User grouping is a critical challenge for Multi-User MIMO (MU-MIMO) systems, especially in Industrial Internet of Things (IIoT) environments. In scenarios with stringent computational constraints, traditional schemes tend to leverage pairwise channel correlation for algorithm design. However, pairwise channel correlation is an indirect and unreliable metric for user grouping, potentially causing undesirable user grouping and consequently a high frame error rate (FER) in certain environments. To solve this problem, we bypass the reliance on pairwise channel correlation and propose a new user grouping metric, pairwise compatibility, which is directly modeled from historical performance data. This transition enables a pairwise compatibility-based user grouping scheme, in which we formulate a combinatorial optimization problem to directly optimize compatibility. Extensive simulations and validation show that our pairwise compatibility-based scheme exhibits a modest advantage in the spatially correlated Rayleigh fading channel, and delivers a significant performance gain in the real-world measured channel. By replacing channel correlation with compatibility, our scheme offers a more efficient and robust solution for user grouping in practical MU-MIMO-based IIoT networks.

Cell-free (CF) massive multiple-input multiple-output (MIMO), as a promising network architecture for beyond the fifth generation (5G), has a great potential to support grant-free (GF) transmission for machine-type communication (MTC). To shed light on this subject, this work aims to model and evaluate the performance of GF transmission in CF massive MIMO under a realistic network deployment scenario, where the spatial locations of both access points (APs) and devices are assumed to be random in nature. In particular, by capitalizing on the distinctive CF network architecture and features, we design a new two-disk based geometric model for GF transmission, which facilitates analysis and understanding in CF massive MIMO. Based on the proposed two-disk model, we derive an approximated closed-form expression for the access success probability by leveraging on techniques from stochastic geometry, and investigate the impact of different key system parameters on the network performance, which have not been presented previously. To highlight the performance superiority of CF massive MIMO, we further provide a comparative analysis by using an analogous single-disk model in an equivalent co-located massive MIMO network. Simulation results verify our analysis and demonstrate that CF massive MIMO is able to significantly outperform its co-located counterpart in terms of access success probability and provide robust performance against increased access density, which well suits to crowd scenarios.

With the rapid development of the wireless communication technologies, it has become a challenging problem for engineering education for senior students on telecommunication major to catch up with the latest development of technologies. In this paper, a simplified 5G experiment system is established online, which is a blended lab platform including virtual simulation and online real-world experiment. Using the platform, the student can study the 5G system modules in the front of his computer and program the 5G module by software-defined radio technology.

In this paper, an improved Tabu-search preamble assignment algorithm is proposed in cell-free massive multiple input multiple output (MIMO) to find the optimal preamble assignment solution with a low complexity. Different from the existing Tabu-search algorithm, the proposed one exploits the large-scale fading knowledge to refine the solution space so that only a small number of solutions that are more likely to mitigate preamble collision are explored. As a result, the complexity of Tabu search for preamble assignment can be significantly reduced. Simulation results demonstrate that the proposed algorithm is able to provide a good tradeoff between the performance and complexity compared to the state-of-the-art algorithms. In particular, it can achieve an over 50% complexity reduction with only a negligible performance loss in terms of the achievable sum rate compared to the existing Tabu-search algorithm.

Preamble resources are scarce and precious in random access (RA), which need to be efficiently utilized to support critical machine-type communication (MTC) with stringent access requirements. In this article, we study a grant-free RA scenario for critical MTC in the context of massive multiple-input–multiple-output (MIMO). To enhance the access reliability of delay-sensitive devices within a predefined latency budget, two dynamic preamble-resource partitioning (DPP) schemes are proposed. Particularly, by leveraging massive MIMO, we first analytically investigate the feasibility of DPP in the considered RA scenario and demonstrate its performance superiority to the conventional scheme. Based on the analytical results, we then propose a greedy DPP scheme that performs locally optimal preamble-resource partitioning in each RA slot. To find the globally optimal DPP solution, we further propose a reinforcement learning (RL)-based scheme by modeling the considered RA scenario as a Markov decision process. Simulation results show the practicality and effectiveness of the proposed DPP schemes. In particular, to achieve a target access failure rate of$1\times 10^{-2}$, the proposed RL-based scheme is able to enhance the RA traffic load by 42% and improve the preamble resource utilization by over 25% compared to the conventional baseline scheme.

Preamble collision is a bottleneck that impairs the performance of random access (RA) user equipment (UE) in grant-free RA (GFRA). In this paper, by leveraging distributed massive multiple input multiple output (mMIMO) together with machine learning, a novel machine learning based framework solution is proposed to address the preamble collision problem in GFRA. The key idea is to identify and employ the neighboring access points (APs) of a collided RA UE for its data decoding rather than all the APs, so that the mutual interference among collided RA UEs can be effectively mitigated. To this end, we first design a tailored deep neural network (DNN) to enable the preamble multiplicity estimation in GFRA, where an energy detection (ED) method is also proposed for performance comparison. With the estimated preamble multiplicity, we then propose a K-means AP clustering algorithm to cluster the neighboring APs of collided RA UEs and organize each AP cluster to decode the received data individually. Simulation results show that a decent performance of preamble multiplicity estimation in terms of accuracy and reliability can be achieved by the proposed DNN, and confirm that the proposed schemes are effective in preamble collision resolution in GFRA, which are able to achieve a near-optimal performance in terms of uplink achievable rate per collided RA UE, and offer significant performance improvement over traditional schemes.

Massive MIMO based grant-free random access (mGFRA) is a promising RA technique for massive access with low signaling overhead. However, due to a limited orthogonal preamble size, preamble collision is a key factor that constrains the success probability of mGFRA. In this paper, we examine the potential of applying non-orthogonal preambles to mitigate the issue, where two typical non-orthogonal sequences, i.e., Gaussian distribution sequences and Zadoff-Chu (ZC) sequences, are taken into account. Asymptotic behaviors of the success probabilities with the non-orthogonal preamble sequences are analyzed and compared using derived closed-forms. Through the analysis we reveal that ZC sequences outperform Gaussian ones. However, though non-orthogonal sequences are able to reduce or even alleviate the preamble collision, they do not necessarily provide better performance than the orthogonal counterpart. In addition, regarding orthogonal sequences as a single-root case of ZC sequences, we show that there exists an optimal number of ZC roots that maximizes the success probability for mGFRA, which is low-bounded by orthogonal baseline. Simulation results evaluate the performance of non-orthogonal sequences in mGFRA under various practical system parameters, and validate our analysis.

Massive multiple-input multiple-output (MIMO) opens up new avenues for enabling highly efficient random access (RA) by offering abundance of spatial degrees of freedom. In this paper, we investigate the grant-free RA with massive MIMO and derive the analytic expressions of success probability of the grant-free RA for conjugate beamforming and zero-forcing beamforming techniques. With the derived analytic expressions, we further shed light on the impact of system parameters on the success probability. Simulation results verify the accuracy of the analyses. It is confirmed that the grant-free RA with massive MIMO is an attractive RA technique with low signaling overhead that could simultaneously accommodate a number of RA users, which is multiple times the number of RA channels, with close-to-one success probability. In addition, when the number of antennas in massive MIMO is sufficiently large, we show that the number of orthogonal preambles would dominate the success probability.

Grant-free random access (RA) with massive MIMO is a promising RA technique that provides significant benefits in increasing the channel reuse efficiency with low signaling overhead. Since user equipment (UE) detection and channel estimation in grant-free RA rely solely on the received preambles, preamble designs that enable high success rate of UE detection and channel estimation are very much in need to ensure the performance gain of grant-free RA with massive MIMO. In this paper, a super preamble consisting of multiple consecutive preambles is proposed for the high success rate of grant-free RA with massive MIMO. With the proposed approach, the success of UE detection and channel estimation for a UE depends on two conditions: 1) it is a solvable UE, where we define the UE whose super preamble is not a linear combination of the other UEs' super preambles as a solvable UE and 2) its super preamble is detected. Accordingly, we theoretically analyze the solvable rate of the UEs with multiple preambles and propose a reliable UE detection algorithm to obtain the super preambles of the UEs by exploiting the quasi-orthogonality characteristic of massive MIMO. The theoretical analysis and simulation results show that turning a preamble into a super preamble consisting of two or three shorter preambles, the success rate of UE detection and channel estimation could be significantly increased using the proposed approach.

In this survey paper, we focus on the offset quadrature amplitude modulation-based orthogonal frequency division multiplexing (OQAM-OFDM)-based wireless communications in future Internet of Things (IoT). First, we present the OQAM-OFDM-based wireless communication system model for IoT and show its superiority compared with OFDM. Then, we give a survey and analyze key technologies including the prototype filter design, peak to average power ratio reduction, tail reduction, channel estimation, and multiple-input multipleoutput combination in OQAM-OFDM-based wireless communication systems for IoT. Extensive simulations are conducted to verify the spectral superiority of OQAM-OFDM and compare the corresponding performances of OQAM-OFDM with OFDM in the above key technology areas.

Fifth-generation mobile communication systems aim to support massive access for future wireless applications. Unfortunately, wireless resource scarcity in random access (RA) is a fundamental bottleneck for enabling massive access. To address this problem, we propose a virtual carrier sensing (VCS)-based RA scheme in massive MIMO systems. The essence of the proposed scheme lies in exploiting wireless spatial resources of uplink channels occupied by assigned user equipments (UEs) to increase channel resources for RA. With the proposed scheme, RA UEs are able to exploit the spatial resources that are approximately orthogonal to those of assigned UEs, thus sharing the uplink channel resource with assigned UEs without causing significant interference to them. Specifically, to ensure RA UEs avoid serious interference with assigned UEs, a base station (BS) sends tailored virtual carriers to RA UEs on behalf of assigned UEs. The RA UEs then conduct VCS to determine whether or not the uplink channel resource is available for RA. The closed-form approximations for probability of channel availability and uplink achievable rate with the proposed scheme are derived. Theoretical analysis and simulation results show that the proposed scheme is able to significantly increase channel resources of RA for massive access.

In this paper, we propose a preamble optimization method for the frequency-division multiplexing (FDM)-structured preamble in multiple-input multiple-output systems employing offset quadrature amplitude modulation-based filter bank multicarrier. Specifically, we formulate an optimization problem based on the periodic preamble structure to minimize the mean square error (MSE) of the channel estimation. For two transmit antennas, we find the relationship between preambles and intrinsic interferences from neighboring symbols to achieve the minimum MSE, and derive the optimal closed-form solution. For more than two transmit antennas, we convert the original optimization problem into a quadratically constrained quadratic program and obtain the suboptimal solution by relaxing the nonconvex constraint. The simulation results demonstrate that, in terms of MSE and bit error rate performances, the proposed method outperforms the conventional FDM method at all signal-to-noise ratio (SNR) regimes and outperforms the interference approximation method-complex method at low-to-medium SNR regimes with lower preamble overhead.

Filter bank multicarrier (FBMC) systems based on offset quadrature amplitude modulation (OQAM), namely FBMC-OQAM, have been criticized for their inefficiency in the use of spectral resources, because of the long ramp-up and ramp-down tails at the beginning and the end of each data packet, respectively. We propose a novel method for shortening these tails. By appending a set of virtual (i.e., none data carrying) symbols to the beginning and the end of each packet, and clever selection of these symbols, we show that the ramp-up and ramp-down tails in the FBMC-OQAM can be suppressed to the extent that they are deemed negligible and thus may be ignored. This shortens the length of signal burst in each FBMC-OQAM packet and improves its spectral efficiency, viz., the same data is transmitted over a shorter period of time. We develop an optimization method that allows computation of virtual symbols for each data packet. Simulation results show that, compared with the existing methods, the proposed tail-shortening approach leads to superior out-of-band emissions performance and a much lower error vector magnitude for the demodulated symbols.

The combination of singular value decomposition (SVD)-based beamforming and filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) has not been successful to date. The difficulty of this combination is that, the beamformers may change dramatically between adjacent subchannels, therefore destroy the orthogonality of FBMC/OQAM real-valued symbols, under frequency selective channels. In this paper, we address this problem from two aspects: 1) an SVD-FBMC/OQAM architecture is proposed to support beamforming with finer granularity in frequency domain based on the frequency spreading FBMC (FS- FBMC) structure; 2) a method is proposed to smooth the beamformers from tone to tone (FS-FBMC tone). The proposed finer beamforming and smoothing greatly improve the smoothness of beamformers, therefore effectively suppress the leaked ICI/ISI from adjacent subchannels. Simulations are conducted under the scenario of IEEE 802.11n wireless LAN. Results show that the proposed SVD- FBMC/OQAM system shares very close BER performance with its orthogonal frequency division multiplexing (OFDM) counterpart under the frequency selective channels. The proposed finer beamforming architecture may also serve as a paradigm for the combinations of other multiple- antenna (MIMO) techniques and FBMC/OQAM.

In this paper, a staggered frame structure is proposed for single-cell massive multiple-input multiple-output (MIMO) systems, and the key idea is that different users transmit training pilots at nonoverlapped time. As a result, users do not have to be synchronized strictly to send pilots and orthogonal pilots are not required. Moreover, we also propose two interference suppressed channel estimation methods, i.e., the linear minimum mean square error (LMMSE)-based and orthogonal projection based least squares (OPLS) methods for the staggered frame structure. Specifically, the LMMSE-based method minimizes the mean square error of the estimation, whereas the OPLS method only estimates the part of the user's channel response that is orthogonal to the other users. All conducted simulation results demonstrate that the massive MIMO system with the both proposed methods can achieve high average achievable rate. Furthermore, when the conjugate beamforming is employed, the massive MIMO system could obtain even higher average achievable data rate with the proposed OPLS method than that of the system with perfect channel state information. Moreover, the computational complexity of the proposed OPLS method is very low.

Offset quadrature amplitude modulation-based orthogonal frequency division multiplexing (OFDM) systems cannot be directly combined with the Alamouti code because of the intrinsic imaginary interference. In this paper, we propose a block-wise space-frequency block coding (SFBC) scheme and a block-wise space-time block coding (STBC) scheme for offset quadrature amplitude modulation-based OFDM systems, which achieve bit error rate performances that are close to OFDM systems. The proposed schemes satisfy the orthogonality condition of the Alamouti code in the complex field with guard band/intervals. To improve the spectral efficiency of the block-wise SFBC scheme, we also consider the case without the guard band. It is observed that only the two innermost subcarriers do not satisfy the complex orthogonality condition when the guard band is removed. Then, a simple equalization scheme is proposed to independently equalize the two innermost subcarriers. Simulation results show that the block-wise SFBC scheme works well under channels with mild-to-moderate frequency selectivity, and the block-wise (STBC ) scheme suffers less than 1 dB loss under severe frequency selective channels at the bit error rate of 10 − 3, when only a simple one tap zero-forcing equalizer is employed. Copyright © 2016 John Wiley & Sons, Ltd.

It is well known that broadband wireless communications (BWC) are necessary for high speed vehicles since passengers need many broadband wireless multimedia services. However, one design challenge is to identify sufficient spectrum resource to support BWC in high speed vehicles. Recently, cognitive radios (CRs) are being considered as a promising technology to solve scarcity problem of spectrum resource. Therefore, we investigate the spectrum resource allocation problem in the CR based high speed vehicle network (CR-HSVN) in this paper. Specifically, we propose a spectrum resource allocation framework, where high speed vehicles could effectively utilize the TV white spaces. Subsequently, we formulate the allocation of spectrum resource as an optimization problem to maximize the available spectrum resources (i.e., TV white spaces) utilized by the CR-HSVN, and investigate the property of the CR-HSVN to reduce the complexity by separation computing method without loss of optimality. Furthermore, we analyze the optimal solution based on branch and bound method, the suboptimal solutions based on single channel and linear programming, respectively. Simulation results show that the proposed framework could offer excellent performances of the spectrum utilization and fairness for the CR-HSVN. Meanwhile, the aggregated interference from vehicles to primary users is constrained.

In this paper, we introduce a new family of low-density parity-check (LDPC) codes, called as invertible subset LDPC (IS-LDPC) code, for peak-to-average power ratio (PAPR) reduction in OFDM systems with low complexity. An IS-LDPC code has a number of disjoint invertible subsets, and each invertible subset can be independently inverted to generate other valid codewords of the LDPC code. To construct IS-LDPC codes with good error-correcting performance, we propose a modified progressive edge-growth construction algorithm and verify its effectiveness by analyzing the constructed Tanner graphs. Both theoretical analysis and numerical results show that the IS-LDPC codes exhibit good error-correcting performance and the proposed PAPR reduction scheme based on IS-LDPC codes significantly reduces the PAPR. Compared with the existing coding-based candidate generation schemes, the proposed scheme has a much lower searching complexity when the codeword is transmitted by multiple OFDM symbols. With all mentioned advantages, the proposed PAPR reduction scheme based on IS-LDPC codes could serve as an attractive PAPR reduction solution for future multicarrier communication systems.

For non-contiguous (NC) OFDM based cognitive radio (CR) systems, schemes have been developed in literature to acquire spectrum synchronization information (SSI) with perfect carrier frequency offset (CFO) synchronization. However, OFDM is extremely sensitive to the CFO in practice, which leads to inter-carrier interference (ICI), hence degrading the spectrum synchronization performance for existing schemes. An accurate CFO estimation is therefore required before setting up the SSI. In this paper, we present a novel scheme based on the maximum likelihood (ML) algorithm to estimate the CFO for the NC-OFDM receiver when the SSI is unknown. A corresponding Cramér-Rao lower bound (CRB) with the ideal SSI is derived to demonstrate the efficiency of the proposed scheme. Simulation results show that the proposed scheme is robust against interference and achieves a satisfactory accuracy of estimation, which is close to the relevant CRB.

Femtocells are attractive candidates in the future cellular system such as long-term evolution (LTE). However, the interference introduced by femtocells may hinder the improved system performance in LTE networks. In this paper, we consider the challenging problem of interference management in LTE networks with femtocells, propose two frequency reuse patterns and a novel interference avoidance method with the frequency resources of the macrocell reused by femtocells. The key idea of the interference avoidance method is to identify the macrocell user equipments (MUEs) which are near femtocells with the help of spectrum sensing performed by femtocells and avoid MUEs and nearby femtocells using the same frequency resources through spectrum scheduling. Moreover, we conduct simulations to verify that our proposed method is successful in avoiding the potential interference and improving the network performance.

In this paper, we analyze the noise distribution and correlation characteristic on each subcarrier in OQAM-OFDM systems, and present a method based on the maximum likelihood (ML) criterion to improve the channel estimation. Then, the performance results are obtained by considering an IEEE 802.22 channel model and the proposed method is compared to the conventional frequency domain averaging (FDA) method. Simulation results show that the proposed method is an efficient method to improve the channel estimation.

For non-contiguous OFDM (NC-OFDM) based communication systems, especially NC-OFDM-based cognitive ratio (CR) systems, one of critical challenges is to establish spectrum synchronization between the transmitter and receiver before data transmission. In [5], an a posterior probability (APP) detection algorithm has been proposed for the receiver to detect the subchannels occupied by the transmitter (active subchannels). However, the encoding scheme adopted in [5] is a concatenation of a convolutional coder and a low rate repetition coder so that the system code rate is only 1/4 when half of the subcarriers are active. In this paper, we consider using low density parity-check (LDPC) codes to improve the system data transmission rate. Furthermore, with the channel model a of NC-OFDM-based communication system, we employ density evolution algorithm to obtain good degree distribution pairs and adopt a modified progressive edge-growth (PEG) algorithm to construct the parity-check matrix for the LDPC code. Simulation results show that our optimized LDPC code for NC-OFDM-based communication systems could obtain satisfactory error performance at a high data transmission rate (system code rate of 1/2).

ABSTRACT In this paper, we consider the narrowband interference problem for orthogonal frequency division multiplexing (OFDM)‐based cognitive radio (CR) systems, in which parts of the OFDM subcarriers and parts of the data frame can be seriously interfered, resulting in significant performance degradation. We propose a scheme of iterative noise plus interference estimation and decoding (IED) to mitigate the performance degradation caused by the narrowband interference, which is based on expectation maximization (EM) algorithm. To reduce the number of OFDM symbols for time domain averaging required in the proposed scheme, and adapt the proposed scheme to rapid changing narrowband interference conditions, we also propose an IED scheme with frequency domain partial averaging (IED‐FPA). Moreover, we derive the Cramér‐Rao lower bounds for unbiased noise plus interference variance estimations, and they can be achieved via the proposed IED schemes. Simulation results show that the proposed IED‐FPA scheme can effectively achieve the same performance as that of the optimal maximum likelihood decoder with full knowledge of the power plus interference variances, and the proposed IED‐FPA scheme is very robust with respect to the number of the interfered subcarriers and positive errors of the knowledge of the interfered subcarriers' number. Copyright © 2010 John Wiley & Sons, Ltd.

In this paper, a novel spectrum sharing scheme is proposed to improve the achievable traffic load of secondary users (SUs) under grade of service (GoS) constraints in cognitive wireless networks with heterogeneous traffic. The key idea of the proposed scheme is to introduce preemptive priority and buffering mechanism for real-time traffic and non-real-time traffic, respectively, according to their different delay characteristics. The proposed scheme can reduce the forced termination probability and the blocking probability for heterogeneous calls simultaneously. Numerical results show that the proposed scheme can effectively improve the achievable traffic load of SUs under GoS constraints.

In this paper, we construct a novel MAC-layer sensing framework for fast discovery of spectrum opportunities. We employ the sequential probability ratio test and a novel collaborative sensing scheme, in which the collaboration exists among multi-users and multi-slots. Then, we propose a novel scheme of adaptive collaboration sensing schedule for fast spectrum opportunities discovery and prove that the sensing order based on sorting is optimal with respect to fast discovery. Moreover, we formulate the adaptive collaboration as an optimization problem, which, however, is an NP-hard problem. Then, we give a suboptimal solution to the problem with low complexity. Simulation results show that the proposed scheme could fast discover the spectrum opportunities.

In this paper, we propose a novel method to generate the cyclostationary signature for Orthogonal Frequency Division Multiplexing (OFDM) signals with a low overhead of signature (i.e. number of signature subcarriers) and a good performance of the signature detection under frequency-selective fading channel, for dynamic spectrum access (DSA) networks. Moreover, we also present how to employ the proposed method to detect the embedded signature. Simulations are conducted to make a comparison of the detection performance between the proposed method and the Sutton method. Relying on single hopping subcarrier to generate the signature, the overhead of the proposed method is only one fourth that of the Sutton method in our example. Nevertheless, the simulation results showed that the detection performance of the proposed method is no worse than that of the method proposed by Sutton.

For non-contiguous OFDM (NC-OFDM) based cognitive radio systems, one challenge is to establish the spectrum synchronization before the data transmission over an idle channel. In this paper, we propose two novel detection schemes, i.e., hard-decision-based detection (HDD) and soft-decision-based detection (SDD) schemes, to detect NC-OFDM symbols without the information of out-of-band spectrum synchronization. The key idea of the proposed schemes is to employ the received training symbol to calculate a posterior probability (APP) of each subchannel's being active. Specifically, for the proposed HDD scheme, the secondary receiver detects whether a subchannel is active or not based on its APP. When the APP of a subchannel is larger than a preset threshold, it will be employed for the data detection. Moreover, we derive the miss detection and false alarm probabilities of the hard decision based detection of active subchannels, respectively. For the proposed SDD scheme, all subchannels are employed for the data detection, in which as a priori information of the subchannels' being active, all APPs are taken into account in the bit metric calculation. Theoretical analysis and simulation results show that the proposed schemes can provide satisfactory performance.

For non-contiguous OFDM (NC-OFDM) based cognitive radio (CR) systems, one of huge challenges is to establish spectrum synchronization before data transmissions. In this paper, we propose a novel active subchannel detection scheme for the NC-OFDM receiver without out-of-band spectrum synchronization information. The key idea of the proposed scheme is to employ the received training symbol to calculate the a posterior probability (APP) of each subchannel being active, and then APPs are used to make a decision on which subchannels are active and to be processed in data detection. Furthermore, performance analysis of active subchannel detection is given. Both theoretical analysis and simulation results show that the proposed scheme can provide satisfactory performance of the symbol error rate (SER) when the interference from primary users is considered.

In this paper, we consider the weighted sum throughput maximization (WSTM) problem of multicell network with full spectral reuse. The WSTM problem is a nonconvex combinatorial problem, and difficult to tackle in practice. Thus, we present and discuss three low-complexity suboptimal algorithms, which solve the WSTM problem via joint user scheduling and power control (USPC) among a set of coordinating cells. Extensive system level simulations show that all the proposed algorithms have close weighted sum throughput (WST) performances to that of exhaustive search (ES). Furthermore, the proposed distributed iterative (DI) algorithm, which enjoys both distributed operation and fast convergence, is the most attractive one in practical systems.

In this paper, we propose a dynamic fractional frequency reuse (DFFR) scheme, which is based on a interference avoidance request (IAR) mechanism, to suppress the inter-cell interference (ICI) for downlink OFDMA cellular networks. The key idea of the proposed scheme is that base stations (BSs) exchange IAR messages among each other, and then dynamically control the transmit power according to their received IAR messages. For the elastic data traffic, we combine the IAR mechanism with proportional fairness (PF) scheduling algorithm, to achieve good performances of both total cell throughput and cell edge user (CEU) throughput. Extensive system level simulations show that the proposed DFFR scheme outperforms the reference frequency reuse schemes in terms of the total cell throughput and CEU throughput. Furthermore, the proposed DFFR scheme requires no centralized controller, and the exchanged information among BSs is very little.

In this paper, we propose an iterative data detection scheme to combat unknown narrowband interference for orthogonal frequency division multiplexing (OFDM) systems. The key idea of the proposed scheme is to joint channel estimation, noise plus interference power estimation and decoding together to enhance both the estimation accuracy and bit error ration (BER) performance. We derive the Cramér-Rao Bounds (CRB) for mean square errors of channel estimation and noise plus interference estimations, and the CRBs can be achieved via the proposed scheme with very few iteration numbers, hence, the complexity of the proposed scheme is very low. Moreover, simulation results show that the decoding performance of the proposed scheme substantially approaches to that of the maximum likelihood decoder with perfect channel estimation and full knowledge of interference distribution.

In this paper, we propose an approach of multi-antenna spectrum sensing based on cyclostationarity for cognitive radio. The key idea of the proposed method is to extend Dandawate's generalized likelihood ratio test to take into account the estimation of all cyclic cross-correlation as well as all cyclic autocorrelation obtainable in a multi-antenna system. The proposed method is able to take advantage of spatial diversity without any prior knowledge or estimation of channel information. Furthermore, we propose a simplified method to replace the full generalized likelihood ratio test in order to reduce the computational complexity. Simulation results show the reliability of our proposed detector and demonstrate the effectiveness of using the cyclic cross-correlations, which contribute to a performance gain of approximately 2dB when a four-antenna receiver is considered.

We investigate the bandwidth allocation and power control schemes in orthogonal frequency division multiplexing (OFDM) based multi-hop cognitive radio networks, and the color-sensitive graph coloring (CSGC) model is viewed as an efficient solution to the spectrum assignment problem. We extend the model by taking into account the power control strategy to avoid interference among secondary users and adapt dynamic topology. We formulate the optimization problem encompassing the channel allocation, power control with the interference constrained below a tolerable limit. The optimization objective with two different optimization strategies focuses on the routes rather than the links as in traditional approaches. A heuristic solution to this nondeterministic polynomial (NP)-hard problem is presented, which performs iterative channel allocation according to the lowest transmission power that guarantees the link connection and makes channel reuse as much as possible, and then the transmission power of each link is maximized to improve the channel capacity by gradually adding power level from the lowest transmission power until all co-channel links cannot satisfy the interference constraints. Numerical results show that our proposed strategies outperform the existing spectrum assignment algorithms in the performance of both the total network bandwidth and minimum route bandwidth of all routes, meanwhile, saving the transmission power.

In this letter, we propose a novel scheme to reduce the PAPR of OFDM signals, and its key idea is that the side information of partial transmit sequence is considered as a part of channel frequency response. Therefore, the proposed scheme does not need to reserve bits for submitting side information, resulting in an increase in the data rate. Furthermore, we propose a novel pilot arrangement, in which pilot tones are independently inserted into each subblock. Simulations show that the proposed scheme with the novel pilot arrangement could offer good performances of bit error rate and PAPR reduction.

Many communication signals exhibit cyclostation-arity with some cycle frequencies and the cycle frequencies are related to signal parameters such as symbol rates or carrier frequencies. Dandawate proposed an asymptotically optimal chi-square statistical test to check the presence of cyclostationarity. In the last few years, the statistical test has been applied to spectrum sensing for cognitive radios. We notice that, for most modulated communication signals like PSK, QAM, OFDM and GMSK, the cyclic autocorrelations for different delays at the same cycle frequency are deterministically related in average sense. And this property is not considered in Dandawate's test. So we propose a novel spectrum sensing algorithm in this paper, which improves the performance of cyclostationary feature detection by using the relationship among the cyclic autocorrelations for different delays. The simulation results demonstrate that the improved method has a better performance in an additive white gaussian noise (AWGN) channel and a multipath fading environment.

To enable coexistence between an OFDM based opportunistic spectrum access system and a primary user, we proposed two novel methods called EAIC (Extended Active Interference Cancellation) and EAIC-H (EAIC-Hybrid) for spectral sculpting of OFDM signal. In EAIC and EAIC-H, cancellation signals are added to OFDM signal to cancel interference in target spectrum band caused by data tones, so that interference perceived by primary user can be limited. The cancellation signal (EAIC tones) has longer time duration than that of OFDM symbol, which enables a better notching capability than that of most existing methods. Optimal weights of EAIC tones have been analyzed and given in this paper. Simulation results show that the proposed methods can obtain very deep spectral notches of about 80 dB. Although the EAIC tones cause certain interferences to OFDM data tones, the SNR degradation of OFDM system is very limited and it can be acceptable for high order modulation such as 64 QAM. Compared with EAIC, EAIC- H provides a better tradeoff between notching performance and SNR degradation for high order modulation.

In this paper, we provide the design criteria of the nonlinear companding transforms for reduction in peak-to-average power ratio (PAPR) of multi-carrier modulation (MCM) signals, which can enable the original MCM signals to be transformed into the desirable distribution. As examples, some novel nonlinear companding transforms have been proposed to transform the amplitude or power of the original MCM signals into uniform distribution, which can effectively reduce the PAPR for different modulation formats and subcarrier sizes without any complexity increase and bandwidth expansion. It has been shown by computer simulations that the proposed schemes can significantly improve the performance of MCM systems including bit-error-rate and PAPR reduction.

Layered video multicasting can be used to accommodate the heterogeneity of users and their receive channels in wired and wireless system. In a wireless system using adaptive modulation and coding (AMC), total utility of all users can be increased by transmitting the video layers over different AMC modes and allocating proper bandwidth to these video layers. In this paper, it is shown that when the utility function is convex, the utility maximization problem can be formulated into a convex optimization problem. Then several important practical issues like limitation of layer numbers and operational layer rates are considered. With these limitations and no convex constraint on utility function, a genetic algorithm (GA) is proposed to maximize the total utility by layer rate allocation and selecting AMC modes to transmit the video layers. A 7-cell cdma2000 HDR system is used in the simulation to show the performance of the proposed methods.

In the distributed space time block coded (DSTBC) orthogonal frequency division multiplexing (OFDM) system (DSTBC-OFDM), since the transmit antennas belong to different nodes that are distributed, there may exist multiple carrier frequency offsets (CFOs) to the transmitters at the receiver. Several kinds of equalization techniques, which intend to mitigate the intercarrier interference (ICI) when detecting symbols, are compared in terms of the BER performance. And an equalization method based on iterative ICI cancellation is proposed. Simulation results show that when the difference of normalized CFOs (with respect to the subcarrier spacing) between distributed transmitters is not more than 0.2, the proposed iterative ICI cancellation method could combat ICI effectively at very low computational cost. However, in case of large difference of normalized CFOs, i.e., 0.4, the frequency offsets could cancel the space diversity gain of distributed STBC totally even when ideal ICI cancellation is assumed, and the performance is close to that of OFDM system using single transmit antenna without space time block coding

Layered video multicasting can be used to accommodate the heterogeneity of users in wired and wireless system. In a wireless system with adaptive modulation and coding (AMC) scheme, total utility of all users can be increased by transmitting the video layers over different AMC modes and allocating proper bandwidth to these video layers. The utility maximization problem can be formulated into a convex optimization when the utility function is convex. When considering the limit of number of layers, a 2-step convex optimization algorithm is proposed to maximize the total utility by properly selecting the AMC modes to transmit the video layers and allocating bandwidth. A 7-cell cdma2000 HDR system is used in the simulation to show the performance of the proposed methods.

In this paper, we investigate the performance of distributed space-time block coded (DSTBC) orthogonal frequency-division multiplexing (OFDM) system (DSTBC-OFDM) with the presence of carrier frequency offsets. Since the transmit antennas belong to different nodes that are distributed, there may exist multiple carrier frequency offsets to the transmitters at the receiver of DSTBC-OFDM system. Received signal model of DSTBC-OFDM system with multiple carrier frequency offsets is given. Then a frequency-domain equalization technique is proposed to mitigate the inter-carrier interference (ICI) and the inter-symbol interference (ISI) when detecting the symbols. The proposed equalizer has a reasonable performance and low computational complexity. Simulation results show that the equalization technique is quite effective when the carrier frequency difference between the distributed transmitters is small, and moreover, by setting the receiver's frequency to the mean value of the transmitters' frequencies, a better performance can be obtained

This letter addresses the carrier frequency offsets and channel gains estimation problem for MIMO channels with multiple carrier frequency offsets that are not assumed to be equal for all transmit-receive antenna pairs. Training sequences are proposed for both flat fading and frequency selective fading MIMO channels. Under the proposed training sequences, sub-optimal performance of parameter estimation can be achieved by a subspace-based method with reasonable computational complexity. The method can also provide flexible tradeoff between complexity and performance

In this paper a VBLAST-OFDM system with linear constellation precoding (LCP) is proposed for wideband wireless communications. Frequency diversity is obtained through the LCP precoding across OFDM subcarriers. Also a joint VBLASTLCP decoder which combines classical V-BLAST successive interference cancellation with Maximum Likelihood decoding for the precoding is proposed. Computer simulation shows that VBLAST-OFDM-LCP has a performance gain of at least 8.5dB at a certain symbol error rate compared with VBLAST-OFDM.

Relying on amicable orthogonal design, we develop for multiple-antenna systems a general differential space-time block code (GDSTBC), which imposes no restrictions on underlying signal constellation compared with the existing differential space-time designs. This generalization potentially allows the spectral efficiency to be increased by carrying information not only on phases but also on amplitudes. We then derive a noncoherent decoder (NCD) for flat Rayleigh fading channels. We will show that NCD may recover data symbols with full antenna diversity and linear complexity at high signal-to-noise ratio. Particularly, while three kinds of conventional signal constellations (QAM, PSK and APSK) are used in GDSTBC, we derive simplified versions of NCDs which can effectively reduce the implementation cost.

We study the turbo equalization approach for OFDM system with combined error control coding and linear precoding. While previous literature: employed linear precoder of small size for complexity reasons, we propose use of a linear precoder of size larger than or equal to the maximum length of the equivalent discrete-time channel in order to achieve full frequency diversity and reduce complexities of the error control coder/decoder. A low complexity LMMSE turbo equalizer is also derived for the receiver. Through simulation and performance analysis, it is shown that the performance of our proposed scheme over frequency selective fading channel reaches the matched filter hound: compared with the same coded OFDM without linear preceding, the proposed scheme shows an SNR improvement of at least 6 dB at a bit error rate of 10/sup -6 /over a multipath channel with exponential power delay profile. Convergence behavior of the proposed scheme with turbo equalization using various types of linear precoder/transformer, various interleaver sizes and error control coders with various constraint lengths is also investigated.