channel quality, referred to as distance based, sectorized, and local SNR based schemes, respectively. Success probabilities for the proposed uncoordinated schemes are analyzed. Numerical and simulation results show that the local SNR based scheme has the best performance and the distance based scheme outperforms the sectorized scheme.

Based on the selection decode-and-forward cooperative protocol, a novel distributed quality of service (QoS) aware routing algorithm is proposed to minimise the total energy consumption of the wireless links from the cross-layer design perspective. For the non-cooperative and cooperative truncated automatic repeat request schemes, performance of packet error rate with M-PSK and M-QAM symbols over Nakagami-m fading channels is first analysed, and then system throughput is derived. Subsequently, transmission power is optimally allocated while satisfying the end-to-end throughput requirement. With polynomial complexity, using the traditional distributed shortest path algorithm, the route that includes a cascade of non-cooperative and cooperative building blocks and has the minimal total link cost (defined as the average total consumed power) is constructed to bear the information flow. In contrast to the previous non-cooperative routing schemes, this cooperative routing algorithm can significantly reduce the total energy consumption.

In this paper, we propose a novel link-utility-based cooperative MAC (LC-MAC) protocol for wireless multi-hop networks. By fully utilizing the broadcast nature of wireless multi-hop networks, the node that has overheard data packets in the previous hop may become a partner of the transmitter in the current hop. As diversity gain can be achieved by virtual antenna array formed by transmitter and partner, one-phase cooperative transmission is introduced to improve the throughput. In LC-MAC, based on the instantaneous channel measurements, each node tries to maximize its own link-utility (indicator of a node's ability to cooperate) by jointly adjusting transmission rate and power. Subsequently, distributed backoff procedure is activated to select the best node that has the maximum link-utility. The optimal transmission type, rate and power are uniquely determined by the best node. Since only local information is required, LC-MAC is a completely distributed protocol. Finally, extensive simulations are performed to investigate the impact of scenario and protocol parameters on the performance of LC-MAC. Numerical results show that LC-MAC significantly outperforms the cooperative relay-based rate adaptation (CRBAR) scheme and the receiver-based rate adaptation (RBAR) scheme in terms of throughput and energy efficiency.

Cooperative diversity has recently been proposed as a promising technology to achieve spatial diversity in wireless networks. In this paper, we analyze the performance of SNR-based hybrid decode-amplify-forward (HDAF) relaying cooperative diversity networks over independent non-identical flat Rayleigh fading channels with maximum ratio combining (MRC) technique. Closed-form expressions for the outage and bit error probability of the HDAF relaying scheme are derived. Computer simulations are carried out to illustrate and validate the correctness of analytical results. Besides, the impacts of the SNR threshold and relay location on the performances of outage and bit error probability are investigated.

We analyze the symbol error probability (SEP) performance of cooperative diversity networks with opportunistic decode-and-forward (ODF) relaying. Assuming that channels suffer from independent nonidentical Rayleigh fading and symbols are M-PSK modulated, the approximate closed-form SEP expressions are derived for opportunistic relaying with adaptive and fixed DF protocols, respectively. Simulations are carried out to validate the theoretical analysis. Results show that the derived approximate SEP is tight particularly at medium and high SNR.

Lifetime extension is a key design issue for wireless sensor networks (WSN) with battery-operated nodes. Compared with direct transmission, transmission power can be significantly reduced by cooperative communication, because it can effectively mitigate multi-path fading by introducing space diversity. In this paper, BER analysis for M-PSK and M-QAM modulation in noncooperative and cooperative situations is firstly presented. Then power is optimally allocated to source and relay nodes with the objective of minimizing the total transmission power under the average BER constraint. Based on these works, a new distributed cooperative MAC protocol is proposed to improve the lifetime of WSN. In this protocol, both channel state information (CSI) and residual energy information (REI) of sensor nodes are considered to choose the cooperative nodes. Simulation results show that when the access point (AP) is above a certain height, the proposed cooperative protocol can significantly prolong network lifetime compared with non-cooperative MAC protocols.