Amid the rise of Sixth Generation (6G) wireless net-works, expected to support vast connectivity and highly reliable transmissions, devising methods for efficient spectrum reuse is vital. This paper delves into the coexistence challenges facing enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communications (URLLC) services in cellular networks, proposing a superposition framework for transmitting multiple URLLC packets alongside a single eMBB user's signal. We've developed a mathematical model and formulated the URLLC allocation as a mixed-integer non-linear program (MINLP) to optimize URLLC admission rates and eMBB rate loss. We utilize one-to-many bipartite matching for eMBB-URLLC pairings and adopt the Non-Orthogonal Multiple Access (NOMA) for superim-posed transmissions, with a power allocation model. For unpaired users, a puncturing approach is used. The subsequent simulation showcases substantial performance gains from our approach.

To meet the extremely low latency requirements of 6G Internet of Things (IoT) services, 6G network should be able to intelligently allocate the network resources. Based on Mobile edge computing (MEC) and network function virtualization (NFV), the 6G NFV/MEC-enabled IoT architecture will be a viable architecture to enable flexible and efficient resource allocation. The architecture will enable the deployment of service function chains (SFCs) in NFV-enabled network edge nodes. However, due to the heterogeneous and dynamic nature of 6G IoT, it is a challenge to deploy SFCs rationally. Therefore, this paper proposes a knowledge-assisted deep reinforcement learning (KADRL) based SFC deployment scheme. The scheme achieves flexible and efficient resource allocation by deploying SFCs at appropriate edge nodes for the requirements of 6G IoT services. Simulation results demonstrate that KADRL can achieve better convergence performance and can meet the requirements of delay-sensitive IoT services.

For the Belief Propagation (BP) algorithm of low-density parity-check (LDPC) codes, existing deep learning methods have a limited performance improvement and it is difficult to train deep-level networks. In this paper, a model-driven quasi-residual network (Quasi-ResNet) BP decoding architecture is proposed for LDPC codes to further improve the performance of standard BP decoding. This method feeds the reliable messages calculated in current iteration into the next iteration based on the shortcut connection, and adjusts the weight of shortcut connection based on the error Back Propagation algorithm of neural network to determine the optimal genetic proportion of reliable messages. The decoding architecture is composed of a model-driven deep neural network (DNN) and shortcut connection. Simulation results show that the decoder can not only unfold more layers quickly compared with the DNN-based BP decoder, but also further improve the decoding performance.

In order to meet the strict quality of service requirements of delay-sensitive networks, this paper studies resource-autonomous decision-making algorithms for 6G MEC networks to improve key performance indicators (KPIs) such as delay, computing rate, and system stability. This paper considers task offloading and resource allocation decisions in multi-user MEC networks with time-varying channels, where user task data arrive randomly. We designed an autonomous decision-making algorithm for Lyapunov Assisted Deep Reinforcement learning (Ly-DRL) that satisfies the data queue stability and average power constraints and maximizes the network computing rate. We construct a dynamic queue of user task data through queue theory and apply Lyapunov optimization theory to decouple the MINLP problem into subproblems for each time slot. Combining DRL and traditional numerical optimization, the subproblems of each slot are solved with low computational complexity. Simulations show that the algorithm performs best while stabilizing the data Queue.

In response to the explosive growth of data computation in vehicular terminals, computation offloading has emerged as a viable solution to mitigate the limitations of resources. Efficient offloading decisions not only meet the demanding requirements of complex vehicular tasks in terms of time, energy consumption, and computational performance but also minimize competition and resource consumption in the network. However, existing work on task offloading in vehicular networks often exhibits certain limitations, such as incomplete consideration of relevant factors or suboptimal utilization of available resources. This research presents the construction of a three-layer vehicular network environment, which is based on cloud and edge computing paradigms. The design entails the formulation of real-time vehicle location tracking and task priority metrics, while also considering the challenges posed by time-varying channels and signal blockage prevalent in vehicular network environments. In this paper, a novel variant of the Deep Deterministic Policy Gradient (DDPG) algorithm NLDDPG is proposed to iteratively train the model, aiming to optimize a weighted objective function. Simulation results show that this algorithm can improve the efficiency and optimize the task average utility.

The coexistence of enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) is common in 5G networks. 5G services require eMBB users to achieve higher data rate, and URLLC users to meet high reliability and low latency requirements. How to utilize limited resources to maximize the throughput of eMBB users in the system while meeting URLLC latency requirements is a very meaningful issue. In this paper, we delve into the latency composition of URLLC packets and subsequently derive an expression to determine the number of mini-slots that URLLC packets can be queued. We propose a greedy scheduling algorithm based on queuing theory, which solves the complex scheduling problem of URLLC packets with different latency requirements. At each mini-slot, we dynamically schedule the URLLC packets that arrive using the proposed algorithm. We demonstrate the significant advantages of our algorithm compared to other algorithms through extensive simulations. Specifically, our algorithm significantly reduces the throughput loss of eMBB users, and also meets the high reliability requirements of URLLC in the case of high URLLC load.

In recent years, human skeleton tracking technology has attracted a lot of attention in the fields of virtual reality, human-computer interaction and medical rehabilitation. Human skeleton tracking technology is the basis for building human models in virtual reality scenarios. Among them, Kinect camera is widely used as a motion tracking sensor for virtual reality human-computer interaction. However, many current studies on skeleton point tracking are limited to single or dual camera systems, which leads to problems such as occlusion, missing skeleton data and errors. To solve the problems of limited capture range and data occlusion of a single Kinect camera, this paper proposes a skeleton point tracking method based on multiple Kinect cameras. The method uses multiple Kinect cameras to track the 3D coordinates of 32 body joints simultaneously, and unifies the joint coordinates captured by multiple Kinect cameras in different viewpoints into the same world coordinate system through coordinate transformation. The BP (Back Propagation) neural network is used to train the skeleton data from multiple viewpoints, thus generating a reliable user skeleton position in real time. By this method, the problems of the existing methods for obtaining skeleton points in a single camera view are solved.

Sparse code multiple access (SCMA) is a competitive non-orthogonal multiple technique for 5G, which can accommodate more communication users by exploiting the multi-dimensional codewords. However, it is inflexible to meet the various demands of detection performance, or the various demands of computational complexity in real scenario. In this paper, we propose two simplified algorithms to deal with the problem, named GAMP-MPA and SIC-GAMP-MPA. These two algorithms are developed from the mainstream SCMA detector algorithm, message passing algorithm (MPA), and inspired by generalized approximate message passing (GAMP) as well as serial interference cancellation (SIC). The modified MPA benefits from the GAMP method since GAMP converts vector problems to scalar problems which provides higher computational efficiency and easier hardware implementation. Simulation results show that the GAMP- MPA achieves better performance than MPA with an acceptable extra complexity cost, and SIC-GAMP-MPA has significantly decreased complexity compared to MPA with similar performance. Particularly, the advantages of both GAMP-MPA and SIC-GAMP-MPA are more apparent in low signal-to-noise ratio (SNR) condition.

This paper considers imperfect channel state information (CSI) in the downlink precoding of cellular multi-user large-scale distributed antenna system (DAS). Specifically, the uncertainty of channel estimation is mainly caused by the pilot reuse among users in adjacent cells. The phenomenon is termed as pilot contamination in a multi antenna system. Since the large-scale fading may vary from antenna to antenna, it's not easy to jointly estimate the channels from all antennas to a certain user. Instead, this paper only jointly estimates the downlink channels of the antennas at the same site, therefore with the same large-scale fading. Consequently, the channel estimation is executed in a distributed manner while the subsequent precoding is done in a centralized way. Based on the analysis of the properties of the DAS channel estimations, a multi-cell MMSE precoding scheme is proposed which aims to minimize the sum of intra- cell interference and the interference the cell pours to other cells. An approximation of the multi-cell MMSE precoding is further derived to reduce the computational complexity. Simulation results show significant performance gains over traditional singlecell precoding schemes.

Ultra dense network (UDN) is identified as one of the key enablers for 5G since it can provide ultra high spectral reuse factor exploiting proximal transmissions. By densifying the network infrastructure equipments, it's highly possible that each user will have one or more dedicated serving base station antennas, introducing the user-centric virtual cell paradigm. However, due to irregular deployment of large amount of base station antennas, the interference environment becomes rather complex, thus introducing severe interferences among different virtual cells. This paper focuses on the downlink transmission scheme in UDN where a large number of users and base station antennas are uniformly spread over a certain area. An interference graph is first created based on the large- scale fadings. Then, base station antennas and users in the virtual cells within the same maximal connected component are grouped together and merge into one new virtual cell cluster, where users are jointly served via zero-forcing beamforming. A multi-virtual-cell minimum mean square error precoding scheme is further proposed to mitigate the inter-cluster interference. Simulation results show that the proposed interference graph based virtual cell merging approach can attain the average user rate performance of the grouping scheme based on virtual cell overlapping with smaller virtual cell size and reduced signal processing complexity.

This paper studies the allocation of information flows in noiseless, memoryless communication networks in the presence of omniscient Byzantine adversary. In such networks, adversary may maliciously modify some edge-flows, and legitimate users should resort to network error correction strategies to transmit data reliably. Unlike prior papers, which focused on the capacities of the networks, we consider the expense of resources used by the flow. Hereby, this paper uses an optimization problem to define the concept of minimum cost network error correction flows. We provide a necessary and sufficient condition of feasibility of the allocation problem, and derive a cut-set outer bound on the feasible region. Using this cut-set bound, we can find the minimum cost network error correction flow in some instances. Moreover, we also consider the relationship between incoming edge-flows and outgoing edge-flows of a vertex. As for the directed acyclic graphs, we propose an algorithm to allocate the network error correction flow. This algorithm is with polynomial time complexity, and proves to be optimal when recoding at intermediate nodes is forbidden. Additionally, in order to justify the necessity of recoding at intermediate nodes, we analyze the benefit of intermediate recoding. On the one hand, we construct a series of instances to prove that intermediate recoding can bring enormous benefits in some networks. On the other hand, numerical analysis shows that the benefit is modest in small random graphs.

In this work, we research the multiuser detection (MUD) in a new Single Carrier (SC) massive Spatial-Modulation-Multiple-Input-Multiple-Output (SM-MIMO) system, where the Base Station (BS) is equipped with massive antennas connecting to independent Radio-Frequency (RF) chains designed by low-resolution Analog-to-Digital Converters (ADCs). Different User Equipments (UEs) possess multiple Transmit Antennas (TAs) but only one RF chain, and transmit data-bits to the BS by Cyclic-Prefix Single-Carrier (CP-SC) SM. We construct a new Message Passing De-Quantization Detector (MPDQD) based on the MPDQ algorithm in Compressed Sensing (CS). MPDQD exploits both the special structures (e.g., sparsity) and prior probability distribution of the transmitted signal, so it reaches superior detection performances. It involves the parallelized matrix-vector multiplication as the most complex operation, so it has low complexity and is suitable for hardware implementation. Simulation results show that MPDQD outperforms the linear detectors, and can approach the performance of the un-quantized system even when ADC resolution is low (e.g., 4bits).

Cloud-RAN is an entirely new wireless radio access paradigm based on GPP(General Purpose Processor) platform. It integrates the ideas of centralized processing, collaborative radio and real-time cloud infrastructure. CoMP (Coordinated Multiple Points) is a key technique in LTE-Advanced. But it is really a challenge to implement real-time CoMP transmission on GPP platform because many factors can affect the performance of soft CoMP base stations. In this paper, We implemented a real-time CoMP architecture on GPP platform by optimizing soft base stations design and data exchange categories. This architecture is fully compatible with TD-LTE R8/R9. The test results show that it can ensure real-time processing and provide stable downlink CoMP transmission for TD-LTE R8/R9 users.

This paper presents an analytical study on the outage performance of cognitive decode-and-forward (DF) two-way relaying networks with underlay spectrum sharing. The bidirectional protocol, i.e., two time-slot multiple access broadcast (MABC) and three time-slot time division broadcast (TDBC), are considered. We propose a definition of outage probability by jointly considering outage events in the whole transmission process and the exact outage probability expressions are given. For both the protocols, the dependence among the received signal- to-noise ratios (SNRs) in the broadcast phase is introduced due to the interference power constraint. The joint probability of the received SNRs is derived. Based on that, the closed-form solutions to outage probability for the two protocols are obtained. The theoretical analysis is verified by Monte-Carlo simulations. Results demonstrate that MABC can achieve better outage performance through optimal interference power distribution between the two secondary users.

In order to satisfy the demand of future Broadband Wireless Access (BWA) with high mobility, high data rate and low cost, Tsinghua University proposes a novel BWA system named BRadio with independent intellectual property rights. This paper describes the BRadio system from the aspects of physical layer technologies, Medium Access Control (MAC), networking methods, and services. The physical layer technologies focus on Time domain and Frequency domain United Orthogonal Frequency Division Multiple Access (TFU-OFDMA), Time domain and Frequency domain United Single Carrier Modulation time division multiple Access (TFU-SCMA), Interleave Division Multiple Access (IDMA), and Multiple Input Multiple Output (MIMO). In MAC layer we describe several new functionalities in BRadio system. The networking methods of BRadio are divided into networking independently and networking with the existing networks. BRadio can support various services, among which, the Personal Media Service (PMS) is presented in detail. Relying on the technical superiority of its high data rate, high mobility and low cost, as well as its flexible networking and comprehensive service platform, BRadio has become a potential technical solution for BWA.

As energy consumption reduction has been an important concern for the wireless industry, energy-efficient communications is of prime interest for future broadband networks. In this paper, we study the energy efficiency of relay-assisted cellular networks using tools from stochastic geometry. We first derive the coverage probability for the macro base station (MBS) to user (UE), the MBS to relay station (RS), and the RS to UE links, and then we model the power consumption of MBSs and RSs. Based on the analytical model and expressions, the energy efficiency of relay-assisted cellular networks is then evaluated and is shown to be strictly quasi-concave on the transmit power for the MBS to UE link or the RS to UE link. Numerical results also show that the energy efficiency first improves while it hits a ceiling as the MBS density increases.

Spatial Modulation is a newly developed concept for multi-antenna systems, which could greatly reduce the signal processing complexity and hardware implementation burden faced by these systems today. To improve the spectral efficiency of spatial modulation system, a novel precoding scheme for downlink multi-user system is proposed in this paper. The precoding scheme not only cancels the inter-user interference, but also preserves the information embedded both in constellation symbols as well as in transmit antenna indexes, decomposing the system into independent single-user spatial modulation systems. Simulation results show that with a high transmission rate, the proposed scheme offers a smaller bit error rate than multi-user Zero Forcing precoding in practical SNR region. With the same base station antenna number and single-antenna user number, it also outperforms Zero Forcing precoding in terms of achievable sum rate. Furthermore, systems using our scheme could achieve a multi-user gain over single-user spatial modulation system.

Massive Multiple-Input Multiple-Output (MIMO) is an emerging research field due to the increasing demand of spectral efficiency in wireless broadband systems, such as Long Term Evolution (LTE) which is proposed by 3GPP (Third Generation Partnership Project). Precoding is important to exploit the performance of massive MIMO, and codebook design is crucial due to the limited feedback channel. In this paper, we propose a novel double codebook design method based on a Kronecker-type approximation of the array correlation structure for the uniform rectangular array, which is preferable for the antenna deployment of massive MIMO systems. As a case study, we run simulations based on assumptions of the LTE-Advanced system which is extended to 32 dual-polarized antennas. The results verify that the proposed codebook design has remarkable performance gain compared to the traditional double codebook under the uniform rectangular array setup.

In this study, the authors mainly study the degrees of freedom (DoFs) of M × N user single input multiple output (SIMO) (or multiple input single output (MISO)) X channels, in which the channel coefficients are drawn from a continuous distribution and data‐sharing among users are unavailable. First, the authors establish an outer bound of DoFs for multiple‐in multiple‐out (MIMO) X channel, which is tighter than those in previous work. For SIMO X channel with R antennas at each user, the authors prove that the DoFs is [ MNR /( M + NR − R )], which is achieved by symbol extension and interference alignment. As the transmitters and receivers are interchangeable in the network considered, the authors obtain corresponding results for MISO X channel. Finally, the authors generalise our result to MIMO X channel with R antennas at each user, and show that its DoFs is [ MNR /( M + N − 1)].

In this paper we present some channel measurement results of distributed multiple-input multiple-output (D-MIMO) systems inside a MD-82 aircraft. Channel capacity and spatial correlation are analyzed from the measured channel matrix. Capacity results of 7 different systems (six D-MIMO systems and a SISO system) are discussed. It is found that D-MIMO systems yield significant capacity gains as compared to SISO system. Spatial correlations among transmitter (Tx) antennas distributed above the cabin aisle are statistically analyzed from the measured data. Result shows that, when receiver (Rx) antenna array is placed in the front or back of the cabin, some Tx antenna pairs are highly correlative (correlation coefficients are higher than 0.8). The effect of Tx antenna selection on capacity characteristic of in-cabin D-MIMO system is also studied. It is found that Tx antenna selection can further improve channel capacity and the optimum selected Tx antennas are usually the ones near to the Rx array.

This paper studies a spectrum sharing scenario between a cooperative relay network (CRN) and a nearby ad-hoc network. In particular, we consider a dynamic spectrum access and resource allocation problem of the CRN. Based on sensing and predicting the ad-hoc transmission behaviors, the ergodic traffic collision time between the CRN and ad-hoc network is minimized subject to an ergodic uplink throughput requirement for the CRN. We focus on real-time implementation of spectrum sharing policy under practical computation and signaling limitations. In our spectrum sharing policy, most computation tasks are accomplished off-line. Hence, little real-time calculation is required which fits the requirement of practical applications. Moreover, the signaling procedure and computation process are designed carefully to reduce the time delay between spectrum sensing and data transmission, which is crucial for enhancing the accuracy of traffic prediction and improving the performance of interference mitigation. The benefits of spectrum sensing and cooperative relay techniques are demonstrated by our numerical experiments.

In this paper, we study a cognitive coexistence strategy for heterogeneous networks. While a lot of previous works focused on spectrum underlay approaches for weak interference scenarios, a high power infrastructure (IS) transmitter creates a large dead zone for nearby ad-hoc (AH) links using the same spectrum. To address this problem, we propose to utilize a half-duplex decode-and-forward (DF) relay node to assist the IS transmitter. The transmission time and power of relay-assisted IS network is optimized to reduce its generated interference while still guaranteeing its quality-of-service (QoS) level. The resource allocation problem of relay-assisted IS network is formulated as a convex optimization problem, for which a tailored dual optimization method is proposed. Our numerical results show that our relay-assisted scheme can produce less interference and/or achieve a higher QoS level.

In this paper, the uplink of a multi-user distributed antenna system (DAS) with antenna elements deployed on a circle is investigated. We address the problem of calculating the sum-rate capacity with per-user power constraints. Based on system scale-up, we derive a good approximation of the sum-rate capacity by adopting random matrix theory. We also propose an iterative method to calculate the unknown parameters in the approximation. The approximation is illustrated to be quite accurate and the iterative method is verified to be quite efficient by Monte Carlo simulations.

In reality, oscillators experience spectral broadening due to internal noise process, which becomes a potentially serious problem in OFDM transmission schemes, especially when high order signal constellations are employed. The paper analyzes the influence of the transmitter phase noise to the OFDM system in the multi-path fading channel, and shows when the transmitter contains large phase noise, the immunity of OFDM system to multipath fading is destroyed and the frequency domain model of the influence of phase noise used by some algorithms is no longer suitable. Then a feedback method to reduce the transmitter phase noise is proposed. Simulation results show that after pre- correction of transmitter phase noise, the existing algorithms that have been proposed in the receiver end can be performed to mitigate the receiver phase noise more effectively in the scenario when PHN generated at the transmitter is large compared to PHN generated at the receiver end.

The performance of multiuser MIMO-OFDM downlink systems with block diagonalization (BD) depends on the accuracy of the channel state information (CSI) available at the transmitter (CSIT) to a great extent. In time-varying channels, the CSIT is always outdated due to an inherent time delay between the uplink channel estimation and the downlink data transmission in TDD systems. This will lead to drastic degradation in system capacity. In this paper, based on the analysis of the effect of the outdated CSIT on multiuser MIMO- OFDM downlink systems with BD method, two linear processing methods, BD with user selection at the transmitter and total MMSE decoder at the receiver (TBDUS-RTMMSE) and BD at the transmitter and partial MMSE decoder at the receiver (TBD-RPMMSE), are proposed to mitigate the effect. To fulfill them, a new pilot-sending architecture is introduced. Analysis and simulation results show that the two proposed methods can effectively reduce the impairment of the outdated CSIT and improve the system sum capacity in suitable time delay regions of the CSIT.

The performance of multiuser MIMO (MU-MIMO) downlink systems with preceding and decoding method by minimizing the total mean square error (TMSE) of all data streams of all users (T-MMSE) depends on the accuracy of the channel state information (CSI) available at the transmitter (CSIT). In this paper, a robust transmission scheme is proposed to suppress the interferences among data streams and users caused by imperfect CSIT. At the transmitter, the precoders are obtained through an iterative procedure by minimizing the expectation of the TMSE of all data streams of all users conditioned on the estimated CSI under a total transmit power constraint. At each user, a linear decoder is calculated based on the minimum mean square error (MMSE) criteria according to the realtime CSI. To fulfill it, a new pilot-sending architecture is introduced. Numerical results demonstrate that the proposed scheme is robust not only to the channel uncertainty at the transmitter but also to the channel uncertainty at each user, and can effectively improve the system performance.

Based on the analysis of the effect of the outdated channel state information (CSI) available at the transmitter (CSIT) on multiuser MIMO (MU-MIMO) downlink systems with block zero-forcing precoding (BZFP) method, two linear processing methods, BZFP at the transmitter with total MMSE decoding at the receiver (TBZF-RTMMSE) and BZFP at the transmitter with partial MMSE decoding at the receiver (TBZF- RPMMSE) are proposed. To fulfill them, a new pilot-sending and channel estimation architecture is introduced. Analysis and simulation results show that the two proposed methods can effectively reduce the impairment of the outdated CSIT and improve the system sum capacitv in the suitable time delav region of the CSIT.

We consider the optimal amount of training for single-antenna Rayleigh flat fading channels with peak-power- limited input. The receiver uses known training symbols to perform minimum mean square error (MMSE) channel estimation. With a block-fading channel model and a perfect interleaving assumption, we first analyze the characteristics of the optimum input distribution, then numerically compute the training-based capacity, and then find the optimal number of training symbols that maximizes the capacity.

In multiple-input multiple-output (MIMO) systems, iterative receiver with turbo processing between detection and decoding can achieve near-capacity performance. This paper introduces detection ordering into conventional soft interference cancellation minimum mean square error (SoIC-MMSE) detector which cancels the interference and detect signals in a 'parallel' manner. With soft information feedback, a novel expected error based (EEB) ordering algorithm is proposed and applied to quadrature amplitude modulation (QAM) signaling. Simulation results show that with a little complexity increase in one turbo iteration compared to the conventional SoIC-MMSE detection, our algorithm needs less iteration to achieve better performance.

In this paper, we aim to design a set of linear transmitter preceding vectors for the downlink of multiuser MIMO system, using the maximal signal to jamming and noise ratio (SJNR) criterion for each user, subject to a transmit power constraint. Unlike some conventional methods, the proposed scheme does not impose any restrictions on system configuration, i.e., the number of antennas at the transmitter and receivers. Furthermore, this new algorithm can mitigate the co-channel interference (CCI) and noise at the same time. Both analysis and simulation results have shown the superiority of the proposed method over conventional transmit precoders.

An adaptive turbo coded modulation orthogonal frequency division multiplexing (OFDM) schemes with power variable over subcarriers, variable over subbands, and constant over subcarriers is proposed. Our object is to improve the overall throughput under target bit-error-rate (BER) for OFDM transmissions. Simulation results indicate that our fixed power adaptation scheme exhibits a more than 7dB signal-noise-ratio (SNR) gain relative to nonadaptive turbo coded modulation, and about 2dB additional gain can be achieved with our variable power adaptation. We also discuss the effect of the amount of subbands on throughput performance in our adaptive scheme.

In this paper, two adaptive orthogonal frequency division multiplexing (OFDM) schemes employing rate-compatible turbo coded modulation with power variable subband-by-subband and symbol-by-symbol are proposed. Our object is to reduce the overall transmit power under target bit-error-rate (BER) for constant throughput OFDM transmissions. Simulation results indicate that our adaptive scheme with power variable over symbols exhibits a more than 5 dB signal-noise-ratio (SNR) gain in contrast to non-adaptive turbo coded modulation, and about 1-2 dB SNR gap from our scheme with power variable over subbands. We also discuss the effect of the amount of subbands on our adaptation performance.

Orthogonal frequency division multiplexing (OFDM) is one of the promising techniques in the next generation high-speed data rate wireless networks for its ability to combat the intersymbol interference(ISI), and fair scheduling algorithms are used to utilize the independence of the fading statistics of different user to improve the spectral efficiency. In this paper, we try to analysed the performance of fair scheduling algorithms in the OFDM systems and choose the appropriate subband size to tradeoff between the throughput and the channel feedback. We show that asymptotic scheduling gain of fair scheduling algorithms in the OFDM system increases as the number of user increases and decreases with the average received SNR. We also present the distribution of the averaged received signal-to-noise-ratio (SNR) in a subband, give a formula to evaluate the scheduling gain approximately, and point out that the maximal eigenvalue is a good indicator to choose the appropriate subband size. It is shown that the appropriate subband number is 16 for channel A or 32 for channel B.

PC based software radio systems can enjoy more flexibility comparing to software radio systems based on DSPs or FPGAs. But a single general-purpose computer nowadays does not have the ability of performing the entire real-time digital signal processing of a conventional wireless communications system. To solve this problem, we designed a parallel computing algorithm to make a group of PCs work cooperatively to accomplish the signal-processing task. Conventional computers lack an I/O system capable of delivering constant data stream to the front-end. Techniques to solve this problem are also discussed in this paper.