In this paper, channel estimation (CE) for up-link hybrid-field communications involving multiple Internet of Things (IoT) devices assisted by an active intelligent reflecting surface (IRS) is investigated. Firstly, to reduce the complexity of near-field (NF) channel modeling and estimation between IoT devices and active IRS, a sub-blocking strategy for active IRS is proposed. Specifically, the entire active IRS is divided into multiple smaller sub-blocks, so that IoT devices are located in the far-field (FF) region of each sub-block, while also being located in the NF region of the entire active IRS. This strategy significantly simplifies the channel model and reduces the parameter estimation dimension by decoupling the high-dimensional NF channel parameter space into low dimensional FF sub channels. Subsequently, the relationship between channel approximation error and CE error with respect to the number of sub-blocks is derived, and the optimal number of sub-blocks is solved based on the criterion of minimizing the total error. In addition, considering that the amplification capability of active IRS requires power consumption, a closed-form expression for the optimal power allocation factor is derived. To further reduce the pilot overhead, a lightweight CE algorithm based on convolutional autoencoder (CAE) and multi-head attention mechanism, called CAEformer, is designed. The Cramér-Rao lower bound is derived to evaluate the proposed algorithm’s performance. Finally, simulation results demonstrate the proposed CAEformer network significantly outperforms the conventional least square and minimum mean square error scheme in terms of estimation accuracy.

Cell-free massive multiple-input multiple-output (MIMO) systems, leveraging tight cooperation among wireless access points, exhibit remarkable signal enhancement and interference suppression capabilities, demonstrating significant performance advantages over traditional cellular networks. This paper investigates the performance and deployment optimization of a user-centric scalable cell-free massive MIMO system with imperfect channel information over correlated Rayleigh fading channels. Based on the large-dimensional random matrix theory, this paper presents the deterministic equivalent of the ergodic sum rate for this system when applying the local partial minimum mean square error (LP-MMSE) precoding method, along with its derivative with respect to the channel correlation matrix. Furthermore, utilizing the derivative of the ergodic sum rate, this paper designs a successive convex approximation based deployment optimization method to improve system deployment. Simulation experiments demonstrate that under various parameter settings and large-scale antenna configurations, the deterministic equivalent of the ergodic sum rate accurately approximates the Monte Carlo ergodic sum rate of the system. Furthermore, the deployment optimization algorithm effectively enhances the ergodic sum rate of this system by optimizing the positions of access points.

Faster-than-Nyquist (FTN) technique promises higher capacity and spectral efficiency for wireless communications. However, existing FTN studies over doubly-selective fading (DSF) channels separate channel-induced inter-symbol interference (channel-ISI) and FTN-induced ISI (FTN-ISI) to simplify cancellation. In practical DSF scenarios, the inherent coupling between FTN-ISI and channel-ISI causes significant performance degradation in conventional detection algorithms. To address this limitation, we first derive a practical FTN transmission model over DSF channels and construct the corresponding coupled interference matrix. Considering that data detection relies on efficient channel estimation, we propose a channel estimation algorithm with joint recovery of resolvable subpath parameters. This algorithm decomposes propagation paths into resolvable subpaths with independent delay-Doppler characteristics, achieving enhanced estimation accuracy through joint gain-phase optimization. Finally, building on the derived transceiver model and coupled interference matrix, we propose a whitening-enhanced orthogonal approximate message passing (WE-OAMP) algorithm that suppresses coupled interference through iterative linear-nonlinear estimation while maintaining spectral compactness. This algorithm constructs a whitening matrix using the FTN-ISI matrix to suppress noise correlation, and then performs detection through iterative linear and nonlinear estimation. Simulation results validate that the WE-OAMP algorithm outperforms benchmark algorithms in terms of bit error rate performance, especially in coded systems. Furthermore, we derive the achievable capacity of FTN signaling with WE-OAMP detection, demonstrating capacity improvement compared to Nyquist systems.

Cell-free (CF) massive multiple-input multipleoutput (MIMO) system based on local operations is recognized as a viable next-generation network architecture with high performance. Leveraging large-dimensional random matrix theory, we have derived a deterministic equivalent performance expression for the performance of a CF MIMO system employing local regularized zero-forcing (L-RZF) precoding, and based on this performance expression, we have designed a high-performance regularization parameter optimization method, termed statistical channel state information aided L-RZF (SCSI-L-RZF). Under SCSI-L-RZF method, the CPU only needs to optimize the regularization parameters for each AP by utilizing statistical channel state information (CSI), while each AP only needs to design precoding by combining the regularization parameter and local CSI to achieve good performance gains. Simulation results demonstrate that our SCSI-L-RZF scheme outperforms the current mainstream maximum ratio transmission (MRT) precoding schemes, L-RZF and local team minimum mean square error (LT-MMSE), further narrowing the performance gap between local and global precoding.

The convergence of hyper-reliable and low-latency communication (HRLLC) with industrial networking has been envisioned as the potential candidate for cyber-physical systems deployments, which increases the flexibility without moderating the stringent requirements of latency and reliability. Throughout the industrial network, event-triggered (ET) traffic bears serious performance degradation of timeliness, stemming from the resource preemption by time-critical time-triggered (TT) traffic under prioritized scheduling mechanisms. Furthermore, wireless links changes and finite blocklength regime in HRLLC result in transmission error, violation of latency requirement, and conservative resource allocation. Consequently, a cross-layer optimization framework is established to compress the total end-to-end (E2E) latency and bandwidth of ET flows under quality-of-service (QoS) constraints. The reliability is characterized with the decoding error probability with frequency diversity in finite blocklength regime, and the timeliness is characterized with the experienced E2E latency considering time offsets and routing of TT flows. Through spatial non-overlapping path planning that balances shorter length and fewer links contention, and physical optimal resource allocation that refines QoS-aware assignments of bandwidth, the number of subchannels, and decoding error probability threshold, the total latency compression and spectrum compaction can be achieved. Simulation results show the distinct performance gain of proposed cross-layer optimization framework compared with other methods.

Ultra-reliable and low-latency communications (URLLC) has great potential in wireless industrial automation, which increases flexibility without moderating the stringent quality-of-service (QoS). In URLLC-enabled Industrial Internet of Things (IIoT), mixed traffic is essential to support applications with differentiated latency requirements, where event-triggered (ET) traffic requires bounded end-to-end (E2E) latency, and time-triggered (TT) traffic demands deterministic latency. This article proposes a mixed traffic scheduling scheme with the preemption mode, in which TT traffic with a higher priority is allowed to preempt the transmission of ET traffic to ensure TT deterministic latency. However, this preemption may cause ET latency to exceed its requirements with constrained resources. To characterize the impact of TT preemption on the ET latency and the deterministic metrics of TT traffic, we derive the ET delay violation probability bound and the TT deterministic delay probability using the stochastic network calculus. To further achieve bandwidth-saving scheduling while ensuring the QoS requirements of mixed traffic, the credit-based shaper (CBS) configurations, radio resources and delay components are assigned under constraints of ultra-reliability, ET bounded latency and TT deterministic latency. Then, a three-step method is proposed to find the global optimal solution to minimize the total bandwidth. Simulation results validate our analysis and show the bandwidth-saving performance gain by jointly optimizing uplink and downlink resources with the proposed preemption-based scheduling scheme.

Existing channel estimation and data detection methods for faster-than-Nyquist (FTN) transmission over frequency-selective fading channels primarily face three key challenges: high pilot and guard interval overhead, low channel estimation accuracy, and long distances in satellite communication systems. To address the first two issues, we design a low-overhead frame structure based on circular convolution and, accordingly, propose a low-overhead precoding-driven channel estimation (PD-CE) algorithm. The proposed algorithm leverages circular convolution to suppress inter-block interference (IBI) from the channel with minimal guard intervals and eliminate FTN-induced IBI without guard intervals, significantly reducing pilot and guard overhead. Meanwhile, the limited guard interval mitigates noise enhancement, enabling PD-CE to achieve superior channel estimation accuracy over existing estimation methods. The third challenge arises from the imperfect channel state information obtained at the transmitter. To enhance the robustness in satellite communication systems, we design a precoding matrix based on the minimum mean square error (MMSE) criterion, introducing a low-overhead precoding-driven channel estimation and data detection (MMSE-PD-CEDD) algorithm for interference suppression. Simulation results indicate that, even under channel estimation error, the proposed MMSE-PD-CEDD algorithm exhibits superior interference resistance compared to existing algorithms, while its bit error rate performance loss remains within an acceptable range relative to the Nyquist criterion.

In this paper, authentication for mobile radio frequency identification (RFID) systems with low-cost tags is investigated. To this end, an adaptive modulus (AM) encryption algorithm is first proposed. To further enhance security without requiring additional storage for new key matrices, a self-updating encryption order (SUEO) algorithm is designed. Furthermore, a diagonal block local transpose key matrix (DBLTKM) encryption algorithm is presented, which effectively expands the feasible domain of the key space. Building upon these three algorithms, a novel joint AM-SUEO-DBLTKM encryption algorithm is constructed. Making full use of the strengths of the proposed joint algorithm, a two-way RFID authentication protocol, named AM-SUEO-DBLTKM-RFID, is proposed specifically for mobile RFID systems. In addition, the Burrows-Abadi-Needham (BAN) logic and security analysis indicate that the proposed AM-SUEO-DBLTKM-RFID protocol can effectively combat various typical attacks. Numerical results demonstrate that the proposed AM-SUEO-DBLTKM algorithm can save 99.59% of tag storage over traditional algorithms. Finally, the proposed AM-SUEO-DBLTKM-RFID protocol achieves both low computational complexity and low storage overhead, making it well-suited for deployment in resource-constrained, low-cost RFID tags.

Ultrareliable low-latency communication (URLLC) has been envisioned as the paradigm shift for wireless industrial automation in the coming industry 4.0. Despite the flexibility in conjunction with stringent requirements of latency and reliability, URLLC cannot guarantee the determinism demanded by industrial automation to provide an in-order and low-jitter delivery. Motivated by the evolvements in Rel-17 that strengthen the intrinsic determinism of 5G system, this article studies deterministic URLLC to achieve low-latency and bandwidth-saving scheduling of time-triggered traffic in Industrial Internet of Things (IIoT), instead of using time-sensitive networking (TSN) or hybrid TSN-URLLC. However, due to the queueing congestion and wireless links changes, it is challenging to achieve deterministic scheduling with bounded end-to-end (E2E) latency and low-loss probability. To ensure the determinism and schedulability of URLLC, offset s in gate control lists (GCLs) and radio resources are assigned under constraints of zero congestion, sequential arrival, bounded latency, and ultra reliability. To further improve the latency reduction and bandwidth saving of deterministic URLLC, we find the feasible transmission delays subject to the contradiction on monotonicity of the E2E latency and bandwidth with respect to the transmission delay, then propose a scheduling method to obtain the best solution of three cases by optimizing transmission delay, offset, bandwidth, and the number of subchannel. Simulation results validate the analysis and show the performance gain of the proposed method in three cases.

In this article, channel estimation (CE) of an active intelligent reflecting surface (IRS) aided uplink Internet of Things (IoT) network is investigated. First, the least square (LS) estimators for the direct channel and the cascaded channel are presented, respectively. The corresponding mean-square errors (MSEs) of channel estimators are derived. Subsequently, in order to evaluate the influence of adjusting the transmit power at the IoT devices or the reflected power at the active IRS on Sum-MSE performance, two situations are considered. In the first case, under the total power sum constraint of the IoT devices and active IRS, the closed-form expression of the optimal power allocation (PA) factor is derived. In the second case, when the transmit power at the IoT devices is fixed, there exists an optimal reflective power at active IRS. To further improve the estimation performance, the convolutional neural network (CNN)-based direct CE (CDCE) algorithm and the CNN-based cascaded CE (CCCE) algorithm are designed. Finally, simulation results demonstrate the existence of an optimal PA strategy that minimizes the Sum-MSE, and further validate the superiority of the proposed CDCE/CCCE algorithms over their respective traditional LS and minimum MSE (MMSE) baselines.

Ultra-reliable and low-latency communications (URLLC) plays a critical role for the coming era of wireless industrial automation, which increases the flexibility without moderating the stringent requirements of latency and reliability. The task for URLLC is to deliver short packets from sensors to actuators via the central controller reliably and timely, during which large bandwidth is required for ensuring stringent quality-of-service (QoS) metrics. Due to the scarce spectrum resource shared by a large number of devices, and the dynamic channel changes over wireless links, it is challenging to achieve bandwidth-saving URLLC with one single method. Motivated by the observation that groups of devices working in close proximity to each other can form device-to-device (D2D) communications, this paper considers multi-connectivity (MC) together with other cross-layer methods including grant-free access, data replication, broadcasting, and processor-sharing server to minimize the total bandwidth under the QoS constraints of URLLC. With the cross-layer design, we fisrt derive the packet loss probability including the factors of collision due to the contention-based access scheme and decoding error due to the dynamic wireless channel, and hence the overall reliability of MC is provided. Then, we establish a framework to minimize the total bandwidth of MC, where the relationship of collision and packet loss probabilities, the monotonicity of collision and decoding error probabilities, and the relationship of the third type blocklength and size rule are proved to find the optimal resource allocation. Simulation results validate the analysis and show the performance gain by optimizing resource allocation with the considered cross-layer design.

As an emerging technology for wireless communication, the intelligent reconfigurable surface (IRS) is made up of numerous low-cost passive elements with reconfigurable parameters, which can reflect signals with a certain phase shift and build a programmable communication environment. To reduce the high hardware cost and energy consumption in spatial modulation (SM), an IRS-aided hybrid secure SM (SSM) system with a hybrid precoder is proposed in this paper, where an optimization problem is formulated to maximize the secrecy rate (SR) by jointly optimizing the beamforming at IRS and the hybrid precoding at transmitter. For the IRS beamforming, an alternating direction method of multipliers (IRS-ADMM) scheme is first proposed. To achieve a higher SR, an IRS beamforming scheme via semidefinite relaxation (IRS-SDR) is put forward. To reduce the high complexity of IRS-SDR, we design a block coordinate ascend-based IRS beamforming scheme (IRS-BCA) with a closed-form solution. As for the hybrid precoding, two methods, called a successive convex approximation method based on the approximate secrecy rate (ASR-SCA) and a gradient ascend method based on cut-off rate (COR-GA), are presented. Simulation results show that the proposed IRS-ADMM and IRS-SDR harvest substantial SR performance gains over IRS-BCA. In comparison to the IRS-ADMM and IRS-SDR, the proposed IRS-BCA is of the lowest complexity at the cost of performance loss. Regarding hybrid precoding, the proposed ASR-SCA outperforms COR-GA in the high transmit power region. According to the complexity and SR performance of six combination methods including each IRS beamforming method and each hybrid precoding method, we selected three combinations: IRS-BCA plus ASR-SCA, IRS-ADMM plus ASR-SCA and IRS-SDR plus COR-GA. Moreover, it is showed that the SR performance achieved by the three combination methods is significantly higher than those of IRS with random beamforming and without IRS.

Ultra-reliable and low-latency communications (URLLC) is firstly proposed in 5G networks, and expected to support applications with the most stringent quality-of-service (QoS). However, since the wireless channels vary dynamically, the transmit power for ensuring the QoS requirements of URLLC may be very high, which conflicts with the power limitation of a real system. To fulfil the successful URLLC transmission with finite transmit power, we propose an energy-efficient packet delivery mechanism incorparated with frequency-hopping and proactive dropping in this paper. To reduce uplink outage probability, frequency-hopping provides more chances for transmission so that the failure hardly occurs. To avoid downlink outage from queue clearing, proactive dropping controls overall reliability by introducing an extra error component. With the proposed packet delivery mechanism, we jointly optimize bandwidth allocation and power control of uplink and downlink, antenna configuration, and subchannel assignment to minimize the average total power under the constraint of URLLC transmission requirements. Via theoretical analysis (e.g., the convexity with respect to bandwidth, the independence of bandwidth allocation, the convexity of antenna configuration with inactive constraints), the simplication of finding the global optimal solution for resource allocation is addressed. A three-step method is then proposed to find the optimal solution for resource allocation. Simulation results validate the analysis and show the performance gain by optimizing resource allocation with the proposed packet delivery mechanism.

Indoor localization based on existing Wi-Fi Received Signal Strength Indicator (RSSI) is attractive since it can reuse the existing Wi-Fi infrastructure. However, it suffers from dramatic performance degradation due to multipath signal attenuation and environmental changes. To improve the localization accuracy under the above-mentioned circumstances, an improved Spearman-distance-based K-Nearest-Neighbor (KNN) scheme is proposed. Simulation results demonstrate that our improved method outperforms the original KNN method under the indoor environment with severe multipath fading and temporal dynamics.

In this paper, we consider low-complexity user scheduling schemes for the multi-pair two-way relay channel, where L pairs of single antenna users are selected from K pairs to perform pair-wise information exchange via an Nr-antenna amplify-and-forward (AF) relay (Nr≥ 2L - 1) with analogue network coding (ANC). We first propose a simple channel norm (CN) based scheme, which enables low-complexity implementation. Then, we propose a near-optimal user scheduling by jointly considering the pair-wise channels norms and orthogonality among all the users (CNO-A), which requires global channel state information (CSI) and centralized computation at the relay. It is noted that CNO-A scheme can achieve comparable performance as the optimal scheduling with reduced computational complexity. Finally, we propose a two-step selective CNO scheme (CNO-S), which strikes a flexible balance between complexity/CSI overhead and performance. CNO-S significantly reduces CSI overhead and computational complexity at the relay, while achieving comparable performance as the CNO-A scheme. Numerical results and complexity analysis not only show that the proposed schemes are feasible and effective, but also demonstrate their advantages over the existing schemes.

This paper addresses the analog feedback of channel state information (CSI) in multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) based wideband wireless communication systems. In MIMO-OFDM systems, the amount of CSI to be fed back grows prohibitively with the number of subcarriers. Aiming at reducing the feedback overhead, we design efficient analog feedback schemes. In our approach, the user equipment (UE) samples the CSI so that only a small fraction of the CSI needs to be fed back to the base station (BS). Based on the sampled feedback information, the BS reconstructs full CSI using some interpolation method. Low complexity clustering/interpolation based schemes are first considered, but these schemes only provide limited feedback quality. To improve the performance, we analyze the sparsity of the time-domain channel impulse response and propose a time-domain filtering scheme based on the sparsity property. Simulation results show that the time-domain filtering scheme efficiently decreases the interpolation distortion and feedback error, and achieves much better performance than the clustering/interpolation based schemes.

Distributed multi-input multi-output (D-MIMO) system is a promising system to greatly improve the spectral efficiency and power efficiency of the cellular system. The performance analysis of the spectral efficiency of D-MIMO system is a fundamental problem for both theoretical study and technique evaluation. In this paper, the theoretical performances of spectral efficiency for D-MIMO system and traditional collocated MIMO (C-MIMO) system are studied and compared. First, a composite D-MIMO channel including path loss, shadow fading and multipath fading is given. Conditioned on the desired user position, by using the tight bounds and central limit theory, the analytical approximations of the mean and the cumulative distribution function (CDF) of the mutual information (MI) are derived for C-MIMO and D-MIMO channels at both high signal to noise ratio (SNR) and low SNR. Assuming that the users are randomly distributed in the cell, the CDFs of the spectral efficiency are also given for the C-MIMO and D-MIMO cellular system, and the closed-form expressions for the mean spectral efficiency, mean outage spectral efficiency are derived. Finally, the theoretical comparisons between C-MIMO and D-MIMO for large number of antennas are given, and simulation results are presented which validate the analytical results.

Under the assumption of single bounce channel model, the position and velocity of a mobile station (MS) can be determined by time of arrival (TOA), angle of arrival (AOA) and doppler-shifted frequency (DSF) measurements at three base stations (BSs) when line of sight (LOS) paths between the three BSs and the MS are all blocked. The equations relating the measured TOAs, AOAs and DSFs to the location parameters are nonlinear and under-determined which are hard to solve. A novel grid search method which requires only two-dimensional search in x-y coordinates and achieves good location accuracy is presented. Simulation results verify the effectiveness of the proposed method.

Based on the joint estimations of time of arrival (TOA), angle of arrival (AOA) and angle of departure (AOD) of two or more single-bounced multipaths from a single base station (BS) and an mobile station (MS) in an MIMO system, two schemes, closed-form total least squares solution (CF-TLS) and constraint-based grid-search approach (CB-GSA) are presented to locate an MS. The CF-TLS method is time saving and provides good location accuracy when two or more resolvable single-bounced multipaths are available. However, the CB-GSA method always provides better location accuracy than the CF-TLS method at a cost of large computational burden. When only two single-bounced multipaths can be observed or the range and angle measurement errors are large, the location accuracy of the CB-GSA method outperforms that of the CF-TLS method significantly. By executing the CF-TLS method to give an initial estimation of an MS position, the search area of the CB-GSA method is narrowed, and the location time is significantly reduced. Simulations verify the effectiveness of the two proposed methods.

We study the symbol error rate (SER) of maximum ratio transmission, transmit antenna selection, and codebook-based beamforming in correlated Rayleigh fading channels. Assuming maximum ratio combining is performed at the receiver, we derive a universal upper bound on the average SER of the three schemes, and prove that the bound is asymptotically tight in high signal-to-noise ratio (SNR) regions. However, numerical results show that at medium SNR, the tightness of the bound depends on the condition number of the channel correlation matrix.

This paper addresses a per user unitary precoding and rate control (PU2RC) system with adaptive multi-level quadrature amplitude modulation (QAM) modulation, where both the PU2RC operation and adaptive modulation are based on finite rate feedback. The system supports two transmission modes (single user transmit beamforming and multiuser transmission with orthogonal beamforming), and switches between the two modes to maximize the average throughput. We first study single user transmit beamforming, and derive closed-form expressions for the average throughput and bit-error rate (BER) with outdated channel information. Then, multiuser transmission with orthogonal beamforming is investigated. We analyze the throughput and BER performance without feedback delay in the normal signal noise ratio (SNR) regime, and the performance with feedback delay in the interference-limited regime. Simulation results demonstrate the effect of time delay, and the preferred regions of the single user transmission mode and multiuser transmission mode.

This paper investigates a quantized beamforming system with feedback delay. A linear channel predictor is used to cope with the feedback delay. We derive an upper bound on the symbol error rate (SER) of phase shift keying (PSK) signal. Based on the bound, we design a predictor that provides good error performance. We also demonstrate that the beamformer design methods developed in a delay-free scenario are applicable to the system with feedback delay.

Codebook-based transmit beamforming with receive combining is a simple and efficient strategy for multiple- input multiple-output (MIMO) wireless systems. However, as a closed-loop technique, codebook-based transmit beamforming suffers from the performance degradation due to feedback delay. In this paper, we design quantizers robust to the feedback delay for codebook-based beamforming systems. Aiming at maximizing the average receive signal-to-noise ratio (SNR) or minimizing the average bit error rate (BER), optimal codeword selection rules are derived for the quantizer. Numerical results reveal that the proposed quantizers well compensate the performance loss due to feedback delay.

In this paper, accurate approximations of the spectral efficiency are presented for co-located multiple-input multiple-output (C-MIMO) and distributed MIMO (D-MIMO) cellular systems in the composite channel model, which includes path loss, shadow fading, and multipath fading. Firstly, conditioned on the desired user position, the analytical approximations of the mean and variance of the mutual information are derived for C-MIMO and D-MIMO at high SNR. Because the exact distribution of the mutual information in a composite Rayleigh- lognormal environment is difficult to analyze, we apply Gaussian approximation to the distribution of the mutual information. Then, the growth in ergodic capacity and outage capacity of a D-MIMO channel with the number of antennas as well as the variance of the shadow fading is well understood. Assuming that the users are randomly distributed in the cell, the closed-form expressions for the mean spectral efficiency and mean outage spectral efficiency are derived. Finally, the numerical results are presented which validate the analytical results.

In a multiple-input multiple-output (MIMO) beam- forming system with finite rate feedback, the receiver sends back quantized channel information to the transmitter via a feedback channel. The overall system performance is degraded due to feedback errors. In this paper, we treat the feedback of the beamforming vector as a generalized vector quantizer (VQ), and adopt index assignment (IA) technique to cope with feedback errors. A lower bound to the average symbol error rate (SER) is derived, and an IA design criterion is proposed to minimize this bound, which is in accord with the pseudo-Gray coding principle in conventional IA design literature. Numerical results show that well-designed IA schemes improve the SER considerably.

In this study, a user selection algorithm aiming to optimize the signal-to-noise ratio (SNR) is proposed for MIMO BC systems. After setting a threshold value, only users whose channel gains are above the set threshold are considered. While achieving the full multiplex gain, this method can get a desirable sum capacity and BER performance. Compared with the exhaustive search scheme, the system overheads, such as the amount of feedback and the number of selection in the base station, decrease evidently.

In a single-input single-output (SISO) downlink with multiple users, to obtain the multiuser diversity gain, the full channel state information (CSI) for all users is required for selecting the 'best' user and transmission mode. However, feedback channels are often capable of carrying only a limited number of bits. With such rate-limited feedback links, we intend to maximize the sum-rate of systems with average power and bit-error rate (BER) constraints by varying the transmission mode according to feedback information. Althrough in multiuser systems with finite-rate feedback, the scheduled user is not necessarily the 'best' user, this work turns out to be a design of transmission modes based on CSI of the 'best' user. A nested iterative algorithm is proposed to obtain optimal thresholds and discrete transmission modes. Simulation results demonstrate the throughput with finite-rate feedback approaches that with perfect CSI when the feedback rate increases. As the number of users increases, the multiuser diversity gain is achieved and the average feedback load is reduced without the decrease of throughput.

A segmented phase noise mitigation algorithm is proposed for OFDM systems. By partitioning the received signal in the time domain, the mitigation can be processed segment by segment. This method can reduce the ICI effectively and lower the influence brought about by phase noise. The computing complexity is analyzed, and simulations are carried out to validate the proposed algorithm.

In a single-input single-output (SISO) downlink with multiple users, to obtain the multiuser diversity gain, the full channel state information (CSI) of all users is required for selecting the desired user and transmission mode. However, feedback channels are often capable of carrying only a limited number of bits. With such rate limited feedback links, we intend to maximize the average throughput of each user by designing transmit power levels, signal constellations and feedback thresholds jointly. Both un-coded adaptive M-ary quadrature amplitude modulation (MQAM) and adaptive trellis-coded modulation (TCM) are investigated. A nested iterative algorithm is proposed to obtain a finite number of transmission modes and thresholds. Optimization in the heterogeneous network is also considered. Simulation results demonstrate a small number of feedback bits make the throughput approach to that with perfect CSI. As the number of users increases, the multiuser diversity gain is achieved and the average feedback load of each user is reduced.

Index assignment (IA) technique is introduced to quantized beamforming systems. The feedback channel in these systems is modeled as a discrete memoryless channel (DMC), and the diversity and array gains with feedback errors are derived. Based on the analytical results, IA scheme is designed to provide a redundancy-free protection against feedback errors. Simulation results show that good IA scheme improves the array gain and symbol error rate (SER).

In this paper, we consider transmit beamforming systems with finite rate feedback and feedback errors. We model the feedback channel as a uniform symmetric channel and analyze the effects of feedback errors on outage probability, bit error rate (BER), diversity gain, and array gain. Both analytical and simulation results show that feedback error with small probability will make the system behave badly at high signal-to-noise ratio (SNR).

An effective method is presented for position tracking of a moving mobile terminal (MT). The method is based on time/distance difference of arrival (TDOA) estimation and Doppler measurements. TDOA locationing based on Chan's method provides an analytical solution which is closer to the Cramer-Rao Lower Bound (CRLB).However its accuracy is affected by multi-access interferences and non-line-of-sight (NLOS), thus making Doppler-aided positioning a good complementary technique due to its independency of the above effects. After acquiring the initial position of MT using TDOA information, the moving track is estimated by a hybrid method which combines TDOA with Doppler. Simulation results show this hybrid method can achieve better performance than TDOA or Doppler technique.

Transmit antenna selection (TAS), an effective technique for improving the performance of multiple antennal system, has been researched extensively under the scenario of block fading channel and no delay uplink. In this paper, the performance of the TAS-based multiple-input single-output (MISO) system with delayed uplink in temporally-correlated Rayleigh fading channel are investigated. The closed-form expression of average bit error rate (BER) and outage probability are derived. Those significant results demonstrate in the time-varying channel, TAS-based MISO system with delayed uplink can hardly achieve a full diversity order.

A location method of the user equipment (UE) in distributed antenna system (DAS) is presented, which uses the technique of the velocity measurement based on Doppler frequency shift. The location equations are formed from Doppler shifts of remote antennas (RAUs). Due to the nonlinear nature of the problem, the solution must be found by the grid searching. However, when the error of the Doppler measurement is a Gauss random variable, the solution can be achieved easily by a two- dimension searching in orthogonal complement space, i.e. the search is only performed in a two-dimension space (the position space) instead of the normal four-dimension space (both the position and the velocity spaces). Simulation results show that the proposed method can approach to a high precise of the UE location.

Using the duality of multiple input multiple output broadcast (MIMO BC) and MIMO multiple access (MIMO MAC), a lower bound and an upper bound of mutual information of MIMO BC systems with imperfect channel estimation are investigated. The gap between two bounds is considerably tight especially at high SNR region. The point-to-point MIMO system is a special case of our results. In the limit where the channel can be estimated perfectly, the mutual information converges to the case without errors. The effect upon the sum capacity of zero-forcing (ZF) preceding MIMO BC systems with channel estimation errors is also studied, which indicates that the sum capacity monotonously decreases as the covariance of channel estimation errors increases. At high SNR region, with fixed covariance of channel estimation errors, the multiplexing gain tends to be zero and the sum capacity will converge to a constant value as SNR increases.

Carrier frequency offset (CFO) estimation in an OFDM system is usually divided into fractional frequency offset (FFO) estimation and integer frequency offset (IFO) estimation. The former is generally done in the time domain while the latter in the frequency domain. We propose a maximum likelihood estimation algorithm for IFO estimation which works in the time domain using two FFOs with different estimation ranges. After introducing the concepts of frequency offset diagram, solution subset and minimum distance, we reveal the close relationship between the minimum distance and the error rate of the ML estimation result. Our further analysis exhibits a performance-range tradeoff which is tunable by the choice of the greatest common divisor (GCD) of two integers.