ABSTRACT This paper explores the practical application of source polar codes to entropy coding tasks in modern transform coding pipelines. Transform coding remains the predominant and rapidly evolving framework for compressing complex real‐world data. Despite the strong theoretical guarantees of polar codes, conventional polarization‐based compression techniques follow a “construct‐then‐use” paradigm, which proves inefficient and inaccurate when applied to transform coding scenarios characterized by highly dynamic entropy models. To overcome this limitation, we propose a construction‐free, plug‐and‐play polar compression scheme. Rather than relying on precomputed polarized entropies, our method selects output symbols based on probability vectors generated by a conditional entropy model. These vectors can be computed with low complexity and exact numerical precision, enabling efficient adaptation across diverse entropy coding tasks. The proposed approach offers greater flexibility than classical methods and achieves superior performance in the finite‐length regime.

Monotone chain polar codes generalize classical polar codes to multivariate settings, offering a flexible approach for achieving the entire admissible rate region in the distributed lossless coding problem. However, this flexibility also introduces significant challenges for existing successive cancellation (SC) based decoding schemes. Motivated by the need for a general SC decoding solution, we present a comprehensive decoding strategy for monotone chain polar codes that can handle arbitrary numbers of terminals, non-binary alphabets, and decoding along arbitrary monotone chains. Specifically, we formulate the SC decoding task as a series of inference subtasks over the polar transform and propose a computational graph framework based on probability propagation principles. This approach highlights the impact of variable switching during decoding and shows that time complexity varies between $O(N\log{N})$ and $O(N^2)$, depending on the specific chain structure. Moreover, we demonstrate that the widely used $O(N)$ space optimization is not universally applicable to monotone chain polar codes, which prompts us to introduce a constant-time decoder forking strategy based on the proposed logical computation graphs. This strategy enables time-efficient list decoding without relying on $O(N)$-space techniques. Numerical results verify the superior performance of the proposed scheme compared with the classical lazy-copy scheme.

This paper investigates energy efficiency maximization for movable antenna (MA)-aided multi-user uplink communication systems by considering the time delay and energy consumption incurred by practical antenna movement. We first examine the special case with a single user and propose an optimization algorithm based on the one-dimensional (1D) exhaustive search to maximize the user’s energy efficiency. Moreover, we derive an upper bound on the energy efficiency and analyze the conditions required to achieve this performance bound under different numbers of channel paths. Then, for the general multi-user scenario, we propose an iterative algorithm to fairly maximize the minimum energy efficiency among all users. Simulation results demonstrate the effectiveness of the proposed scheme in improving energy efficiency compared to existing MA schemes that do not account for movement-related costs, as well as the conventional fixed-position antenna (FPA) scheme. In addition, the results show the robustness of the proposed scheme to imperfect channel state information (CSI) and provide valuable insights for practical system deployment.

The rapidly evolving field of generative artificial intelligence technology has introduced innovative approaches for developing semantic communication (SemCom) frameworks, leading to the emergence of a new paradigm—generative AI-assisted SemCom (GSC). Benefiting from its strong ability to understand and generate high-quality content across various domains, this approach can effectively address the reconstruction limitations that challenge traditional SemCom systems. However, this architecture often suffers from high latency due to the complex processes involved in semantic extraction and generative semantic inference. To mitigate this issue, we propose a low-latency GSC framework, achieved by enabling the parallel execution of the transmitter’s semantic extracting and the receiver’s generating processes from a macro perspective. Furthermore, to attain more accurate and semantically aligned reconstruction, we design a temporal prompt engineering approach that utilizes reinforcement learning to sequence the temporal feature extraction steps at the transmitter. The results show that compared to the conventional GSC architecture, our designed framework can achieve a 52% reduction in residual task latency that extends beyond the fixed inference duration while only incurring an approximate 9% decrease in task score.

In this paper, we present OpenEmbedding, a distributed parameter server system for deep learning recommendation models (DLRM) workloads. In order to support rapid growth in the number of features and the model size (Terabytes are common) of DLRM workloads, OpenEmbedding takes advantage of emerging persistent memory (PMem) to address scalability and reliability issues in training DLRMs. Compared to DRAM, PMem can have much lower per-GB cost, higher density, and non-volatility, while with slightly low access performance to DRAM. OpenEmbedding uses DRAM as cache and PMem as storage for the sparse features and develops a simple but effective pipeline processing approach to optimize the access latency of the sparse features in PMem. For reliability, we develop a lightweight synchronous checkpointing scheme that is specially co-designed with the pipelined cache to reduce the run-time overhead of checkpointing. Our evaluations on a real-world industry workload consisting of billions of parameters demonstrate 1) the effectiveness of our PMem-aware optimizations, 2) checkpointing mechanism with near-zero run-time overhead to the training performance and 3) fast recovery with up to 3.97× speedup compared to the state-of-the-art. OpenEmbedding has been deployed in hundreds of scenarios in industry within 4Paradigm, and is open-sourced1.

Abstract Reconfigurable intelligent surface (RIS) has emerged as a promising technique for enhancing the performance of wireless networks. However, the traditional reflecting‐only RIS requires that the transmitter and receiver ought to be on the same side of the RIS, limiting the flexibility of RIS deployment. To overcome this drawback, a new simultaneous transmission and reflection reconfigurable intelligent surface (STAR‐RIS) has been proposed. Different from STAR‐RIS assisted half‐duplex systems in the existing literature, this work investigates a novel STAR‐RIS aided full‐duplex (FD) communication system. An FD base station (BS) communicates with an uplink (UL) user and a downlink user simultaneously over the same time‐frequency dimension assisted by a STAR‐RIS. The authors aim to maximise the energy efficiency by jointly optimising the transmit power of the BS and the UL user and the passive beamforming at the STAR‐RIS. The authors decouple the non‐convex problem into two subproblems and optimise them iteratively. The Dinkelbach's method is used to solve the power optimisation subproblem, whereas the penalty‐based method and successive convex approximation are applied to design the passive beamforming. The convergence and complexity of the proposed algorithm are also analysed. The simulation results demonstrate the superior performance of the proposed scheme compared with other baseline schemes.

Abstract The sixth generation advocates green communications, thus energy efficiency (EE) has become an important metric. In this study, a method to increase the EE of a reconfigurable intelligent surface (RIS) aided point‐to‐point communication system is proposed, where both sources are equipped with multi‐antenna and operate in the full‐duplex (FD) mode. The extra power consumption for self‐interference cancellation in the FD mode is considered and modelled as a linear function of the transmission power. The EE maximisation problem is divided into an active beamforming subproblem for the two multi‐antenna sources and a passive beamforming subproblem for RIS, and an alternative optimisation framework is adopted to solve them iteratively. Dinkelbach's method is used to address the fractional objective function in the active beamforming optimisation problem. The penalty method and successive convex approximation are exploited for passive beamforming design. Simulation results show that the scheme can greatly boost the EE performance compared with the half‐duplex mode and is superior to the sum‐rate‐maximisation scheme in larger transmission power settings. The proposed method can be used to extend the lifetime of communication devices without deteriorating communication performance.

Abstract A latest coding scheme named polarization‐adjusted convolutional (PAC) codes is shown to approach the dispersion bound for the code (128,64) under list decoding. However, to achieve the near‐bound performance, the list size of list decoding needs to be excessively large, which leads to insufferable time complexity. In this paper, to improve the speed of list decoding, fast list decoders for PAC codes are proposed. Four types of constituent nodes are defined and fast list decoding algorithms are provided for each of them. Simulation results present that fast list decoding with three types of constituent nodes can yield exactly the same error‐correction performance as list decoding, and reduce more than 50% time steps for the code (128,64). Moreover, fast list decoding with four types of constituent nodes can further reduce decoding complexity with negligible performance degradation.

Owing to the critical path loss of the millimeter wave band (mmWave), the signal sent by the base station (BS) is severely attenuated when it reaches indoors. Recent studies have proposed a glass-based metasurface that can dynamically control the scattering characteristics and realize mmWave focusing. Furthermore, such a surface can enhance indoor mmWave signals and improve the data rate. A transparent reconfigurable intelligent surface (RIS) focuses the received signals on a particular place indoors, where signal connection to the indoor users is required. An intelligent reflecting surface (IRS) or a conventional decode-forward (DF) relay can be installed in a particular place. This study compares the performance of the IRS connection with the conventional DF relay connection. In particular, the IRS connection is superior to the conventional DF relay connection when extremely high rates are required to minimize the transmit power and maximize the energy efficiency. In addition, when the BS is far away, the IRS connection requires more unit cells to achieve better performance than the conventional DF relay connection.

Abstract Puncturing and shortening are two common ways to achieve rate‐compatible non‐systematic polar codes (NSPCs). Systematic polar codes (SPCs) have been shown to outperform NSPCs with the same encoding and decoding complexity. However, rate‐compatible SPCs have never been comprehensively studied in previous work. In this paper, two rate‐compatible algorithms for SPCs are first proposed: uniform puncturing (UP) algorithm and uniform shortening (US) algorithm, which are referred to as SPC‐UP and SPC‐US, respectively. In order to effectively estimate the maximum likelihood decoding performance of punctured and shortened polar codes, subsequently, a distance spectrum calculation algorithm based on successive cancellation list (SCL) decoder for rate‐compatible polar codes is proposed. Simulation results show that rate‐compatible SPCs yield better bit error rate performance than rate‐compatible NSPCs while they have the same frame error rate performance under different code rates and decoding algorithms. Eventually, union bounds that are obtained by the distance spectrum to provide the theoretical explanation for the superiority of rate‐compatible SPCs are utilised.

On-line decision augmentation (OLDA) has been considered as a promising paradigm for real-time decision making powered by Artificial Intelligence (AI). OLDA has been widely used in many applications such as real-time fraud detection, personalized recommendation, etc. On-line inference puts real-time features extracted from multiple time windows through a pre-trained model to evaluate new data to support decision making. Feature extraction is usually the most time-consuming operation in many OLDA data pipelines. In this work, we started by studying how existing in-memory databases can be leveraged to efficiently support such real-time feature extractions. However, we found that existing in-memory databases cost hundreds or even thousands of milliseconds. This is unacceptable for OLDA applications with strict real-time constraints. We therefore propose FEDB ( F eature E ngineering D ata b ase), a distributed in-memory database system designed to efficiently support on-line feature extraction. Our experimental results show that FEDB can be one to two orders of magnitude faster than the state-of-the-art in-memory databases on real-time feature extraction. Furthermore, we explore the use of the Intel Optane DC Persistent Memory Module (PMEM) to make FEDB more cost-effective. When comparing the proposed PMEM-optimized persistent skiplist to the FEDB using DRAM+SSD, PMEM-based FEDB can shorten the tail latency up to 19.7%, reduce the recovery time up to 99.7%, and save up to 58.4% total cost of a real OLDA pipeline.

Polar codes have been adopted as the control channel coding scheme in the fifth generation new radio (5G NR) standard due to its capacity-achievable property. Traditional polar decoding algorithms such as successive cancellation (SC) suffer from high latency problem because of their sequential decoding nature. Neural network decoder (NND) has been proved to be a candidate for polar decoder since it is capable of one-shot decoding and parallel computing. In his paper, we propose a residual neural network decoder (RNND) for polar codes. Different from previous works which directly use neural network for decoding symbols received from the channel, the proposed RNND introduces a denoising module based on residual learning before NND. The proposed residual learning denoiser is able to remove remarkable amount of noise from received signals. Numerical results show that on one hand our proposed RNND outperforms traditional NND with regard to the BER performance under comparable latency. On the other hand, although the proposed scheme is a little inferior to the traditional successive cancellation list decoder when the BER performance is under consideration, it reduces significant decoding latency with the aid of the parallel structure of neural network and modern graphical processing units.

Carrier Aggregation (CA) is introduced by 3GPP to support much wider transmission bandwidth and higher data rate by aggregating multiple component carriers (CC). In the LTE-Advanced (LTE-A) systems with CA, the CC selection method is of great significance to the system efficiency and fairness because user equipment (UEs) may have different capabilities of aggregating carriers. This paper proposes a novel UE preference based dynamic CC selection (UP-DCS) algorithm to optimize system throughput, with an emphasis on dynamic load balancing among the CCs. UEs select their preferred CCs on the basis of both the load and channel conditions of different CCs. Simulation results demonstrate that the proposed algorithm can achieve a better system performance in terms of throughput, coverage and fairness compared to the existing algorithms.

Sparse code multiple access (SCMA) is a non-orthogonal multiplexing technique proposed to the 5G mobile communication system, which can improve the system performance in massive connectivity scenarios. However, during the random access phase of the SCMA system, the codebook used by a user is unknown to the base station. It is very likely some of the users will choose the same codebook in the same time-frequency resource. Therefore all the data in this resource block will be corrupted and can not be recovered. This paper proposes a new method to deal with collision situation by dividing the decoding process into two parts. First, we use a short pilot to detect the codebook used by each user in an SCMA system. Then, with the knowledge of codebook usage, the decoding of actual user data can be simplified. Using the proposed method, codebook collision can be detected. Although the conflicted users can't be decoded by the receiver, other users using the same resource block can still be recovered with relatively low performance penalty. The random access protocol we propose here is called SCMA-Aloha. Its peak throughput can be 20 percent higher than traditional slotted-Aloha.

In order to meet the growing demand of higher transmission rate, carrier aggregation (CA) technology has been proposed in 3GPP Release 10. In a backward compatible LTE-Advanced (LTE-A) system with CA, a well-designed component carrier (CC) selection scheme is critically significant for system performance, especially when lots of user equipment (UE) with different CA capabilities are coexisted. This paper presents a novel dual priority CC selection (DPCS) algorithm, considering not only UE's CA capability but also carrier load and channel conditions on different CCs. Simulation results demonstrate that the given algorithm is able to improve the system performance in terms of throughput and fairness compared to the existing algorithms.

In wireless video transmission, encoded video frames are often transmitted with certain delay and reliability constrain. Due to the limited wireless bandwidth, time-variant channel conditions and the fluctuant video frame rate, it is difficult to allocate transmission resources to guarantee online video quality. In this paper, we propose a fuzzy logic resource allocator (FLRA), aiming at enhancing the user experience of different online video streams and increasing the system load. By jointly utilize the information of remainder play time of user equipments (UEs) and the varying channel conditions of different UEs, FLRA employed in the base station calculates the priority list and makes allocation in real time. The proposed FLRA is implemented in a Long Term Evolution (LTE) simulation system, in which three kinds of video streams are supported. Numerical results verify that the proposed method outperforms the conventional methods significantly, especially in high signal-to- noise ratio condition.

Given the enormous demands for location based applications and service, localization technologies, which estimate accurate locations of the mobile users, have been developed rapidly outdoors (e.g., GPS) and indoors (e.g., WiFi fingerprinting). However, in half-open areas where existing studies are rarely conducted, either the outdoor or indoor localization technique fails to achieve a satisfactory performance due to the short of signals. In this paper, we propose a hybrid localization method tailored for the use in half-open areas. Specifically, we present two individual probabilistic localization techniques, which are extended from conventional GPS and WiFi fingerprinting. Moreover, our hybrid approach combines the locations estimated by the two techniques, and modifies the estimation by an enhanced Kalman filter leveraging the user movement capacity as well as the location probability. The experiment results show our hybrid localization method outperforms the conventional GPS and WiFi fingerprinting based methods in half-open areas.

The resources of dedicated accelerators (e.g. FPGA) are still required to bridge the gap between software-based Middleboxs(MBs) and the commodity hardware. To consolidate various hardware resources in an elastic, programmable and reconfigurable manner, we design and build a flexible and consolidated framework, OpenANFV, to support virtualized accelerators for MBs in the cloud environment. OpenANFV is seamlessly and efficiently put into Openstack to provide high performance on top of commodity hardware to cope with various virtual function requirements. OpenANFV works as an independent component to manage and virtualize the acceleration resources (e.g. cinder manages block storage resources and nova manages computing resources). Specially, OpenANFV mainly has the following three features. (1)Automated Management. Provisioning for multiple Virtualized Network Functions (VNFs) is automated to meet the dynamic requirements of NFV environment. Such automation alleviates the time pressure of the complicated provisioning and configuration as well as reduces the probability of manually induced configuration errors. (2) Elasticity. VNFs are created, migrated, and destroyed on demand in real time. The reconfigurable hardware resources in pool can rapidly and flexibly offload the corresponding services to the accelerator platform in the dynamic NFV environment. (3) Coordinating with Openstack. The design and implementation of the OpenANFV APIs coordinate with the mechanisms in Openstack to support required virtualized MBs for multiple tenants.

Two-way relaying in wireless systems has initiated a large research effort during the past few years. In particular, structured codes and lattices are instrumental for achieving high rates when using Physical Layer Network Coding (PLNC). In an attempt to bring the gains of PLNC beyond the classical traffic pattern of two-way relaying, in this paper we consider a scenario with four-way relaying, where each of the two Mobile Stations (MSs) has a two-way connection to the same Base Station (BS), while each connection is through a dedicated Relay Station (RS). The two RSs are in the range of the BS, but they are at antipodal positions within the cell and do not interfere with each other, i. e. achieve a perfect spatial reuse. We introduce communication schemes for serving the four communication flows in two transmission phases. Each phase consists of combined broadcast and multiple access. The main design ingredients are dirty paper coding nested lattice code codes. We compare the performance with a reference scheme that utilizes Decode-and-Forward (DF). The results show that the usage of structured codes in the four-way relaying scenario can significantly increase the achievable rate region.

This paper studies the suppression of frequency- hopping (FH) narrowband interference (NBI) in spread-spectrum (SS) systems. Existing NBI suppression algorithms focus on the case that the center frequency of the NBI is invariant and the performance degrades severely when the NBI is frequency-hopping. In this paper, an FH-NBI suppression algorithm is proposed based on the time- domain least mean square (LMS) prediction filter. The proposed algorithm detects the instant when the NBI hops from one frequency to another, and updates the filter coefficients using a novel method, which avoids the convergence time required in the conventional LMS algorithm. Both theoretical analysis and simulation results show that the proposed algorithm can suppress FH-NBI much more effectively than the existing algorithms.

In this paper, we develop a cross-layer design incorporating adaptive modulation coding (AMC) and truncated hybrid automatic repeat request (HARQ). We define different packet error rate (PER) constraints for each (re)transmission, thus adopting dynamic switching thresholds correspondingly to fully exploit the combining gain in HARQ protocols. Analytical expressions for performance metrics are derived, based on which both multidimensional and simplified single-dimensional optimization problems are proposed to maximize the average spectral efficiency. Numerical results are obtained for analysis and our design has shown considerable performance improvement compared with conventional switching thresholds. Moreover, the single-dimensional optimization approach implies a suitable tradeoff between throughput and complexity.

Supporting the quality of service of unlicensed users in cognitive radio networks is very challenging, mainly due to the dynamic resource availability induced by the licensed users' activities. In this paper, we derive the optimal admission control and channel allocation decisions in cognitive overlay networks to support delay sensitive communications of unlicensed users. We formulate it as a Markov decision process problem, and solve it by transforming the original formulation into a stochastic shortest path problem. We then propose a simple heuristic control policy, which includes a threshold-based admission control scheme and and a largest-delay-first channel allocation scheme, and prove the optimality of the largest-delay-first channel allocation scheme. We further propose an improved policy using the rollout algorithm. By comparing the performance of both proposed policies with the upper-bound of the maximum revenue, we show that our policies achieve close-to-optimal performances with low complexities.

In this paper, a group-wise joint detection (JD) algorithm is proposed for dual rate time division-synchronization code division multiple access (TD-SCDMA) systems. The proposed algorithm first groups users based on their data rates and then deals with them group by group starting from the group of high data rate users (HRUs). Simulation results illustrate that compared to conventional JD algorithms, the proposed algorithm can improve the performance of low data rate users (LRUs) significantly while having similar performance for HRUs, with negligible computational complexity increase. It is worth mentioning that the proposed group-wise JD algorithm can be easily extended for the application in multi-rate TD-SCDMA systems.

In this paper, we proposed a new velocity estimation algorithm for orthogonal frequency division multiplexing (OFDM) systems, which is based on the variance of channel frequency response (CFR) of two adjacent OFDM symbols. The proposed algorithm can fast and accurately estimate the velocity in both Rayleigh and Rician fading channels, and is low in computational complexity and memory storage. Simulation results show that, compared with the conventional crossingbased and covariance-based velocity estimation algorithms, the proposed algorithm can reduce estimation error by nearly 20% on average.

The release of IEEE 802.15.4 medium access control and physical layer specifications, employing carrier sense multiple access/collision avoidance (CSMA/CA) strategies, represents a significant milestone in promoting deployment of wireless sensor networks. In this study, the authors first analyse the performance of the slotted CSMA/CA strategy specified in the contention access period (CAP) of IEEE 802.15.4 by integrating the discrete-time Markov chain models of the node states and the channel states; and then, extend the Markov chain models by adopting a modification to the CAP. The extended models could be used to analyse the performance of the unslotted CSMA/CA strategy specified in IEEE 802.15.4 as well as that of the slotted CSMA/CA strategy. Extensive simulations demonstrate the accuracy and effectiveness of the proposed models and conclusions.

The cyclic prefix comb spectrum code division multiple access (CP-CS-CDMA) has comb spectrum in frequency domain. Its different code groups can be separated with a simple hopping accumulator. In the paper, we combine OFDMA and CP-CS-CDMA, and propose a new multiple access scheme, hybrid multiple access (HMA). Then we propose a new reuse partitioning system with HMA. The cell of the system has two regions: the inner region with OFDMA and the outer region with CP-CSCDMA. The proposed system makes full use of the advantages of OFDMA and CDMA to improve the performance. Theoretical analysis and simulation results have verified that our HMA and the new reuse partitioning system significantly increase the capacity compared with the conventional OFDMA system.

Proper admission control in cognitive radio networks is critical in providing QoS guarantees to secondary unlicensed users. In this paper, we study the admission control and channel allocation problem in overlay cognitive radio networks under the maximum cumulative delay constraint. We formulate it as a Markov decision process problem, and then solve it by transforming the original formulation into a stochastic shortest path problem. We further simulate the performance of a class of threshold-based admission control with the largest-delay-first channel allocation policy, and show its advantage over other two benchmark policies.

When an IEEE 802.15.4 network has many nodes and is almost saturated, the probability of collision is large, and the throughput is small. The main reasons are the adoption of slotted CSMA/CA and the mechanism that if a data transmission cannot be completed before the end of contention access period (CAP), it has to wait until the start of the CAP in the next superframe. This paper proposes an enhanced collision-avoidance MAC protocol for IEEE 802.15.4, and establishes a simulation model to analyze and compare the enhanced collision-avoidance MAC and the IEEE 802.15.4 MAC. The proposed protocol, compatible with the IEEE 802.15.4 protocol, has less probability of collision (almost 0), higher probability of successful transmission (close to 1), and larger network throughput (more than two times of that of IEEE 802.15.4).

Narrowband interference (NBI) affects performance of orthogonal frequency division multiplexing (OFDM) system to a great extent. Although NBI can be suppressed by employing time-domain prediction error filter (PEF), its effectiveness is reduced due to the usage of guard band which interrupts the spectrum. This paper discusses this problem and proposes two methods to improve the efficiency of PEF in the presence of guard band. Simulations show that proposed methods outperform conventional PEF in the signal to interference plus noise ratio (SINR). Moreover, channel effect caused by PEF must be compensated before demodulation in OFDM system, which is also discussed in this paper.

This paper proposes a feed-forward neural network predictor to adaptively estimate and suppress the narrowband interference (NBI) in the Direct Sequence-Code Division Multiple Access (DS-CDMA) signal. The iterative code-aided estimation is used to further improve the system performance. Simulation results reveal that the proposed algorithm outperforms conventional linear prediction filtering and recurrent neural networks (RNN) based NBI rejection methods, in different interference models.

Reading efficiency is one of the key factors to evaluate Radio Frequency Identification (RFID) systems. For the system using multi-branch protocols, the performance would be better if the tags are properly divided into multiple groups. This paper firstly gives the closed-form of system efficiency for binary tree algorithm. Based on the theoretical analysis, the optimal branches number is derived. An efficient multi-branch tree (EMBT) algorithm is proposed subsequently, along with a tag number estimation algorithm and performance evaluation. Both theoretical analysis and simulation results indicate that multi-branch tree algorithm has better performance than the conventional binary tree algorithm. System identification efficiency of the proposed method can achieve above 45%, while that of the binary tree algorithm is only 34.8%.

Trellis-coded modulation (TCM) is an attractive coded modulation technique which yields significant coding gain without bandwidth expansion. So far, researches concerning the optimization of TCM codes under Gaussian channels have concentrated on minimizing the error-event probability (EEP). The criterion is to maximize the free Euclidean distance of TCM coded sequences. However, for bitwise communication systems, minimizing the bit error rate (BER) is particularly important. This paper illustrates that the conventional TCM coding criterion is not sufficient in terms of minimizing BER. The reason is that the BER depends on not only Euclidean but also Hamming distances. We then propose a new criterion for TCM codes design, which considers both Euclidean and Hamming distances. Based on the proposed criterion, a general design method is given. A number of optimum TCM codes are designed by using this method. Simulation results verify that, compared with the conventional best-performed TCM codes, our codes not only yield the minimum EEP but also achieve better BER performance.

Spectrum detection for vacant bands is one of the key techniques in cognitive radio (CR) systems. Cooperative detection outperforms single user detection in many aspects. The existence of malicious user could severely degrade the performance of cooperative CR systems. In this paper, a new cooperative detection scheme with malicious user suppression is proposed, which has lower complexity and better performance compared with the existing one. Simulation results show that when 25% users in the system are malicious, our proposed method can introduce more improvement of missed detection probability by nearly 20%.

In the orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems, adaptive resource allocation should consider the out-of-band interference. In this paper, allocation schemes with different optimization targets and multiple practical constraints are studied. Nonlinear equations for the optimal allocation are deduced. Two suboptimal methods based on bit removal procedure are also proposed. Simulation verifies that the proposed methods can reach a better tradeoff between the optimization target and other constrained performance.

Orthogonal frequency division multiple access (OFDMA) relay networks have been drawing much more attention in recent years. Cross layer optimization has been shown to be an efficient tool for resource allocation in OFDMA relay networks. However, the fairness issues for OFDMA relay networks have not been examined clearly in existing literature. In this paper, we considered a relay scenario with multiple sources, multiple relays and a single destination. A complete-fairness cross layer optimization framework is proposed, which considers fairness for both sources and relays. The simulation results show that the proposed algorithm outperforms the existing fairness algorithms significantly.

Network utility maximization (NUM) is an effective tool in cross layer design. This paper extends the traditional NUM into a more general framework of weighted network utility maximization (WNUM) and studies the multi-user resource allocation problem in orthogonal frequency division multiple access (OFDMA) networks. Combined queuing priority is introduced to serve as a tool which helps to configure the weight parameters. Mathematical solutions to WNUM with various constrain sets in OFDMA systems are also provided. The presented WNUM optimization helps to reduce the complexity in cross layer design and is capable of making trade-offs between efficiency and fairness. Numerical results show that the WNUM scheme can outperform throughput-optimal scheme in both average queue length and average delay time.

In orthogonal frequency division multiplexing (OFDM) systems, automatic gain control (AGC) and direct current estimation (DCE) need to take longer time than the single carrier case. It is due to the fact that OFDM signals in the time domain appear as Gaussian distribution, therefore estimation errors in the AGC and the DCE will occur because of the clipping effect introduced by analog to digital converters (ADCs). Moreover, the AGC and the DCE will interfere with each other when operated separately, and generate larger estimate errors. This paper proposes a joint method for both AGC and DCE (AGC-DCE) based on distribution function for OFDM systems. The method overcomes the clipping effect of ADC and the interference between the separate operation of AGC and DCE. Simulation results demonstrate that only after one time adjustment, the proposed AGC-DCE method achieves less than 0.13 dB power estimate error and less than 71 mV DC estimate error, whereas the traditional method provides less than 20 dB and less than 550 mV estimate errors respectively. If the same estimate errors are reached, the estimate delay of the proposed AGC-DCE method is 33.3%-66.7% of that of the traditional method.

The system efficiency is one of the key factors for RFID (Radio Frequency Identification) systems. This paper gives theoretical study on binary tree algorithm that is adopted by the international standard ISO 18000-6B. The close form for calculating system efficiency is derived. Simulation is performed and results agree with theoretical one. In addition, the simplified expression for very large tag number is also provided.

The mergence of VoIP and WLAN has created a new wireless platform for voice service delivery - VoWLAN. Focusing on the hot spot of WLAN research, we figure out the capacity bottleneck of VoWLAN and the QoS problem of the VoIP service existing in current solutions such as M-M (Multicast Multiplexing) scheme. A novel unicast multiplexing (U-M, Unicast Multiplexing) scheme to improve the voice capacity and guarantee the QoS of VoIP service in WLAN is proposed. Both theoretical and simulation results show that the U-M scheme doubles the number of VoIP users in VoWLAN as compared with traditional scheme and reduces the packet lost rate by nearly 10% as compared with M-M scheme. Meanwhile, U-M is also a power-saving scheme in the voice-dominant application typical of VoWLAN.

For orthogonal frequency-division multiplexing (OFDM) communication systems, the frequency offsets in mobile radio channels distort the orthogonality between subcarriers resulting in intercarrier interference (ICI). This paper studies an efficient ICI cancellation method termed ICI self-cancellation scheme. The scheme works in two very simple steps. At the transmitter side, one data symbol is modulated onto a group of adjacent subcarriers with a group of weighting coefficients. The weighting coefficients are designed so that the ICI caused by the channel frequency errors can be minimized. At the receiver side, by linearly combining the received signals on these subcarriers with proposed coefficients, the residual ICI contained in the received signals can then be further reduced. The carrier-to-interference power ratio (CIR) can be increased by 15 and 30 dB when the group size is two or three, respectively, for a channel with a constant frequency offset. Although the redundant modulation causes a reduction in bandwidth efficiency, it can be compensated, for example, by using larger signal alphabet sizes. Simulations show that OFDM systems using the proposed ICI self-cancellation scheme perform much better than standard systems while having the same bandwidth efficiency in multipath mobile radio channels with large Doppler frequencies.