The pervasive threat of jamming attacks, particularly from adaptive jammers capable of optimizing their strategies, poses a significant challenge to the security and reliability of wireless communications. This paper addresses this issue by investigating anti-jamming communications empowered by an active reconfigurable intelligent surface. The strategic interaction between the legitimate system and the adaptive jammer is modeled as a Stackelberg game, where the legitimate user, acting as the leader, proactively designs its strategy while anticipating the jammer’s optimal response. We prove the existence of the Stackelberg equilibrium and derive it using a backward induction method. Particularly, the jammer’s optimal strategy is embedded into the leader’s problem, resulting in a bi-level optimization that jointly considers legitimate transmit power, transmit/receive beamformers, and active reflection. We tackle this complex, non-convex problem by using a block coordinate descent framework, wherein subproblems are iteratively solved via convex relaxation and successive convex approximation techniques. Simulation results demonstrate the significant superiority of the proposed active RIS-assisted scheme in enhancing legitimate transmissions and degrading jamming effects compared to baseline schemes across various scenarios. These findings highlight the effectiveness of combining active RIS technology with a strategic game-theoretic framework for anti-jamming communications.

Blockchain plays a crucial role in ensuring the security and integrity of decentralized systems, with the proof-of-work (PoW) mechanism being fundamental for achieving distributed consensus. As PoW blockchains see broader adoption, an increasingly diverse set of miners with varying computing capabilities participate in the network. In this paper, we consider the PoWblockchain mining, where the miners are associated with resource uncertainties. To characterize the uncertainty computing resources at different mining participants, we establish an ambiguous set representing uncertainty of resource distributions. Then, the networked mining is formulated as a non-cooperative game, where distributionally robust performance is calculated for each individual miner to tackle the resource uncertainties. We prove the existence of the equilibrium of the distributionally robust mining game. To derive the equilibrium, we propose the conditional value-at-risk (CVaR)-based reinterpretation of the best response of each miner. We then solve the individual strategy with alternating optimization, which facilitates the iteration among miners towards the game equilibrium. Furthermore, we consider the case that the ambiguity of resource distribution reduces to Gaussian distribution and the case that another uncertainties vanish, and then characterize the properties of the equilibrium therein along with a distributed algorithm to achieve the equilibrium. Simulation results show that the proposed approaches effectively converge to the equilibrium, and effectively tackle the uncertainties in blockchain mining to achieve a robust performance guarantee.

The space-air-ground integrated network (SAGIN) reveals enormous potential towards ubiquitous access with pros-perous applications for future 6G wireless networks, yet the limited energy presents a significant challenge towards the efficient operation of SAGIN. In this paper, we propose to employ an un-manned aerial vehicle (UAV) to approach the ground nodes to help alleviate the computation burden, where the computed results are then forwarded to the satellite for remote use. We formulate the problem to minimize the weighted energy consumption in terms of data offloading, computation, and results forwarding, along with the UAV propulsion energy, while jointly investigating the transmissions, scheduling, computation, and trajectory strategy design. The problem is then decomposed and solved in a block coordinate descent framework. Simulation results demonstrate that the proposed joint optimization scheme effectively reduces the overall energy consumption compared to benchmark approaches.

Over-the-air computation (AirComp) is a promising technique that addresses big data collection and fast wireless data aggregation. However, in a network where wireless communication and AirComp coexist, mutual interference becomes a critical challenge. In this paper, we propose to employ an unmanned aerial vehicle (UAV) to enable integrated communication and AirComp, where we capitalize on UAV mobility with alleviated interference for performance enhancement. Particularly, we aim to maximize the sum of user transmission rate with the guaranteed AirComp accuracy requirement, where we jointly optimize the transmission strategy, signal normalizing factor, scheduling strategy, and UAV trajectory. We decouple the formulated problem into two layers where the outer layer is for UAV trajectory and scheduling, and the inner layer is for transmission and computation. Then, we solve the inner layer problem through alternating optimization, and the outer layer is solved through soft actor–critic-based deep reinforcement learning. Simulation results show the convergence of the proposed learning process and also demonstrate the performance superiority of our proposal as compared with the baselines in various situations.

The development of the next generation ubiquitous network puts forward higher requirements for the connection density in the communication network, e.g., massive IoT and UAV swarm, which has led to a lot of research on link management. With the expansion of network scale, the weaknesses of existing algorithms in computing efficiency, performance, and realizability have become prominent. The emerging graph neural network (GNN) provides another way to solve this problem. In this paper, we design a cross-receptive distributed GNN structure from the perspective of communication system, combining measurable index of the actual scene with message passing frame. This new GNN structure and the additional input feature dimension work together to provide richer and more comprehensive information for network training. After the initial deployment of the power decision from GNN, we select some links to shut down and others to reduce their transmit power to further improve system performance and save energy. Simulation results show that our proposed method reaches 83.1% performance of the centralized mechanism. In addition, the discussion on scalability suggests that in order to save training cost, small-scale scenes with the same density can be selected for training in the application of large-scale scenes.

Studying the characteristics of the satellite-toground (S2G) channel model is essential for the development and assessment of satellite communication systems. In this study, we introduce a unique approach by combining a low-earth-orbit (LEO) random geometric satellite channel model with line-of-sight (LoS) and single-bounced (SB) non-line-of-sight (NLoS) components for the S2G multiple-input multiple-output (MIMO) channel. By utilizing the coaxial cylinders reference model in a lightly shadow environment occluded by terrain features, we compute the space correlation function (SCF) and time correlation function (TCF). To simplify the simulation process, we present a deterministic simulation model using the finite number of scatterers and analyze the various factors that influence channel characteristics. The findings from the simulation indicate that the orientation of both the satellite and terrestrial receiver antennas, as well as their respective movement directions, distances, and the density of scatterers’ azimuth angles, all play a significant role in shaping the statistical properties of the channel model.

To facilitate UAV-based wireless communications, a comprehensive understanding of the wireless channel characteristics is critical. However, most existing studies focuse on the high-altitude channel, while limited attention is paied to the ultra-low altitude channels given the evident difficulties in the measurement of the latter scenario. In this paper, we have tackled the challenges of lightweight channel sounder system design enabling aerial mounting and flying to facilitate the ultra-low altitude UAV channel measurement. We specially focus on the ultra-low altitude channel at 1.4 GHz, which is recently authoried in China to particularly facilitate UAVs applications. With extensive measurements in a riverside area, we analyze the propagation properties and enlighten the large-scale fading and small-scale fading characteristics, where the large-scale parameters are revealed and the Log-logistic distribution is evaluated as the best fit to characterize the small-scale fading. These findings provide important guidance for the ultra-low altitude UAV communication particularly in the riverside scenarios.

In blockchain systems characterized by computation competition, allocating computation resources is of paramount significance for the economic benefits of nodes. Besides, uncer-tainties of computation resources also affect the node's profits. In this paper, we address the computation resource allocation issue within a proof-of-work (PoW) blockchain system without exact information on the available resources, which impedes the direct investigation of the maximum mining profit. Correspondingly, we establish the chance-constrained threshold for maximum achievable profit through the blockchain in an uncertain environment and maximize this threshold under a given outage probability. Particularly, the uncertain computation resource is modeled only with its first and second statistics, which lack the exact distribution information. In this respect, we propose the distributionally robust approach to tackle the chance-constrained resource allocation strategy, which guarantees the intended profit threshold regardless of the actual distribution. We show that the considered problem admits a conditional value-at-risk (CVaR) approximation reformulation, which can be handled by alternately optimizing the resource allocation strategy and the profit threshold. Simulation results demonstrate that the proposed design is robust against the uncertainty distribution, and effectively guarantees the profits of miners.

Satellite communication is considered as one of the key technologies to achieve global seamless coverage and has attracted wide attention. It is crucial to establish an accurate clutter loss model for satellite-ground communication. Clutter loss refers to the extra path loss caused by the obstruction of the terrain and objects on the ground, especially when a satellite has low elevation angle. The clutter loss model proposed by ITU-R P.2108-0 only considers the influence of the elevation angle, and the traditional prediction model for the clutter loss is limited in accuracy and stability. In this work, we used a satellite ground station to carry out the channel measurement at 8.25 GHz on the clutter loss of the X-band satellite-to-ground (S2G) links in the suburban campus environment, and extracted the clutter loss data set involved in the process of satellite inbound and outbound from the received signal strength. We utilize the multi-layer perceptron (MLP), long short-term memory (LSTM), and bidirectional-long short-term memory (Bi-LSTM) neural networks to build channel models to predict the clutter loss based on the measurement data. The model prediction results show that the Bi-LSTM-based model has higher prediction accuracy than the MLP-based and LSTM-based models.

The unmanned aerial vehicle (UAV) communication with the multiple-input–multiple-output (MIMO) system has attracted lots of attention to improve spectral efficiency and channel capacity. The large antenna spacing is often required to reduce the spatial correlation between subchannels. However, this requirement cannot be well satisfied in practical scenarios due to the limited dimension of UAV platforms. Therefore, polarization diversity is a promising approach for UAV MIMO systems. In this work, we propose a three-dimensional (3-D) geometrical-based polarized model for UAV-correlated MIMO channels. By utilizing the geometrical theory of polarization, we describe the channel depolarization caused by the terrestrial scattering environment based on the multicylinder geometrical model and then acquire the channel polarization function and polarized channel impulse response (CIR). Furthermore, we investigate the impact of the key factors, such as the UAV tilt rotation, cross-polarization, and limited antenna spacing on the UAV MIMO channel capacity. To validate the proposed UAV channel model, we compare the spatial correlations of the numerical simulation and UAV field measurement results for the co- and cross-polarized channels, and the close agreements between them are observed. This work can provide useful guidance and support for the design and performance evaluation of UAV communication networks.

Unmanned aerial vehicles (UAVs)-enabled multicasting network has attracted significant attention in recent years. However, there are still some disadvantages of existing multicasting schemes used in these systems, such as low transmission efficiency and high feedback overhead. Accordingly, we propose a sliding coding window (SCW)-based random network coding (SCWRNC) scheme for a UAV multicasting network where one UAV base station is dispatched to the multicast data stream to multiple user equipments (UEs). The proposed scheme includes an SCW scheduling original packets for encoding, a lower triangular coding structure enabling UEs to decode out information even without receiving a full set of coded packets, and a feedback-compete mechanism requiring only one UE to send feedback information. The packet scheduling process is described as a five-tuple Markov decision process. Then, we give a theoretical analysis of the proposed scheme, based on which the sliding steps of SCW and the UAV hovering location are jointly optimized to maximize the system throughput. The optimal sliding steps are obtained by applying the Greedy scheduling technique, while the UAV optimal position is obtained by minimizing the maximum outage probability of all UEs. Furthermore, we also propose a flexible feedback mechanism, which enables more than one UE to send feedback for systems with sufficient resources and a “F-SCWRNC” scheme for systems where no UE is allowed to send feedback. Numerical results show that both the proposed SCWRNC scheme and F-SCWRNC scheme could achieve significant throughput gain over the existing ones.

In this paper, we consider a UAV-enabled two-way relaying system where the UAV relay assists the information exchange between two ground users (GUs). The two-slot physical network coding scheme (PNC) for information exchange is adopted. The rate region of this scheme in this UAV-enabled relaying system is firstly analyzed. Then, we maximize the system average sum rate under this scheme by jointly optimizing the time resources allocation, transmission powers of the transceivers, and the UAV trajectory subject to the UAV mobility constraints and the information causality constraints. The formulated problem is a nonconvex optimization problem which is hard to solve directly. We propose an iterative algorithm by applying the successive convex approximation and block coordinate descent techniques to solve this problem. Specifically, the time resources allocation, transmission powers and the UAV trajectory are alternatively optimized in each iteration. In addition, the non-convex trajectory optimization problem is solved by successively solving an approximate convex optimization problem. To gain more insights, we also investigate the effects of traffic pattern, which is defined as the ratio between the traffic in two directions, on the system performance by considering a new traffic pattern constraint. Numerical results show that the proposed relaying scheme with moving relay can achieve great throughput gains as compared to the conventional scheme with static relay.

Unmanned aerial vehicles (UAVs) have played an important role in wireless communications due to the advantages, such as highly controllable mobility in 3-D space, swift deployment, Line-of-Sight (LoS) aerial–ground links, and so on. In this article, we consider a UAV-enabled two-way relaying system, where the UAV relay assists the information exchange between two ground users (GUs) under three different schemes, i.e., four-slot multihopping (FSMH) without network coding (NC), three-slot NC, and two-slot physical NC (PNC). First, the capacity region of each scheme in this relaying system is analyzed. Then, we maximize the system average sum rate by jointly optimizing the time resource allocation, transmission powers of the transceivers, and the UAV trajectory subject to the constraints on UAV mobility and information causality under each scheme. To solve those problems, we propose an iterative algorithm by applying the successive convex approximation and block coordinate descent techniques. Specifically, the time resource allocation, transmission powers, and the UAV trajectory are alternatively optimized in each iteration. In addition, the nonconvex trajectory optimization problem is solved by successively solving an approximate convex optimization problem. To gain more insights, we also investigate the performance of those three schemes with symmetric and asymmetric traffic, respectively, by introducing a new traffic pattern constraint. Numerical results show that the proposed relaying schemes with moving relay can achieve great throughput gains as compared to the conventional scheme with static relay. The three relay schemes also show great performance heterogeneity under different traffic patterns.

UAV-based base station (UBS) has played an important role in the air-ground integration network due to its high flexibility and nice air-ground wireless channels. Especially in Internet of Things (IoT) services, UBS can provide an efficient way for data collection from the IoT devices. However, due to the continuous mobility of UBS, the communication durations of devices in different locations with the UBS are not only time-limited, but also vary from each other. Therefore, it is a challenging task to analyze the throughput performance of the UAV-based IoT network. Accordingly, in this paper, we consider an air-ground network in which UAV flies straightly to collect information from the IoT devices based on CSMA/CA protocol. An analytical model analyzing the performance of this protocol in the network is proposed. In detail, we set up the system model for the network, and propose a new concept called quitting probability. Then, a modified Markov chain model integrating the quitting probability is introduced to describe the transmission state transition process and an accurately theoretical analysis of saturation throughput is given. In addition, the effects of the network parameters are discussed in the simulation section.

Random network coding (RNC) is an efficient coding scheme to improve the performance of wireless networks. However, when the users move randomly in the network, the transmitting distance between the transceivers changed randomly, causing the time-varying channel conditions. To fit this situation, in this paper, we considered the heterogenous wireless channels of the users caused by user movement and analyzed the energy consumption of overall network. Then, we introduce the Kalman filter to predict the locations of the users and proposed a new power control metric that both considered the energy consumption and the network throughput. Based on the new metric and the Kalman prediction, we optimized the transmitting power of the BS and proposed an energy-efficient ARNC scheduling to improve quality of service. The simulation results also showed the effectiveness of the optimization and proposed methods compared with the traditional methods.

As industrial production gradually becomes more intelligent, 5G technology has great application prospects in industrial environments. However, the channel in the industrial environment is different from the channel in the other scenarios, and hence need to be characterized specifically. In this paper, a measurement campaign and modeling of industrial scenarios are presented. And we study the main three frequency bands of 3.5GHz, 4.9GHz and 5.8GHz in Sub-6G. Then we model the path loss to fill the gaps in measurement modeling currently in large indoor industrial scenarios. At the same time, we also modeled and analyzed the shadow fading. The results show that the shadow fading in the industrial environment conforms to the lognormal distribution.

Random network coding (RNC) is an efficient coding scheme to improve the performance of wireless multicast networks, on the premise that a receiver is able to collect a full set of network-coded packets. However, in resource (time, bandwidth, and so on) constrained communications, a receiver may only receive a partial set of network coded packets, leading to poor system performance. On the other hand, device-to-device (D2D) communications utilize user proximity and spatial diversity to improve the communication efficiency. In this paper, we propose a hybrid multicast and D2D transmission scheme based on adaptive RNC (ARNC) to increase network throughput under packet erasure channels. In the proposed scheme, the packet encoding structure is optimized adaptively according to the network status, such that even if only a partial set of the coded packets are received, the user equipments (UEs) can still decode useful information and regenerate new encoded packet for D2D communications. In particular, the multicast mode and D2D mode are switched dynamically during a scheduling session according to the status of each UE. Under this hybrid mode, we can effectively overcome the effect of erasure channel and improve the overall network throughput. By comparing our scheme with other scheduling methods, we provide simulation results to corroborate the effectiveness of the proposed techniques.

Nonorthogonal multiple access (NOMA) exhibits superiority in spectrum efficiency and device connections in comparison with the traditional orthogonal multiple access technologies. However, the nonorthogonality of NOMA also introduces intracell interference that has become the bottleneck limiting the performance to be further improved. To coordinate the intracell interference, we investigate the dynamic user scheduling and power allocation problem in this paper. Specifically, we formulate this problem as a stochastic optimization problem with the objective to minimize the total power consumption of the whole network under the constraint of all users' long-term rate requirements. To tackle this challenging problem, we first transform it into a series of static optimization problems based on the stochastic optimization theory. Afterward, we exploit the special structure of the reformulated problem and adopt the branchand-bound technique to devise an efficient algorithm, which can obtain the optimal control policies with a low complexity. As a good feature, the proposed algorithm can make decisions only according to the instantaneous system state and can guarantee the long-term network performance. Simulation results demonstrate that the proposed algorithm has good performance in convergence and outperforms other schemes in terms of power consumption and user satisfaction.

It is known that random network coding (RNC) can be used to improve the performance of wireless multicast networks. However, in the delay sensitive applications, no useful information can be recovered if a user cannot collect a full set of the encoded packets. This becomes more severe for multicast hard deadline constrained prioritized data because of the delivery time limitation and packet interdependency. Meanwhile, the performance of a multicast network is also limited by its bottleneck user(s) due to the heterogeneity of the underlying physical channels. Accordingly, we propose a cross-layer transmission scheme that utilizes beamforming at physical layer and adaptive RNC (ARNC) at network layer to maximize the overall network throughput. Under this joint-design, a smart antennas array that operates the beamforming is used to dynamically allocate the transmitting power among users, and the ARNC is adopted to achieve the network coding gain. In this way, the performance of each user is well balanced and the overall network throughput is increased observably. Furthermore, we propose a sample-based feedback scheme to reduce the system overhead. The analytical and simulation results are shown that the throughput of the network has increased by 30%~40% under our schemes compared with other RNC-based schemes.

Tag collision occurs easily when the multiple tags respond to the same reader query simultaneously over a shared wireless channel. The solution of tag collision is a significant issue in the mobile RFID environment, because of the random mobility of tags. For the purpose of object tracking, locating and monitoring, the reader will repeatedly identify the same tags which still stay within its read range, which will waste a lot of resources and add some new collisions. To handle the above case well, we propose a tag anti-collision protocol based on adaptive ternary query splitting (ATQS), which can effectively avoid the collisions between the new arriving tags and the original staying tags by the blocking technique. Using the formula for the conversion of binary to ternary, the number and length of queries are simplified by a ternary query tree. Using the ternary resolution technique, the new processing procedure of tag identification is designed, in which the reader can use only one prefix of ID to identify at most three staying tags having the same prefix. From the simulation and performance analysis, it obtains that our protocol can effectively decrease the identification delay, which outperforms the previous protocols with the impacts of the staying ratio, the arriving radio and the number of tags.

Network coding offers a promising platform for multicast transmission to improve the throughput. However, the users may not collect a full set of the encoded packets to recover the useful information because of its block processing property, leading to the low throughput under wireless fading channel. While the schemes that using feedback from all the receivers to cover the problem above always causes the huge feedback overhead in large network. Accordingly, in this paper we proposed an adaptive sliding window random network coding(ASWRNC) that introduce RNC with lower triangular coefficient matrix and sliding window to achieve higher network throughput and lower feedback overhead. Particularly, we introduce an adaptive encoding scheme that can let the user decode out partial original packets without collecting the full set of encoded packets. On the other hand, the window sliding scheduling helps the user with better channel decode out more original packets. Further, we discuss in detail the feedback-compete mechanism to decline the feedback overhead.

Random network coding (RNC) is an efficient coding scheme at the network layer to improve the performance of the wireless multicast networks. However, the full set of encoded packets may not be collected to correctly decode the original information in wireless fading channels, leading to the low network throughput. This issue will be more severe in multimedia transmission. On the other hand, cooperative transmission scheme that fully utilizes the space diversity can effectively mitigate channel impairments. Accordingly, we proposed an all-user cooperation scheduling scheme based on the adaptive random network coding to improve the transmission efficiency in wireless networks with fading channels. The cooperative and adaptive random network coding (CARNC) scheme schedules one user as a relay to help the transmitter by maximizing the immediate network throughput based on the packet reception status of all users. This cooperation can effectively repair the encoded packet set due to deep channel fading and improve the throughput of the whole network. Extensive simulations have been conducted to evaluate and compare the performance of the proposed CARNC and other scheduling schemes. The results have shown that CARNC has a better performance in the whole network throughput due to the utilization of both adaptive coding and user cooperation.

In wireless multicast multimedia streaming, every data block (i.e., a group of packets) needs to be delivered within a hard deadline. On the other hand, to provide multilevel services, the widely adopted scalable video coding (SVC) prioritizes a video bit stream into a base layer and several enhancement layers. In this paper, we combine SVC with the transmission delay constraint and focus on multicasting hard deadline constrained prioritized (HDCP) data over wireless erasure channels. Specifically, we first define the performance metric and formulate the optimization problem. Then, we focus on a new adaptive RNC encoding and scheduling scheme to maximize the effective network throughput. Particularly, we propose simple and efficient greedy scheduling technique, which not only obtains near-optimal performance with low-time complexity but also suits time-varying fading channels. In addition, we extend our discussion to the more practical scenario with erroneous feedback and propose sampling-based feedback mechanism to reduce the system overhead. Both analytical and simulation results are provided to corroborate the effectiveness of the proposed techniques.

Random liner network coding (RLNC) is an efficient coding scheme to improve the performance of the wireless multicast networks. However, traditional RLNC comes with a sacrifice in service delay because if the users are not able to collect a full size of the encoding packets, the useful information can not be recovered under the wireless fading channel. This effect is more severe for transmitting the hard deadline constrained prioritized (HDCP) data due to the limited delivery time and data interdependencies. On the other hand, relay scheme provides a useful way to overcome the fading problem in wireless network. Accordingly, we propose a BS-relay competitive scheduling algorithm (BRCS) which introduced adaptive RLNC in the rely-aided wireless network to deliver the HDCP data, to overcome the problem above. BRCS fully considered the property of the multimedia data and design a scheduling scheme, in which BS and relay compete with other to choose the best encoding packet for transmission so as to maximize the network throughput for the given hard deadline. Moreover, to show the performance of BRCS, we compared with other scheduling schemes and the simulation results are provided to corroborate the effectiveness of the proposed technique.

In dense wireless local area networks (WLANs), the hidden stations (HSs) cause severe collisions and performance degradation. Although cooperative communications can achieve spatial diversity, how to efficiently cooperate in a wireless network is a challenging issue due to the distributed nature of the stations. The contributions of this paper are two-fold. First, we establish an analytical model for the IEEE 802.11 WLANs with HSs using the mean value analysis method, which can provide the theoretical results of collision probability, frame service time and network throughput. Second, we propose a new medium access control (MAC) protocol, named network coding cooperative MAC (NCC-MAC) to utilize cooperation to relieve the HS problem. Different from the traditional RTS/CTS scheme, NCC-MAC utilizes random linear coding (RLC) to realize opportunistic cooperative transmission without extra control messages. Furthermore, the ACK-triggering mechanism is introduced which can help to avoid repeated collision caused by HS(s) in dense WLANs and also improve the efficiency of the coded cooperation. Simulations have been conducted to compare the performance of NCC-MAC with DCF, simple cooperative MAC (SC-MAC) without network coding, and other cooperative MAC in the literature. The results show that NCC-MAC can effectively realize station cooperation and reduce the effect of HS(s), improving the network throughput and delay performance considerably.

In this paper, we consider a random wireless sensor network where the sensor nodes are distributed randomly and form clusters to cooperatively transmit data packets using cooperative multi-input-multi-output (CMIMO) technique. We focus on analyzing the overall system performance in terms of packet error rate (PER). And then propose a new node sleep strategy to minimize the overall energy consumption. We take the random distribution of sensor nodes into consideration and get a closed form of overall PER expression that simplifies the overall energy consumption optimization. We analyze the tradeoff between the overall energy consumption and sensor nodes sleep rate. The simulation results corroborate our analysis and show that the proposed sensor node sleep strategy has significant energy saving compared with other transmission schemes.

A distributed localization method has been proposed recently based on a novel concept of angle of departure (AOD) to estimate the angle information and achieve localization. Based on the AOD estimation, we introduce a synthetic uniform circular array (SUCA) synthesized by a rotary antenna to get the distributed localization of the sensors in multi-path environments. The rotated operation of antenna can create the SUCA with a larger aperture to deal with AOD of multi-path components (MPCs) and relaxes the condition that array aperture should be greater than the number of MPCs. This method only relies on radio transceivers without requiring extra measuring equipments, and the sensor nodes conduct localization in a completely distributed manner instead of centralized processing.

This paper considers random wireless sensor networks where nodes are distributed randomly and form clusters to transmit the packets to relay clusters using cooperative multi-input-multi-output (CMIMO) technique. First, we study CMIMO energy consumption and divide it into intra-cluster energy consumption and inter-cluster energy consumption. Then we focus on analyzing the system performance in terms of overall packet error rate (PER) and energy consumption. Based on system performance metrics, we propose a new node sleep strategy, where overall energy consumption is minimized by coordinating inter-cluster transmitting energy consumption and the number of sensor nodes in activation. Compared to existing works, our contributions are (1) we consider random distribution of nodes and get a closed-form expression of overall PER that simplifies the energy minimization problem; (2) we analyze the tradeoff between overall energy consumption and nodes active rate. In simulation, we investigate the relationships among nodes active rate, inter-cluster transmitting energy consumption and the overall energy consumption. Furthermore, we discuss how these parameters are affected by the nodes density, the inter-cluster transmitting distance, the cluster radius and the intra-cluster energy consumption. Finally, we validate that the proposed nodes sleep strategy has significant energy savings compared with non-sleep cooperative transmission and direct transmission.

In this paper, we propose an energy-efficient cooperative geographic routing (ECGR) to reach energy-efficient routing in wireless sensor networks(WSN). ECGR fully takes advantage of cooperative diversity and geographic routing. At physical layer, the sensor nodes cooperatively transmit the same packet to achieve farther transmission distance of each hop. While at the network layer, ECGR adaptively selects the appropriate cooperative nodes to form forwarding clusters to forward the packet when we consider energy consumption and geographic information of the sensor nodes. In order to fully assess the energy consumption, we take circuit energy consumption of the sensor nodes into consideration. According to the description above, we increase the transmitting distance of each hop but decrease the hop number. Meanwhile, the overall network energy consumption is reduced and balanced among nodes to extend the life of the network.

In this paper, an energy-efficient cooperative geographic routing (ECGR) is proposed for Wireless Sensor Networks(WSN). ECGR fully takes advantage of cooperative diversity and "multi-sensor diversity" to reach energy-efficient routing. At physical layer, thanks to the high redundancy of the nodes in WSN, the nodes that have correctly decoded the packet are selected to realize "multi-sensor diversity" gain, and these nodes form a new forwarding cluster in a distributed way to obtain transmit diversity. At the network layer, based on the geographic routing, ECGR makes the packet always rout toward the destination and avoids diverging routes, simultaneously. In this way, we increase the transmitting distance of each hop, meanwhile, the overall network energy consumption is reduced and are balanced among the sensor nodes to extend the life of the network. We also discuss an appropriate packet error rate (PER) to optimize average transmitting distance.

In this paper, we present a framework to analyze the outage performance of the cooperative systems exploiting multiuser diversity (MUD). A two-hop wireless network consisting of a destination node, a relay node and multiple source nodes is considered. The asymptotic expressions of the system outage probability are derived for amplify-and-forward (AF) and decode-and-forward (DF) protocols. From these expressions it is shown that the diversity orders of K+1 and K can be achieved for AF and DF, respectively, where K is the number of source nodes in the network.

The opportunistic cooperation schemes, where only the “best” relay is selected to forward the message, are well performed in outage probability but with poor fairness among relays. In this paper, we introduce the concept “outage priority based fairness” (OPF), aiming at improving the fairness appropriately without outage performance deterioration. A novel cooperation scheme is proposed to meet the concept and theoretical analysis is provided further. Then, based on OPF, the achievable upper bound of the fairness is derived and an optimal cross-layer designed scheme is also provided to achieve the bound. Numerical simulations are carried out finally, which validate the correctness of our analysis and show that in the proposed schemes, the fairness among all relays is greatly improved especially in high SNR regime, without any loss of outage performance.

In this paper, we propose a family of space-time block codes (STBC) that achieve full diversity when linear receivers, such as zero-forcing (ZF) or minimum mean square error (MMSE) receivers, are used. Our proposed STBC family is a combination/overlay between orthogonal STBC and Toeplitz codes, which can be viewed as a generalization of overlapped Alamouti codes (OAC) and Toeplitz codes recently proposed in the literature. Simulation results show that the newly proposed STBC may outperform the existing codes when linear receivers are used.

In this paper, we propose a simple analog network coding (ANC) scheme for asynchronous two-way relay networks where two sources exchange information through the relay nodes. In our proposed scheme, we consider a wireless scenario with frequency selective fading channels. Orthogonal frequencydivision multiplexing (OFDM) is implemented at the two sources and then the OFDM blocks are transmitted to the relay nodes in the first time slot. In the second time slot, at the relay nodes, the ANC that has a few simple operations are implemented on the received mixed signals and then the processed signals are broadcasted to the two sources to finish the information exchange. In this scheme, at each source, when recovering the designed information transmitted from another source, the received signals have the orthogonal space time block code (OSTBC) structure or the quasi-orthogonal space time block code (QOSTBC) structure on each subcarrier. By a proper power allocation, the two sources can achieve full spatial diversity and fast ML decoding without the requirement of symbol level synchronization. By the repetition across the subcarriers, multi-path diversity available in frequency selective fading channels can be also exploited. Moreover, the proposed ANC scheme is also valid for multi-way relay networks where multiple sources exchange information.

In this paper, we propose a design scheme for a family of space-time block codes (STBCs) that achieve full diversity when linear receivers, such as zero-forcing (ZF) or minimum mean square error (MMSE) receivers, are used. New STBCs obtained based on this design scheme are characterized by their special structure that combines/overlays orthogonal STBCs (OSTBCs) and Toeplitz codes together, which are called Group Orthogonal-Toeplitz Codes (GOTCs). By choosing different parameters, codes with different symbol rates and orthogonality can be obtained. It is indicated that the existing STBCs achieving full diversity with linear receivers, such as the Toeplitz codes and overlapped Alamouti codes (OACs) recently proposed in the literature and the OSTBCs are three members of the GOTCs with different parameters. It is also shown that the time-reversal space-time block codes (TR-STBCs) and delay diversity codes (DDCs) for frequency selective channels are equivalent to OACs and Toeplitz codes, respectively, and thus they both can achieve full space-frequency diversity with only linear receivers. Furthermore, simulations show that with linear receivers, GOTCs outperforming the above mentioned three codes can be obtained from the proposed design scheme.