While stacked intelligent metasurfaces (SIMs) have demonstrated significant technical and cost advantages in multiuser scenarios, existing literature universally assumes ideal transceiver hardware. Addressing this gap, this paper investigates the design and optimization of a SIM-assisted multiuser downlink multiple-input single-output (MISO) system under practical transceiver hardware impairments (HWIs). To accurately capture distortion effects at both the base station (BS) and user equipment, we adopt an aggregate HWI model based on improper Gaussian statistics. The considered impairments include finite-resolution digital-to-analog converters (DACs), power amplifier (PA) nonlinearities, in-phase/quadrature (I/Q) imbalance, and other radio-frequency (RF) front-end non-idealities. We formulate a sum-rate (SR) maximization problem that jointly optimizes digital beamforming at the BS and multi-layer analog beamforming at the SIM. To tackle this highly non-convex optimization challenge, we propose a closed-form-based iterative algorithm that alternately updates BS and SIM beamforming with guaranteed convergence. Extensive simulations validate the effectiveness of the proposed algorithm, quantify the impact of different HWI sources, and demonstrate that SIM deployment significantly improves system robustness, mitigates HWI-induced performance degradation, and reduces DAC resolution requirements without substantial performance loss.

Stacked intelligent metasurface (SIM) extends the concept of single-layer reconfigurable holographic surfaces (RHS) by incorporating a multi-layered structure, thereby providing enhanced control over electromagnetic wave propagation and improved signal processing capabilities. This study investigates the potential of SIM in enhancing the rate fairness in multiuser downlink systems by addressing two key optimization problems: maximizing the minimum rate (MR) and maximizing the geometric mean of rates (GMR). The former strives to enhance the minimum user rate, thereby ensuring fairness among users, while the latter relaxes fairness requirements to strike a better trade-off between user fairness and system sum-rate (SR). For the MR maximization, we adopt a consensus alternating direction method of multipliers (ADMM)-based approach, which decomposes the approximated problem into sub-problems with closed-form solutions. For GMR maximization, we develop an alternating optimization (AO)-based algorithm that also yields closed-form solutions and can be seamlessly adapted for SR maximization. Numerical results validate the effectiveness and convergence of the proposed algorithms. Comparative evaluations show that MR maximization ensures near-perfect fairness, while GMR maximization balances fairness and system SR. Furthermore, the two proposed algorithms respectively outperform existing related works in terms of MR and SR performance. Lastly, SIM with lower power consumption achieves performance comparable to that of multi-antenna digital beamforming.

To meet the emerging demands for rapid data aggregation and reliable information transmission in future wireless applications, a novel system architecture integrating Over-the-Air Computation (AirComp) and downlink multi-user communication via a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is proposed in this paper. In the considered cellular scenario, an unmanned aerial vehicle (UAV) carries a STAR-RIS beneath its fuselage, creating a programmable aerial platform that concurrently serves Internet-of-Things (IoT) devices and conventional mobile users. The STAR-RIS operates in transmission mode to enable efficient wireless data aggregation of IoT devices, while its reflection mode establishes high-quality downlink channels from the base station (BS) to multiple users. Capturing the true electromagnetic behavior of the STAR-RIS, we explicitly model the practical coupling between the reflection and transmission phase shifts. Two optimization problems are then formulated: one minimizes AirComp distortion and the other maximizes the minimum user rate in the downlink. Both non-convex problems are tackled by efficient iterative algorithms derived from the penalty dual decomposition (PDD) framework. Extensive simulations confirm that the proposed design markedly outperforms baseline approaches and its performance can approach that of ideal phase-shift control by enhancing the key system parameters. Additionally, the trade-off between computation and communication performance is demonstrated.

Over-the-air computation (AirComp), as an effective method to wireless data aggregation, has attracted much attention recently. It helps to improve network efficiency and scalability by integrating communication and computation in the air. In AirComp, both signal magnitude misalignment and noise lead to computation error. In the single antenna case, by allowing misalignment in signal magnitude, a good tradeoff can be achieved between signal distortion and noise power, which leads to a minimal error. In the multi-antenna case, usually the zero-forcing policy is used to enforce signal magnitude alignment (no distortion), which, however, increases noise and affects the overall computation error. To better exploit multiple antennas at the sink, in this paper, we propose a misalignment allowed optimization (Miso) method for AirComp. Specifically, a group of nodes whose signals may be misaligned are dynamically selected, and other signals are aligned to a higher level with higher quality. On this basis, the optimization of multi-antenna AirComp is converted to a difference of convex problem and is solved iteratively. Simulations confirm that the proposed method greatly reduces computation error and scales better with the number of nodes, compared with previous methods.

In this paper, we consider an intelligent reflecting surface (IRS) assisted downlink multiuser communication system with improper Gaussian signaling (IGS) that serves as generalized Gaussian signaling and can effectively combat multiuser interference. We focus on the max-min achievable rate optimization problem by jointly optimizing the transmit beamforming vectors and reflecting phase shifts, subject to the transmit power budget constraint at the access point (AP). We propose a low-complexity iterative algorithm based on a two-layer iterative procedure, which differs from these existing algorithms that rely on inefficient alternating optimization framework and high computational complexity convex optimization tools. Specifically, in the outer layer procedure, we employ a tractable lower bound of user communication rate to reformulate the original problem and repeatedly update the lower bound in each iteration. In the inner layer procedure, based on the alternating direction method of multipliers (ADMM), we decompose the reformulated problem into several convex sub-problems, which can be alternately solved by closed-form solutions. Furthermore, we study the initialization, convergence, and computational complexity of the proposed algorithm. Additionally, we simplify the algorithm to make it applicable for the cases of conventional proper Gaussian signaling (PGS) and without IRS. Finally, numerical results validate the advantages of the proposed algorithm over benchmarking algorithm in terms of rate performance and average execution time.

Intelligent reflecting surface (IRS) is considered as an enabling technology for millimeter wave (mmWave) communications since it can provide an extra reflection link to increase the macrodiversity gains and improve the transmission performance. These advantages principally rely on the assumption that the blocking of direct and reflection links is mutually independent. However, due to the non-negligible sizes of blockages in some practical cases, both links may be simultaneously blocked by the same blockage, which is the so-calledblockage correlation. To this end, in this work we investigate the impact of blockage correlation in the IRS-assisted mmWave communication systems. Firstly, we provide a new joint line-of-sight/non-line-of-sight (LOS/NLOS) probability model by considering blockage correlation. The correlation coefficient of direct and reflection link states is also derived. Secondly, we study the effects of blockage correlation on the system performance of IRS-assisted mmWave communication systems by deriving the expression of transmission success probability. Moreover, we provide a deployment optimization algorithm for IRS when blockage correlation is considered. Finally, simulations verify our theoretical results and validate the IRS deployment optimization algorithm. It is shown that the widely-used independent blocking assumption not only always incur an overestimation of the performance of IRS-assisted mmWave communication systems, but also misdirect the optimal deployment of IRS.

Compared to reflective-only or transmissive-only reconfigurable intelligent surface (RIS), simultaneous transmitting and reflecting RIS (STAR-RIS) has the capability to simultaneously serve users on its both sides. Then, simultaneous indoor-and-outdoor communication (SIOC) has been commonly regarded as one of the most evident and promising applications of STAR-RIS in the next generation wireless networks. However, it introduces intricate interference for wireless networks, i.e., transmission and reflection interference, which would impact the network coverage and transmission capacity drastically. Unfortunately, there is a dearth of research investigating STAR-RIS-enabled SIOC from a network-level perspective. To fill the gap, by leveraging stochastic geometry, we provide the first comprehensive analytical framework for STAR-RIS-enabled SIOC networks. We investigate three commonly used STAR-RIS protocols, i.e., energy splitting (ES), time switching (TS) and mode switching (MS), and consider both unicast communication (UC) and multicast communication (MC) schemes. Exact and approximate expressions of transmission success probability and transmission capacity are derived. Furthermore, we also optimize the key parameters of STAR-RIS protocols to maximize the transmission capacity. Finally, via simulations, the accuracy of theoretical expressions is confirmed, the optimization of the key parameters is investigated, and the advantages of STAR-RIS over conventional RISs are also demonstrated. Moreover, we also conclude the recommended STAR-RIS protocols to UC and MC schemes, respectively.

In this paper, we focus on simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted multiuser multiple-input single-output (MISO) interference channels (ICs), where the RIS can serve the users in both forward and backward half-spaces at the same time by transmitting and reflecting incident signals. To suppress the inevitable inter-user interference, we adopt improper Gaussian signaling (IGS) and propose to jointly optimize the beamforming vectors of APs and the passive transmission and reflection coefficients of RIS to maximize the minimum achievable information rate among these users. In order to address the non-convex optimization problem, we provide an efficient iterative optimization algorithm to attain high-quality solutions. Numerical results demonstrate the superiority of IGS over conventional proper Gaussian signaling in STAR-RIS assisted MU-MISO ICs.

IoT systems typically involve separate data collection and processing, and face the scalability issue when the number of nodes increases. For some tasks, only the result of data fusion is needed. Then, the whole process can be realized in an efficient way, integrating the data collection and fusion in one step by over-the-air computation (AirComp). Its shortcoming, however, is signal distortion when channel gains of nodes are different, which cannot be well solved by transmission power control alone in times of deep fading. To address this issue, in this paper, we propose a multi-slot over-the-air computation (MS-AirComp) framework for the sum estimation in fading channels. Compared with conventional data collection (one slot for each node) and AirComp (one slot for all nodes), MS-AirComp is an alternative policy that lies between them, exploiting multiple slots to improve channel gains so as to facilitate power control. Avoiding to obtain instantaneous channel gains of all nodes at the sink is a key point. Specifically, the transmissions are distributed over multiple slots and a threshold of channel gain is set for distributed transmission scheduling. Each node transmits its signal only once, in the slot when its channel gain first gets above the threshold, or in the last slot when its channel gain remains below the threshold. Theoretical analysis gives the closed-form of the computation error in fading channels, based on which the optimal parameters are found. Noticing that computation error tends to be reduced at the cost of more transmission power, a method is suggested to control the increase of transmission power. Simulations confirm that the proposed method can effectively reduce computation error, compared with state-of-the-art methods.

In this paper, we present a unified performance analysis of stochastic clustered cooperative systems with distance-based relay selection, where the destination is located at the cluster center, the locations of the source and the candidate relays follow an independent and identical distribution, and there exists a Poisson field of interferers. As the distance-based relay selection leads to spatial correlation between cooperative transmission distances in a cluster, we first derive a general joint probability distribution function of cooperative transmission distances based on$\psi $-order statistics. Then, the transmission success probability of the stochastic clustered cooperative systems with decode-and-forward (DF) scheme is derived. In order to reduce computational complexity, we offer an approximate expression of the transmission success probability. Furthermore, we provide an asymptotic transmission success probability in the interference-limited scenario, when the number of candidate relays in a cluster is sufficiently large. Besides, the performance analysis framework is extended into the clustered cooperative systems with finite relay distribution regions. Taking the cluster member distributions of Thomas cluster process (TCP) and Matérn cluster process (MCP) as examples, we verify our theoretical analysis via Monte-Carlo simulations. With the theoretical results, the optimization of relay selection parameter is also demonstrated.

We consider a wireless-powered ad hoc network with clustered Power Beacons (PBs). The wireless-powered transmitters (WP-Txs) only activate stochastically deployed PBs within the circles centered at their locations. The PBs transmit radio frequency (RF) signals, which are harvested by the WP-Txs to transmit information to their target receivers. The nodes are modeled as following$\beta $-Ginibre Point Processes ($\beta $-GPP), which incorporate repulsion among PBs or active WP-Txs. Lacking analytical mathematical tools to describe the distribution of clustered PBs, we establish lower and upper bounds on the energy outage probability, as well as an approximated expression of it. We then derive the information outage probability, an approximate expression of it by approximating the reduced Palm distribution of$\beta $-GPP as an inhomogeneous Poisson point process (IPPP), and an analytical approximate expression of it by further ignoring the small-scale fading of interfering WP-Tx links. Finally, the transmission capacity is obtained based on the overall outage probability. The simulation results show that the derived theoretical expressions accurately reflect the performance of the wireless-powered ad hoc networks and the performance gap introduced by approximations is negligible. Transmission capacity optimization based on the derived expressions is also possible and demonstrated.

In this paper, we analyze the energy coverage performance of heterogeneous millimeter wave (mmWave) wireless power transfer (WPT) networks, where macro base stations (MBSs) are distributed according to a Poisson point process (PPP), the location of power beacons (PBs) is modeled as a k-tier Poisson cluster process (PCP), and energy users (EUs) are clustered around the centers of PB clusters. Moreover, the cosine antenna gain model is adopted instead of the prevalent flap-top gain model, which is simpler in derivation but less accurate. Based on the generalized exponential distribution approximation, we propose a new technique of deriving the energy coverage probability of randomly deployed mmWave WPT networks. Specifically, taking the Thomas cluster process (TCP) for instance, we derive the energy coverage probabilities with two PB association strategies, i.e., the random PB association and the nearest PB association. Through Monte-Carlo simulations, our theoretical results are verified and the impact of system parameters, such as the array antenna size, energy threshold or average number of PBs in a cluster, are also investigated.

CSMA is the de facto standard in wireless LANs (WLANs), where one transceiver is used for both the control and data planes. When many nodes contend to access a same channel, the WLAN transceiver performs carrier sense during most time, which wastes much energy. Dynamic adjustment of parameters (contention window) is suggested in previous works for improving network throughput, but this is also realized at the cost of continuously monitoring the channel with large power consumption. To solve these problems, in this paper, we suggest tightly integrating a lower power wake-up radio (WuR) with the power-hungry WLAN module. Specifically, (i) The WuR is used to monitor the channel and conduct carrier sense. It activates the WLAN module for actual transmissions when the channel gets ready. (ii) The WuR is used to measure the inter-frame space, based on which contention window is adjusted accordingly. Extensive simulation evaluations confirm that the proposed scheme (WuR-CSMA) effectively reduces the duty ratio of WLAN modules and improves system throughput, achieving both energy and spectral efficiency compared with the conventional schemes.

In this paper, time-switching based wireless powered relay systems, where the source and destination jointly transfer energy to the relay, is studied. Given a certain transmission duration, there is a time trade-off between wireless energy transfer and information cooperative transmission. Although more time for energy harvesting causes more available power at the relay, it shrinks the duration of information transmission and may incur less system throughput. To obtain the optimal time allocation, we derive the information outage probability of the relay system. And then we solve the optimization problem of time allocation to achieve the maximum system throughput. Finally, simulation results verify our derived theoretical results. And it is also shown that the proposed time allocation scheme indeed achieves maximum average throughput.

In this paper, an amplify-and-forward two-way relay system with unknown carrier frequency offsets (CFOs) is considered. A double differential transmission scheme is proposed to achieve successful two-way relaying transmission without any CFOs information. The average symbol error rate (SER) performance of the proposed scheme is analyzed and a closed-form upper bound of the average SER is derived. Simulation results are provided to validate the proposed scheme.

Wireless energy harvesting has attracted a lot of attention in relay networks, where each relay node can use its harvested energy from the received radio frequency (RF) signals to forward messages. In this letter, we consider a multi-relay system and study the problem on how each relay splits the power of its received RF signal to maximize the information signal-to-noise ratio (SNR) at the destination node. As this problem is non-convex and difficult to be solved, we propose an iterative dynamic power splitting algorithm based on analyzing the first order necessary condition of the optimal solution. Besides, the convergence, initial value and transmission protocol of the proposed algorithm are also discussed. Simulation results show that the proposed iterative power splitting algorithm has nearly the same performance as the optimal exhaustive search and outperforms fixed power splitting schemes.

Power allocation for the multiple-channel cognitive systems is studied in this paper. Our objective is to maximize the throughput of the cognitive link while providing delay guarantee for the secondary user. By using the effective capacity theory, the problem is modeled as effective capacity maximization problem with average power constraint, which turns out to be a constrained convex optimization problem. Through Lagrangian dual theory, the problem is divided into a group of sub-problems, where each sub-problem aims at maximizing the effective capacity under a certain channel sensing result. Then a gradient descent method based algorithm is proposed which solves the problem effectively. Simulation results show that the proposed power allocation scheme can effectively improve the effective capacity for the cognitive link and provide specific delay guarantee in terms of delay violation probability.

Simultaneous wireless information and power transfer (SWIFT) is an appealing solution to balance the energy distribution in wireless networks and improve the energy-efficiency of the entire network. In this paper, we study the optimal policies for SWIPT over the flat-fading channel subject to the co-channel interference. The SWIPT receiver using dynamic power splitting (DPS) is expected to achieve the maximum information rate given an average harvested energy constraint. In the case that channel state information at the transmitter (CSIT) is not available, the optimal power splitting is derived. For the case with CSIT, the joint optimal policy of the transmit power and the power splitting are also provided. Through simulations, we show the optimal rate-energy (R-E) trade-offs of our proposed policies. The case with CSIT achieves better R-E trade-off than the case with no CSIT.

Harvesting energy from the radio-frequency (RF) signal is an exciting solution to replenish energy in energy-constrained wireless networks. In this paper, an amplify-and-forward (AF) based wireless relay network is considered, where the relay nodes need to harvest energy from the source's RF signal to forward information to the destination. To improve the performance of information transmission, we propose two distributed relay selection protocols, Maximum Harvested Energy (MHE) protocol and Maximum Signal-to-Noise Ratio (MSNR) protocol. Then, we derive the outage probabilities of the system with our proposed relay selection protocols and prove that the proposed selection protocols indeed can improve the system performances and the MSNR protocol outperforms the MHE protocol. Simulation results verify the analysis and theorems. In addition, the effects of key system parameters are also investigated via simulations.

Increasing the number of transmit antennas can improve the diversity gain of the system, and accordingly reduce the transmit power dissipation. However, the high circuit power dissipation incurred cannot be ignored. Generalised selection combining, which could provide a certain spatial diversity in the transmit diversity systems, performs a good balance between system performance and practical implementation cost. In this study, the authors propose an energy efficient generalised selection combining (EE‐GSC) scheme which obtains improved transmitter energy efficiency (EE) by providing a best tradeoff between the diversity gain and the circuit power dissipation of multiple antennas. Based on the classical results of order statistics, a theoretical analysis of EE‐GSC performance is carried out in detail over Rayleigh fading channels. Based on this analysis, the average number of active branches as well as the average power dissipation of the proposed scheme is also derived. Numerical results are also given to further illustrate the EE performance of the proposed scheme.

In this study, the authors present a novel low backhaul load cooperative transmission framework for the two‐cell multiple‐input–single‐output systems. In this framework, the neighbouring two base stations (BSs) take turns to transmit data in two consecutive slots. In each slot, only one BS is active, transmitting the preprocessed data symbols of both its own serving user and the cooperative user in neighbouring cell, and sharing its preprocessed data symbols to the other cooperative BS for next transmission. Linear constellation spreading is utilised for preprocessing which helps the system to exploit the macro‐diversity without reducing the multiplexing gain. Besides, zero‐forcing beamforming is applied in each transmission slot so as to cancel the multiuser interference. In this way, the inter‐cell links become beneficial rather than detrimental. Pairwise error probability analysis demonstrates that the multi‐cell spatial diversity gain can be achieved for each data stream. Both theoretical analysis and simulation results confirm that the proposed scheme outperforms the existing relevant strategies with less channel estimation overhead. It is shown that because of the higher diversity order it achieved, the proposed scheme can significantly improve the error performance in a distributed manner while maintaining the same multiplexing gain.

ABSTRACT For amplify‐and‐forward relay networks, we propose an iterative scheme to estimate channel and detect information symbols for the multi‐antenna destination in spatially correlated noise. The equivalent channel coefficients and noise covariance are estimated by expectation–maximization algorithm. In addition, we discuss the initialization of iteration and analyze the modified Cramér–Rao bound to show the performance of the proposed iterative estimation. Moreover, on the basis of the structure of the proposed iterative estimator, a joint channel estimation and detection receiver is also provided. Finally, simulation results show that the proposed channel estimator and receiver can achieve the optimal performances in amplify‐and‐forward relay networks with unknown noise correlation. Copyright © 2012 John Wiley & Sons, Ltd.

Although Orthogonal Frequency Division Multiplexing (OFDM) is an effective way to combat frequency selective fading of the wireless channel, it is very sensitive to frequency shifts. Doppler spreading due to the mobility of communication terminals or reflectors destroys the orthogonality among the subcarriers, and results in intercarrier interference (ICI). In this paper, we propose a multiple Doppler frequency offsets (DFOs) estimation based scheme to diminish the ICI in an OFDM transceiver for communication between a base station (BS) and a high-speed train. The proposed scheme utilizes a particularly designed preamble to estimate the DFOs by single receiving antenna, and preprocesses the received (downlink) OFDM symbol based on the DFOs estimates so that the post signal-to-interference ratio (SIR) gain can be obtained. Numerical results demonstrate that the proposed scheme can effectively obtain the DFOs estimates and achieve an obvious SIR gain when there exists a strong line-of-sight (LoS) path between the BS and the high-speed train.

SUMMARY Dynamic spectrum auction (DSA) has been considered as one of potential spectrum allocation approaches in cognitive femtocell networks. As a modified version of traditional spectrum auction, DSA should not only increase auction revenue but also improve spectrum utilization on finer time granularity. We propose a DSA algorithm based on a double optimization framework (DOF), which focuses on the optimization of auction revenue and spectrum utilization. The optimization processing consists of two stages. Firstly, a proper auction period is selected to balance the expected spectrum utilization and auction revenue. Then, the cognitive femtocell base station adjusts its reserve price with the repetition of auction to leverage over instant revenue and spectrum utilization. At the same time, the bidders can adjust their bidding price to improve utilities. Performance analysis shows that the DOF‐based DSA algorithm has low complexity and can resist collusion, so it can be carried out frequently with small overhead. On the other hand, it is better than the greedy algorithm and Vickrey–Clarke–Groves auction on revenue. Simulation results show that the DOF‐based DSA algorithm can keep a fine spectrum utilization and bring the cognitive femtocell base station more revenue in both single‐unit award spectrum auction and multi‐unit awards spectrum auction. Copyright © 2012 John Wiley & Sons, Ltd.

Due to common interference or noise propagation, noise correlation between relays could occur in Amplify-and-Forward relay networks. To estimate channel coefficient, the noise covariance is required in traditional channel estimations. On the other hand, we also need channel coefficient to estimate the noise covariance. Therefore,traditional channel estimators can not be utilized when noise correlation is unknown. In this paper, we propose an Expectation-Maximization algorithm based iterative channel estimator to solve this problem. Moreover, we analyze the modified Cram'er-Rao bound to show the performance of the proposed channel estimation. Finally, simulation results show that the proposed channel estimator can work well in Amplify-and- Forward relay networks with unknown noise correlation.

Deliberate clipping is a simple solution to high Peak-to-Average Power Ratio problem of OFDM signal. In Amplify-and-Forward OFDM relay networks, due to the limited resource, deliberate clipping is also suggested. However, clipping is a nonlinear process and may cause significant performance degradation. Based on Bussgang Linearization Theory, we provide a linear system model for Amplify-and-Forward OFDM relay networks. To achieve spatial diversity, we design a practical nonlinear distortion aware receiver at the destination. Considering a total relay power constraint, we propose an optimal power allocation scheme to maximize the signal-to-noise distortion ratio. Simulation results show that our optimal relay power allocation scheme can improve the system throughput and resist the non-linear distortion. It is also verified that our proposed transmission scheme outperforms other transmission schemes without considering non-linear distortion.

The time-varying feature of wireless channels usually makes the hard delay bound for data transmissions unrealistic to guarantee. In contrast, the statistically-bounded delay with a small violation probability has been widely used for delay quality-of-service (QoS) characterization and evaluation. While existing research on the statistical-delay control mainly focused on the single-hop links, in this paper we propose the QoS-driven power-allocation scheme over two-hop wireless relay links to statistically upper-bound the end-to-end delay for the decode-and-forward (DF) relay transmissions. Specifically, by applying the effective capacity and effective bandwidth theories, we first analyze the delay-bound violation probability over the two-hop link with independent service process in each hop. Then, we show that an efficient approach for statistical-delay QoS guarantees is to make the delay distributions of both hops identical, which, however, needs to be obtained through asymmetric resource allocations over the two hops. Motivated by this observation, we formulate and solve an optimization problem aiming at minimizing the total power consumptions to satisfy the specified end-to-end delay-bound violation probability over two-hop relay links. Also conducted is a set of numerical results to show the impacts of the QoS requirement, the traffic load, and the position of the relay node on the power allocation under our proposed scheme.

One of the major challenges in the medium access control (MAC) protocol design over cognitive Ad Hoc networks (CAHNs) is how to efficiently utilize multiple opportunistic channels, which vary dynamically and are subject to limited power resources. To overcome this challenge, in this paper we first propose a novel diversity technology called Channel-Aggregation Diversity (CAD), allowing each secondary node to use multiple channels simultaneously with only one data radio per node under the upperbounded power. Using the proposed CAD, we develop a CAD based MAC (CAD-MAC) protocol, which can efficiently utilize available channel resources through joint power-channel allocation while guaranteeing the transmission-time fairness. Particularly, we convert the joint power-channel allocation to the Multiple-Choice Knapsack Problem, such that we can obtain the optimal transmission strategy to maximize the network throughput through dynamic programming. Simulation results show that our proposed CAD-MAC protocol can significantly increase the network throughput as compared to the existing protocols.

Auction based dynamic spectrum access is an efficient approach to solve the spectrum shortage problem. However, conventional spectrum auction algorithms mainly concentrated on maximizing the revenue, under which the dynamic spectrum access (DSA) users with poorer cognitive abilities are usually hard to win the auction for spectrum access. In this paper, we propose a novel waiting-time auction (WTA) algorithm to improve the winning probabilities for the DSA users with poorer cognitive abilities. Specifically, we formulate the spectrum allocation as an auction game. In the auction, all game users bid with waiting time to obtain the spectrum access opportunities, unlike the conventional approaches where users bid by using money. Correspondingly, users with poorer cognitive ability, usually having the lower time cost, can win the auction with higher probabilities through bidding longer waiting time. We prove that there exists a unique Nash equilibrium (NE) in the WTA based spectrum-allocation game and the NE is desirable for all game users. Both theoretical and simulation analyses show that our proposed WTA algorithm can effective improve the winning probabilities of users with lower cognitive abilities.

Spectrum sensing is performed by secondary users (SUs) to detect primary users (PUs) in cognitive radio networks. The limited time duration allocated for spectrum sensing may result in high false alarm probability, which always leads to low spectrum utilization and degrades the SU throughput. To overcome this problem, we propose a novel joint sensingtime adaption and data transmission (JSTA-DT) scheme. In our proposed JSTA-DT scheme, two adjacent sensing periods are bundled to form a sensing block. In each sensing block, the SU performs partial time spectrum sensing in the first sensing period. If the sensing result indicates that the PU is absent, the SU transmits its data; otherwise, the SU performs full time spectrum sensing adaptively. Both theoretical analyses and simulation results show that compared to the conventional partial time spectrum sensing and data transmission scheme, the spectrum utilization and achievable SU throughput are improved significantly by using our proposed JSTA-DT scheme.

At present, the 3rd Generation Partnership Project (3GPP) has agreed on using the high interference indicator (HII) for uplink inter-cell interference coordination (ICIC). HII indicates the potential uplink interference to other cells. In traditional HII based ICIC schemes, cells suffering from different inter-cell interference receive the same HII, it therefore causes ICIC to have a low efficiency. To overcome the shortcomings, a cell-specific and realistic HII is designed firstly. Second, two uplink coordination methods are selected and simplified. Integrating HII and the modified methods, a novel uplink ICIC scheme is proposed to improve the performance of ICIC for 4G Long Term Evolution (LTE) systems. Further, simulation results show that our scheme significantly improves the system's spectral efficiency and reduces the blocking probability of user equipments (UEs) by more than 50% compared with existing ICIC schemes.

In distributed space–time (DST)-coded regenerative relay networks, demodulation error produced by relays degrades the receiver performance significantly. To mitigate this disadvantage, two threshold-based selective relaying schemes are proposed, that is, centralised selecting scheme and distributed selecting scheme, where each relay forwards signals only if its received signal–noise ratio is larger than a threshold. Both proposed schemes can work well with arbitrary modulation constellation and any number of relays and no matter whether the source–destination channel is available or not. Simulation results show both proposed selective relaying schemes outperform conventional schemes significantly and the improvement increases as the scale of relay network grows. Centralised selecting has a slightly better performance than the distributed selecting. However, the latter has a far lower system cost. This contribution provides two useful relaying mechanisms to mitigate error propagation.

A new resource allocation scheme for cooperative transmission over multiuser OFDM networks is proposed in this paper. It is assumed that each user has the ability to relay information for others to improve the overall system performance, operating in decode-and-forward (DF) mode. Sub-carriers and power are allocated to users to maximize the uplink system throughput under the constraints of half-duplex, distributed power, and queue state of traffic. A two-slot resource allocation structure is constructed to deal with the half-duplex constraint. A suboptimal low-complexity algorithm is developed to solve the allocation problem efficiently. This algorithm first greedily allocates subcarriers and power to users and then determines the relay selection. Numerical examples demonstrate the good throughput performance of our proposed scheme and the effectiveness of the low-complexity algorithm.

In this paper, a novel channel estimation scheme called Two-Pilot Estimation (TPE) is proposed for distributed space-time decoding in Amplify-and-Forward (AF) based wireless relay networks. For Maximum Likelihood (ML) decoding at the destination, directly estimating the product of source-relay (S-R) channel and relay-destination (R-D) channel either is difficult, or causes drastic receiver degradation. The TPE scheme employs two pilot space-time codes to jointly estimate the channels. One pilot code is used to experience the source-relay-destination (S-R-D) channels, and another one is assigned for the R-D channel. To reduce computation complexity, Two-Step TPE and LSE-TPE estimators are proposed as the suboptimal TPE estimators. Optimal power allocation between two pilots is also derived. Simulation shows that the TPE scheme is better than the product based estimation schemes.

In this paper, we study optimum SNR threshold based partial decode-and-forward (PDF) schemes where relays may choose to forward the demodulated symbols in form of distributed space-time coding (DSTC) or to remain silent according to instantaneous link qualities of the source to relays. Considering the possible error of Maximum likelihood decoding at intermediate relays, we model a general noise in the received signal-noise ratio (SNR) expression at the destination. First, we analyze a centralized scheme, called Centralized Selecting, where the relaying decisions are based on all average SNRs of the source-relays and relays-destiantion. The SNR threshold vector are calculated in a central way. Then, a distributed scheme, Distributed Selecting, is proposed to let every relay individually make its decision based on two related average SNRs, i.e., source-ith relay and ith relay-destination. We show that both proposed selective PDF relaying schemes have better performances than traditional partial decode-and-forward scheme, and the centralized selecting is better than distributed selecting. However, distributed selecting approaches the centralized selecting closely with a far lower system cost.

Rate and diversity impose a fundamental tradeoff in wireless communication. We propose a novel distributed space-time coding (DSTC) scheme based on linear constellation precoding (LCP) for Amplify-and-Forward relaying networks. The proposed scheme can achieve full-diversity or full-rate, and also offers a flexibility for a desired rate-diversity tradeoff. This scheme works well with arbitrary signal constellation and any number of relays. Through performance analysis, coding design criteria and decoding strategy are provided. Simulation results show that the proposed coding scheme outperforms diagonal DSTC (DDSTC) and distributed linear dispersion (DLD) code at high power. From the comparison with DDSTC, the DSTC-LCP scheme achieves the same rate using a lower modulation order, yielding almost the same performance.