This paper explores the use of reconfigurable intelligent surface (RIS) in the integrated terrestrial-space network (ITSN) to mitigate co-channel interference between low earth orbit (LEO) satellite systems and terrestrial base stations (BS), with a focus on minimizing total power consumption. An alternating optimization framework is developed, decomposing the problem into three subproblems: BS beamforming design solved via second-order cone programming (SOCP) with interior-point methods or zero-forcing (ZF) method with water-filling algorithm, RIS phase-shift optimization addressed through semi-definite relaxation (SDR) combined with Gaussian randomization to handle non-convex unit-modulus constraints, and LEO satellite transmit signal power optimization addressed by water-filling algorithm. Simulation results show that compared to conventional non-RIS architectures, the proposed system achieves significant power reductions: 14–16 dB for BS and 8.68 dB for satellites. Compared to the WMMSE-based method, it reduces interference at BS users by 4–6 dB and at SU by 15–20 dB. When the satellite power is fixed, it still reduces the power consumption of the BS by 6-8 dB. The proposed methodology enables harmonious coexistence between terrestrial BSs and LEO satellite service zones under strict power budgets through enhanced interference decoupling capabilities.

This letter investigates blocked job offloading in a satellite-ground integrated network, which allows each low-earth orbit (LEO) satellite to execute more jobs on-board instead of sending the raw data to ground users. We propose an approximation offloading model to approximate the satellite-ground integrated network and analyze the average execution time, which is defined as when the user receives the raw data or processed result from the LEO satellite. Furthermore, we propose the maximum equivalent arrival rate and blocked job offloading algorithms. Numerical results validate the approximation model’s effectiveness and the proposed algorithms’ performance advantages and indicate a way to select the proper algorithm.

In this paper, the covert performance of the simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided Terahertz (THz) communication systems with rate-splitting multiple access (RSMA) over the generalized α-μ fading channel is studied, where the discrete phase error and noise power uncertainty are considered. By means of the Gaussian approximation and Gamma distribution, the closed-form outage probability (OP) of the legitimate users is firstly derived. Then, the covert performance of the system is analyzed, and average detection error probability (ADEP) is deduced for the warden, and resultant closed-form ADEP is obtained. By minimizing the ADEP, the optimal detection threshold is derived with closed-form expression. With these results, subject to the constraints of covertness and reliability, the power allocation (PA) coefficients are optimized to minimize the OP of the covert user. Two adaptive PA schemes based on two-dimensional (2D) and one-dimensional (1D) search methods are respectively proposed to achieve the solutions, and resulting lower OP is attained. Simulation results indicate that the theoretical OP and ADEP can agree the corresponding simulations well, and randomizing the noise power of warden can improve the covert performance. Moreover, the warden with the optimized threshold can obtain lower ADEP than that with a fixed threshold, and the system with two PA schemes has a lower OP than that with a fixed PA, especially the scheme based on 1D search has lower complexity while maintaining the superior performance.

Edge computing is near end users and provides them with fast computing services. Compared to the cloud, edge provides services with low communication delays but can only service limited users due to constrained storage and computing resources. Thus, allocating more computing resources to some jobs will block the execution of others, and the edge sends them either to the cloud or back to the users. This article focuses on minimizing the average system time by exploring blocked job scheduling (BJS) and redundant computing resource allocation (RCRA) in the cloud–edge–user system. Since edge nodes often operate in highly unpredictable environments, we adopt the replication redundancy to use the resources to shorten the job execution time. First, we propose an approximate model to evaluate the average system time theoretically. Second, we analyze the optimal scheduling for the blocked jobs and the optimal resource allocation for the redundant computing resources. Finally, we propose an algorithm combining BJS and RCRA. Simulation results show that the proposed model approximates the cloud–edge–user system well, and the combined algorithm significantly outperforms the other algorithms under different service time distributions.

Terahertz (THz) communication has received much attention recently for its large bandwidth availability, high data-rate transmission and alleviating the spectrum shortage, and it can meet the requirements of Internet of Things with large system capacity and networking capability. In this paper, the performance of multi-antenna THz communication systems with rate-splitting multiple access (RSMA) under the hardware impairments and imperfect successive interference cancelation (SIC) are investigated, where the THz channel is modeled as a composite fading channel including the molecular absorption effects, misalignment fading and small-scale α-μ fading. Taking the hardware impairments and imperfect SIC into account, the probability density function and cumulative distribution function of the effective channel gain are derived. A joint zero-forcing and maximum ratio transmission beamforming design is employed to eliminate the interference among devices. Then, with the performance analysis, the closed-form outage probability (OP) and diversity gain of the system are respectively deduced. By minimizing the OP, a closed-form power allocation (PA) scheme is proposed to adjust the PA coefficients between the common stream and private streams, and resultant lower OP is attained. Moreover, the closed-form expression of the ergodic sum rate (ESR) is derived by means of Fox-H function and the Meijer-G function. With this ESR expression, the asymptotic ESR at high signal to noise ratio (SNR) is also provided to gain further insights. Furthermore, a simple upper bound of the ESR is derived for performance evaluation based on the Jensen’s inequality. Simulation results show that the theoretical analysis is effective, and the proposed PA scheme can obtain lower OP. Besides, the impact of different system and fading parameters on the performance are also analyzed.

In this paper, the joint power allocation (PA), passive beamforming (BF) and hybrid BF (HBF) including digital and analogue BFs are designed for an intelligent reflecting surface (IRS)-assisted millimeter-Wave (mmWave) communication system with imperfect channel state information (CSI) and multiple mobile users to optimize the weighted sum rate (WSR) and energy efficiency (EE). The achievable WSR and EE of the IRS-mmWave system are first derived based on imperfect cascaded CSI for performance optimization. Then, the non-convex constrained problem is formulated to maximize the WSR, where the PA, HBF, phase and amplitude of IRS elements are jointly optimized. Given PA and passive BF (PBF), closed-form suboptimal HBF is obtained for each iteration. Also, given HBF and PBF, using the block coordinate descent (BCD) methods, closed-form PA is derived. Moreover, the phase and amplitude of IRS elements are derived for PBF design during each iteration. With the obtained HBF, the digital and analogue BFs are also derived. Based on this, joint schemes of PA, HBF and PBF are developed. Besides, an efficient iterative algorithm based upon the alternating optimization (AO), weighted minimum mean-square error (WMMSE) and Dinkelbach methods are presented for EE maximization and the suboptimal solution is obtained. Correspondingly, the energy-efficient design for joint PA, HBF and PBF is provided. Simulation results verify the proposed solutions.

In this paper, the covert performance of the non-orthogonal multiple access (NOMA) system with random transmit power under hardware impairments over the Nakagami fading channel is investigated, where the stronger user serves as a covert user (Bob) and receives the secure message from the transmitter (Alice) under the cover of the weaker user (Carl). The transmission is monitored by a warden (Willie). To improve the covert performance, we randomize the transmit power of Alice, which is subject to a uniform distribution. Based on this, considering the randomness of covert user’s position, we derive the closed-form expressions of the average detection error probability (ADEP) respectively for the warden with instantaneous and statistical channel information. Then, the optimal detection threshold based on one-dimensional (1D) search method is presented for minimizing the ADEP. To simplify the optimal threshold, the suboptimal threshold with low complexity is also derived. Besides, the outage probability (OP) of the system is deduced for performance analysis and optimization. Under the constraints of covertness and reliability, the power allocation (PA) coefficient is optimized to minimize the OP of the covert user, and the 1D search scheme is proposed to obtain the optimal solution. Also, a suboptimal scheme is presented to reduce the complexity of the optimal scheme. Numerical results are presented to validate our theoretical analysis, and show that randomizing the transmit power can effectively improve the covert performance. Moreover, the system with two optimized thresholds can obtain lower ADEP than that with a fixed threshold, and the system with two optimized PA coefficients has a lower OP than that with a fixed PA.

In this paper, the energy consumption (EC) optimization of an aerial high altitude platform station (HAPS) aided mobile edge computing (MEC) network with non-orthogonal multiple access (NOMA) in the presence of imperfect successive interference cancellation is studied. Specifically, joint design schemes of the resource allocation (RA) and the two-dimensional (2D) horizontal position are proposed to minimize the sum EC subject to the different constraint conditions. In particular, we jointly optimize the receive beamforming (BF), the power allocation (PA), HAPS position, the local computation resource, the computation task offload coefficient, and the computation resource allocated for each user via the block coordinate descent method. Namely, given the other optimization parameters, we first optimize a 2D position of HAPS. Then, given the 2D position, by introducing the auxiliary variables, a joint design of BF, PA, offload coefficient and computation resource is solved by an efficient iteration algorithm based on the successive convex approximation method. Additionally, a suboptimal joint design scheme is also developed to lower the complexity. Simulation results show that the proposed two design schemes of the joint RA and position are effective in reducing the EC, and they have a lower EC when compared to benchmark schemes.

In this article, energy efficiency (EE) is maximized for the reconfigurable intelligent surface (RIS) aided millimeter-Wave (mmWave) networks with non-orthogonal multiple access (NOMA) and multiple mobile devices. To this end, we first propose the EE optimization, under the constraints of maximum power, minimal rate of devices and constant modulus of beamforming (BF) vectors. Then, the joint resource allocation scheme of power allocation (PA) and BF is designed. Specifically, given PA, an effective iterative algorithm based on the majorization-minimization, concave-convex procedure and block coordinate descent (BCD) is presented to obtain closed-form solutions of suboptimal passive BF (PBF) and analog BF (ABF) for each iteration. Then, given PBF and ABF, an effective iterative algorithm based on the successive convex approximation, BCD and Dinkelbach methods is derived to achieve suboptimal closed-form PA for each iteration. By incorporating these two algorithms into the BCD method, a joint optimization algorithm for EE maximization is presented. As a result, joint resource allocation of PA, PBF and ABF is attained. Besides, the convergence and complexity of the algorithms are analyzed. For comparison, the benchmark scheme based on the multidimensional search method and artificial bee colony algorithm is also presented. Simulation results show that the proposed joint scheme is effective and higher EE can be obtained with lower complexity.

Cell-free massive multiple-input multiple-output (CF mMIMO) is known for its ability to provide ubiquitous connectivity. In this paper, we investigate the achievable spectral efficiency (SE) of a user-centric (UC) CF mMIMO system with both multi-antenna APs and users over joint-correlated Rayleigh fading channels. First, we provide a performance analytical framework for the system with the linear decorrelator and study the impact of hardware impairments (HIs) at transceivers on the uplink SE. Based on that, we discuss the local minimum mean-squared error (MMSE) and partial MMSE combining schemes and the partial large-scale fading decoding (LSFD) method from a scalable point of view. Besides, the exact closed-form SE expression is derived with maximum ratio combining (MRC). Then, we study the MMSE-based successive interference cancelation (MMSE-SIC) detector and give an approximate closed-form SE expression with MRC. In the simulations, we compare the linear decorrelator to the MMSE-SIC detector under different hardware-impaired scenarios. Numerical results correspond to the theoretical analyses and show that the impact of HIs can be mitigated by adding the number of receive antennas.

Unmanned aerial vehicle (UAV) communication system assisted by intelligent reflecting surface (IRS) has promising applications in future wireless communication. Considering the realistic high-efficient IRS-UAVs transmission system in the three-dimensional (3D) space, we propose a joint beamforming and UAVs’ trajectory optimization algorithm to maximize the secrecy energy efficiency (SEE) of the system, which is constrained by the mobility of UAVs and the number of access users. In the proposed algorithm, the beamforming vector, multi-IRS diagonal phase shift matrix, and UAVs’ trajectory are optimized alternately using block coordinate descent (BCD). We not only derive the optimal closed-form expression of the multi-IRS diagonal phase shift matrix, but also solve the 3D trajectory optimization of UAVs under the given beamforming matrix. Simulation results show the effectiveness of the proposed algorithm and the performance improvement for IRS-UAVs communication.

Due to the existence of imaginary interference in filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) systems, channel estimation technology is a difficult task. In this paper, an innovative preamble structure is suggested to increase the channel estimation accuracy. The suggested preamble structure employs five FBMC/OQAM symbols and every three adjacent subcarriers constitute a preamble sub-block (PSB). According to interference factors, the special pilots are designed for PSB, which can increase the central point’s pseudo-pilot power and therefore improve channel estimation performance. Experimental results testify that the suggested approach works better than its conventional counterparts.

With the evolution of the fifth generation (5G) and beyond heterogeneous wireless networks, telecommunications operators and researchers are driven to develop advanced transmission technologies that enable the coexistence of multiple radio access technologies (multi‐RATs). By employing the efficient implementation of filter‐bank multi‐carrier (FBMC) transceiver and the scalable matrix transformation (SMT) module, in this study, a unified multiple access architecture termed as FBMC‐SMT is proposed, which is capable of flexibly integrating with both 3G and 4G transmission schemes and improving the system performance. As a special case of FBMC‐SMT, the performance evaluation of FBMC‐CDMA is conducted by extensive simulation. It is verified that FBMC‐CDMA with 16 subbands allocated outperforms the traditional single carrier wideband code division multiple access when the number of code channels assigned is larger than 5. Moreover, the signal‐to‐interference‐plus‐noise ratio analysis of FBMC‐SMT is also given and matches the simulation results very well. Hence, the proposed FBMC‐SMT can serve as a unified architecture to flexibly integrate multi‐RATs, thus to meet variable application requirements in 5G and beyond heterogeneous wireless networks.

This paper presents an efficient channel estimation scheme of the non-orthogonal multiple access system which named CDMA-FMT in 5G wireless networks. The proposed channel estimation scheme is theoretically analyzed based on the equivalent sub-band channel model. Moreover, the simulation results show that, by using the proposed channel estimation scheme, the BLER performance of the CDMA-FMT system outperforms the traditional CDMA system.

Abstract Distributed caching‐empowered wireless networks can greatly improve the efficiency of data storage and transmission and thereby the users' quality of experience (QoE). However, how this technology can alleviate the network access pressure while ensuring the consistency of content delivery is still an open question, especially in the case where the users are in fast motion. Therefore, in this paper, we investigate the caching issue emerging from a forthcoming scenario where vehicular video streaming is performed under cellular networks. Specifically, a QoE centric distributed caching approach is proposed to fulfill as many users' requests as possible, considering the limited caching space of base stations and basic user experience guarantee. Firstly, a QoE evaluation model is established using verified empirical data. Also, the mathematic relationship between the streaming bit rate and actual storage space is developed. Then, the distributed caching management for vehicular video streaming is formulated as a constrained optimization problem and solved with the generalized–reduced gradient method. Simulation results indicate that our approach can improve the users' satisfaction ratio by up to 40%. Copyright © 2015 John Wiley & Sons, Ltd.

Abstract In delay and disruption tolerant networks, the contacts among nodes are intermittent. Because of the importance of data access, providing efficient data access is the ultimate aim of analyzing and exploiting disruption tolerant networks. Caching is widely proved to be able to improve data access performance. In this paper, we consider caching schemes for broadcasting and multicasting to improve the performance of data access. First, we propose a caching algorithm for broadcasting, which selects the community central nodes as relays from both network structure perspective and social network perspective. Then, we accommodate the caching algorithm for multicasting by considering the data query pattern. Extensive trace‐driven simulations are conducted to investigate the essential difference between the caching algorithms for broadcasting and multicasting and evaluate the performance of these algorithms. Copyright © 2016 John Wiley & Sons, Ltd.

The downlink capacity performance of distributed antenna systems (DAS) with antennas selection is investigated in a composite fading channel which takes path loss, Rayleigh fading and lognormal shadowing into account. According to the performance analysis, and using log-normal distribution approximation, the tightly approximate closed-form expressions of cumulative distribution function (CDF) and probability density function (PDF) of the effective SNR are obtained, respectively. Based on these results, the outage capacity performance of DAS is analyzed. By utilizing the Gaussian distribution approximation, a simple approximate outage capacity is derived. As a result, the closed-form expression is achieved. All these expressions will provide good theoretical performance evaluation for DAS. Simulation results show that the derived outage capacity is effective, and can match the corresponding simulation well.

Based on the asymptotic analysis in spatially correlated Rician fading channel, the precoders for space‐time block‐coded multiple‐input multiple‐output system at high and low signal‐to‐noise ratios (SNRs) are, respectively, designed to minimise the system bit error rate (BER). The precoding for high SNR provides closed‐form positive power allocation by using the Langrage optimisation method, and has the BER performance close to that of the optimal scheme, especially at high SNR. The Langrage multiplier does exist and is unique, and practical Newton's method is proposed to find its optimal value. Moreover, it has a closed‐form solution for some special cases. Thus, it avoids the iterative calculation of some existing precoding schemes. The precoding for low SNR has a closed‐form solution similar to onedirectional (1‐D) beamforming, and can obtain the performance very close to that of the optimal scheme at low SNR. By combining the above two schemes, a simple and effective suboptimal precoding scheme can be achieved for different SNRs. Simulation results show that the proposed scheme derived from the integration of high and low SNR can provide BER lower than the equal power precoding, and obtain the BER performance close to that of the existing optimal scheme with low complexity.

Channel estimation for an orthogonal frequency-division multiplexing (OFDM) broadband system operating with high mobility is very challenging. This is mainly due to the significant Doppler spread, inherent in a time-frequency doubly-selective (DS) channel. Consequently, a large number of channel coefficients must be estimated, forcing the need for allocating a large number of pilot subcarriers. To address this problem, we propose a novel channel estimation method based on basis expansion models (BEMs) and distributed compressive sensing (DCS) theory. To be specific, we develop a two-stage sparse BEM coefficients estimation method, which can effectively combat the Doppler spread and enable accurate channel estimation with dramatically reduced number of pilot subcarriers. The numerical results reveal that, in a typical LTE system configuration, the proposed scheme can increase the spectral efficiency by 40% and achieve a 6 dB gain in terms of normalized mean square error (NMSE), both compared to the conventional scheme.

In this paper, we tackle the energy efficiency (EE) issue in uplink virtual MIMO systems, which requires the optimization of two interlaced parameters: the number of constituent mobile users in the virtual MIMO and their corresponding power allocation. By exploiting the fact that increasing the number of active users can increase the number of contributors to the total EE on one hand but reducing the diversity order for each single user on the other, we can show the existence of an optimal transmission mode and find a simple way for its search. Through in-depth analysis, we show the existence of a unique globally optimal power allocator for the case without power constraints, and further reveal the impact of power constraints upon power allocation, as compared to its global counterpart, aiming to provide a powerful means for power-constrained EE optimization. Finally, we establish theories, for homogeneous networks, to narrow down the search range for possible transmission modes, leading to a significant reduction of computational complexity in optimization. Simulation results are presented to substantiate the proposed schemes and the corresponding theories.

A reverse transmission mechanism for smart grid is introduced, in which the transmission robustness of ad-hoc net-segment is guaranteed. The goal is to address the deadlock issue caused by the broken down of the targeted tower , and to reduce transmission power consumption. Performance analysis of the new mechanism is presented, in which the configuration of different number of electrical towers is discussed. Upon examination of the simulation results, we conclude that the proposed mechanism provides lower latency and power consumption compared to the traditional transmission mechanism, and guarantees the transmission robustness for the smart grid.

Limited feedback precoding (LFP) significantly improves multiple-input multiple-output (MIMO) spatial multiplexing link reliability with a small amount of feedback from the receiver back to the transmitter. One of the key problems linked to LFP is how to select an optimal precoder from a pre- determined unitary codebook. We find that the conventional precoder selection criteria are not applicable to the stream maximization transmission (SMT) mode with linear receivers, including zero forcing (ZF) and minimum mean square error (MMSE) decoders. To solve this issue, a novel singular value decomposition (SVD) based precoder selection criterion is proposed in this paper. This criterion features a unified structure for all the linear receivers such as ZF and MMSE decoders, and is shown by simulation to provide significant coding gains in various SMT systems. With the same complexity as the conventional one, the proposed criterion could find its applications in next generation systems employing SMT spatial multiplexing, significantly improving system performance with affordable feedback requirement.

The performance analysis of a space‐time coded multiple‐input multiple‐output (MIMO) system with variable‐rate adaptive modulation over flat Rayleigh fading channels for both perfect and imperfect channel state information (CSI) is presented. In this study, the optimum fading gain switching thresholds for attaining maximum spectrum efficiency (SE) subject to an average bit error rate (BER) constraint are derived. The existence and uniqueness of the Lagrange multiplier in the constrained SE optimisation is studied. It is shown that the Lagrange multiplier does exist and is unique for imperfect CSI. On the other hand, the Lagrange multiplier will be unique if the existence condition for MIMO under perfect CSI is satisfied. A practical iterative algorithm based on Newton's method for finding the Lagrange multiplier is proposed. By the switching thresholds, closed‐form expressions of the SE and average BER are obtained. Simulation results for SE and BER are in good agreement with the theoretical analysis. The results show that the space‐time block coded MIMO system using adaptive modulation (AM‐STBC‐MIMO) with average BER constraint provides SE better than AM‐STBC‐MIMO with fixed thresholds, and AM‐STBC‐MIMO using a BER upper bound, but it has performance degradation in SE for imperfect CSI.

Driven by green communications, energy-efficient transmission is becoming an important design criterion for wireless systems, aiming to extend the life cycle of batteries in mobile devices. In this paper, we tackle the energy efficiency (EE) issue in uplink virtual multiple-input multiple-output (MIMO) systems, which requires the optimization of two interlaced parameters: the number of constituent mobile users in the virtual MIMO and their corresponding power allocation. The former parameter is a structural parameter defining the size of the virtual MIMO (usually known as the transmission mode) and its optimization relies on the method of enumeration. The difficulty is further aggravated by the fact that the EE is a non-convex function of power, even for a given transmission mode. By exploiting the fact that increasing the number of active users can increase the number of contributors to the total EE on one hand but reducing the diversity order for each single user on the other, we can show the existence of an optimal transmission mode and find a simple way for its search. Through in-depth analysis, we show the existence of a unique globally optimal power allocator for the case without power constraints under the assumption of zero-forcing receivers, and further reveal the impact of power constraints upon power allocation, as compared to its global counterpart, aiming to provide a powerful means for power-constrained EE optimization. Finally, we establish theories, for isometric networks, to narrow down the search range for possible transmission modes, leading to a significant reduction of computational complexity in optimization. Simulation results are presented to substantiate the proposed schemes and the corresponding theories.

Channel estimation for an orthogonal frequency-division multiplexing (OFDM) broadband system over a doubly selective channel is very challenging. This is mainly due to the significant Doppler shift, which results in a time-frequency doubly-selective (DS) channel. The DS channel features a large number of channel coefficients, which introduces inter-carrier interference (ICI) and forces the need for allocating a large number of pilot subcarriers. To tackle this problem, in this paper we propose a novel channel estimation scheme based on distributed compressive sensing (DCS) theory. Taking advantage of the basis expansion model (BEM) and the channel sparsity in the delay domain, we transform the original DS channel into a novel two-dimensional channel model, where several jointly sparse BEM coefficient vectors become the estimation goal. Then a special decoupling form originating from a novel sparse pilot pattern is designed for such estimation, which results in an ICI-free structure and enables the DCS application to make joint estimation of these vectors accurately. Combined with a smoothing treatment process, the proposed scheme can achieve significantly higher estimation accuracy than the existing ones, although with a much smaller number of pilot subcarriers. Theoretical analysis and simulation results both confirm its performance merits.

Compressed sensing (CS) has recently emerged as a powerful signal acquisition paradigm. CS enables the recovery of high-dimensional sparse signals from much fewer samples than usually required. Further, quite a few recent channel measurement experiments show that many wireless channels also tend to exhibit sparsity. In this case, CS theory can be applicable to sparse channel estimation and its effectiveness has been validated in point-to-point (P2P) communication. In this work, we study sparse channel estimation for two-way relay networks (TWRN). Unlike P2P systems, applying CS theory to sparse channel estimation in TWRN is much more challenging. One issue is that the equivalent channels (terminal-relay-terminal) may be no longer sparse due to the linear convolutional operation. On this basis, novel schemes are proposed to solve this problem and effectively improve the accuracy of TWRN channel estimation when using CS theory. Extensive numerical results are provided to corroborate the proposed studies.

Energy-efficient transmission is increasing in importance for wireless system design because of limited battery power in mobile devices. In this paper, we consider energy efficiency optimization in uplink virtual MIMO systems. First, accurate closed-form expressions are derived for the achievable energy efficiency for both multi-user (MU) transmission mode and single-user (SU) transmission mode, which are based on the ergodic throughput, transmit power and circuit power. Then, we demonstrate the existence of unique globally optimal energy efficiency for SU and MU mode, respectively. Since users have data rates requirement and peak power limits, we further consider power constraint, and develop joint mode switching with power loading schemes to optimize energy efficiency for both homogeneous and heterogeneous networks. Finally, our simulation results show that the proposed adaptive transmission strategies significantly improve energy efficiency.

Employing user pairing schemes in uplink virtual multiple-input multiple-output (V-MIMO) systems can significantly improve system capacity owing to multi-user diversity. Most of the existing user pairing algorithms were developed under the assumption that a scheduler always possesses the perfect channel state information (CSI). In this paper, taking into account the effects of channel estimation error (CEE), the authors propose a robust user pairing algorithm based on a closed-form average sum rate under CEE. In addition, the authors further propose an adaptive user selection scheme as an effort to maximise the sum rates owing to the rate constraint under CEE. The gain of the proposed method is shown in simulation results.

In this study, a precoding scheme for minimising bit error rate (BER) subject to a power constraint for space-time-coded multiple-input–multiple-output systems over flat Rayleigh fading channel in the presence of channel estimation error and feedback delay is presented, it includes the precoding with estimation error or feedback delay only as its special cases. With this scheme, an algorithm for attaining the optimal power allocation across multiple beams is developed. It is shown that the Lagrange multiplier for the constrained optimisation does exist and is unique. A practical iterative algorithm based on Newton's method for finding the Lagrange multiplier is proposed. Generally, the optimal power control may result in negative power allocation to some beams. A necessary and sufficient condition for the beams having positive power control is developed that simplifies the optimisation procedure. Simulation results show that the developed precoding scheme can provide BER lower than the equal power allocation scheme and the existing precoding scheme, but has performance degradation when compared with the precoding scheme with estimation error or feedback delay only.

This paper presents an evolution framework of resource allocation by infection among secondary users (SUs) in an OFDMA-based cognitive radio cellular networks. Each primary user (PU) sells his extra sub-channels to SUs in his sensing range to achieve the highest payoff and each SU may come across another in his sensing range to make infection. Two different infection processes among SUs, the infection with and without local knowledge respectively, are considered. We prove the existence and convergence of evolutionary equilibrium (EE) for both cases, and show some interesting properties such as the impact of cheating of SUs in the above infection processes. Besides, we proposed two algorithms for the infection processes to converge to EE in a distributed manner. Simulation results show that the algorithms with local knowledge can equally share the extra resources among SUs efficiently, which is actually the overall optimal solution (OOS). While for the algorithm without local knowledge, we find that though EE exists, OOS cannot always be achieved. Furthermore, we optimize the second algorithm to make EE approximate to OOS.

The carrier frequency offset (CFO) could destroy the orthogonality of OFDM subcarriers and induce the inter-carrier interference (ICI) spread. However, the generalized multi-carrier (GMC) system reflects different interference characters in the presence of CFO. In this paper, we exactly analyze the CFO effect on GMC, which multiplexes several single band single carrier based frequency division multiple access (SC-FDMA) streams in frequency domain, under the scenario of additive white Gaussian noise (AWGN) channels. A closed-form expression of signal to interference plus noise ratio (SINR) is derived for the GMC systems in the presence of CFO. Furthermore, the bit error rate (BER) performance analysis is also performed in comparison with the traditional OFDM system for both uncoded and coded environments. Analysis and simulation results show that the feature procedure of discrete Fourier transform (DFT) spread may condense the CFO-induced interference in the first symbol of each subband, resulting in less interference in other symbols. Such condensing procedure can provide robustness against the CFO effect, which also lies on the subband bandwidth for GMC systems. In contrast to the conventional OFDM system, GMC exhibits more robustness when each subband width is designed for 4 times larger than the subcarrier spacing.

In this paper, we propose a null space-based precoding scheme for secondary transmission in a cognitive radio multiple-input multiple-output (CR-MIMO) network under the assumption that time-division-duplex is employed by the primary transmission. First, the secondary transmitter periodically senses the transmitted signals from the primary users and estimates the corresponding covariance matrix. Then, subspace techniques are utilized to estimate the noise subspace of this covariance matrix, and the dimension of the noise subspace is estimated using information theory criteria (AIC or MDL). Finally, the obtained null space is used as the precoding matrix for the secondary transmission, which effectively avoids the interference induced by the secondary transmission at the primary users. Moreover, the achievable capacity of the secondary MIMO channel by the proposed scheme is derived. Simulations are performed to show the efficacy of the proposed scheme.

A noise variance optimization method is proposed for the time and frequency dimension separate (2 times 1D) Wiener-filtered channel estimation of OFDM based systems. According to Wiener-filter theory, the noise variance is necessary to achieve optimal solution. For 2 times 1D Wiener-filtered channel estimation, the Wiener-filtering will be applied twice respectively in time and frequency dimension. However, the effect of variety of noise variance induced by the first filter should be considered on the second filter in this method. In the proposed method, the noise variance used by the second filter is optimized according to the mean square error (MSE) of channel estimation by the first filter. The exact MSE of channel estimation is derived in this paper. Moreover, the channel estimation performance is evaluated with different noise variance optimizing criteria. The simulation results show that the performance of the proposed method is better than the 2 times 1D filters method without noise variance optimization, and is very close to that of the Wiener 2D filter.