This study introduces an analytical model for a nonsaturated IEEE 802.11ax network, designed to capture the coexistence characteristics of uplink orthogonal frequency division multiple access (OFDMA)-based random access (UORA) and non-random access (UONRA) mechanisms under imperfect channels. Existing models fail to assess this realistic network due to three issues: 1. When accounting for frame aggregation, imperfect channels, and queue buffer, the network exhibits an overwhelming number of states, which renders the evaluation of its performance infeasible. 2. Existing bulk-service queue models fail to evaluate these queue characteristics influenced by imperfect channels. 3. The coexistence characteristics of the two mechanisms remain unexplored due to their complex interactions. To address Issue 1, we propose two designs: device updates and hardware implementation, which reduce the massive states and facilitate the network evaluation. To address Issue 2, we have developed a feedback-driven bulk-service queue model that captures the joint effects of imperfect channels and queue characteristics. To address Issue 3, we comprehensively analyzed the joint impact of the two mechanisms from a probabilistic perspective. Extensive simulations demonstrate that the proposed model accurately captures network performance (throughput, delay, and collision probability) and reduces the mean estimation error by a factor of 30 compared to existing studies.

Interference is the major bottleneck for the performance of the two-way K-user interference channel. In this paper, we demonstrate that by employing a reconfigurable intelligent surface (RIS), we can indeed achieve the interference-free degree of freedom (DoF). Mathematically, achieving interference-free DoF amounts to finding the solutions of a linear system with the modulus-1 constraint. To address this challenging problem, we take a geometric perspective and leverage the tools of high-dimensional probability. As a result, we are able to provide the nearly exact characterizations of both the maximal and the minimal number of RIS elements required for the interference-free DoF. Simulation results match our theoretical prediction quite well.

Analog self-interference cancellation (A-SIC) plays a crucial role in the implementation of in-band full-duplex (IBFD) radios, due to the fact that the inherent transmit (Tx) noise can only be addressed in the analog domain. It is thus natural to ask what the performance limit of A-SIC is in practical systems, which is still quite underexplored so far. In this paper, we aim to close this gap by characterizing the fundamental performance of A-SIC which employs the common multi-tap delay (MTD) architecture, by accounting for the following practical issues: 1) Nonstationarity of the Tx signal; 2) Nonlinear distortions on the Tx signal; 3) Multipath channel corresponding to the self-interference (SI); 4) Maximum amplitude constraint on the MTD tap weights. Our findings include: 1) The average approximation error for the cyclostationary Tx signals isequal tothat for thestationarywhiteGaussianprocess, thus greatly simplifying the performance analysis and the optimization procedure. 2) The approximation error for the multipath SI channel can bedecomposedas the sum of the approximation error for the single-path scenario. By leveraging these structural results, the optimization framework and algorithms which characterize the fundamental limit of A-SIC, by taking into account all the aforementioned practical factors, are provided.

This study explores the performance optimization of uplink orthogonal frequency division multiple access (OFDMA)-based random access (UORA) in IEEE 802.11ax networks. UORA supports multi-user transmission via two methods, where users transmit either fixed-size or variable-size aggregated MAC protocol data units. However, three critical issues arise. 1 Existing studies only focus on the fixed-size method with low practicality, and overlook the impact of traffic load which leads to inaccurate evaluation of the network performance. 2 The variable-size method has never been studied due to a complex scenario, where user frames append padding bits to fulfill the transmission opportunity constraint. 3 In realistic networks, the variable-size method sacrifices throughput to achieve high practicality and low latency. To address the first two issues, we proposed two novel models based on queueing theory that accurately capture the impact of these transmission methods and various parameters (e.g., the traffic load and padding bits) on throughput, packet loss rate, and latency. To address Issue 3, we design aDynamicSelectionAlgorithm ofTransmissionMethods (DSATM), which dynamically switches between the two transmission methods to enhance practicality, maximize throughput, and minimize latency. Finally, we conducted extensive simulations to verify the accuracy of our models and DSATM.

Network pruning is a commonly used measure to alleviate the storage and computational burden of deep neural networks. However, the fundamental limit of network pruning is still lacking. To close the gap, in this work we'll take a first-principles approach, i.e. we'll directly impose the sparsity constraint on the loss function and leverage the framework of statistical dimension in convex geometry, thus enabling us to characterize the sharp phase transition point, which can be regarded as the fundamental limit of the pruning ratio. Through this limit, we're able to identify two key factors that determine the pruning ratio limit, namely, weight magnitude and network sharpness. Generally speaking, the flatter the loss landscape or the smaller the weight magnitude, the smaller pruning ratio. Moreover, we provide efficient countermeasures to address the challenges in the computation of the pruning limit, which mainly involves the accurate spectrum estimation of a large-scale and non-positive Hessian matrix. Moreover, through the lens of the pruning ratio threshold, we can also provide rigorous interpretations on several heuristics in existing pruning algorithms. Extensive experiments are performed which demonstrate that our theoretical pruning ratio threshold coincides very well with the experiments. All codes are available at: https://github.com/QiaozheZhang/Global-One-shot-Pruning

Reconfigurable intelligent surface (RIS) is a promising technology for future wireless communications due to its capability of optimizing the propagation environments. Nevertheless, in literature, there are few prototypes serving multiple users. In this paper, we propose a whole flow of channel estimation and beamforming design for RIS, and set up an RIS-aided multi-user system for experimental validations. Specifically, we combine a channel sparsification step with generalized approximate message passing (GAMP) algorithm, and propose to generate the measurement matrix as Rademacher distribution to obtain the channel state information (CSI). To generate the reflection coefficients with the aim of maximizing the spectral efficiency, we propose a quadratic transform-based low-rank multi-user beamforming (QTLM) algorithm. Our proposed algorithms exploit the sparsity and low-rank properties of the channel, which has the advantages of light calculation and fast convergence. Based on the universal software radio peripheral devices, we built a complete testbed working at 5.8 GHz and implemented all the proposed algorithms to verify the possibility of RIS assisting multi-user systems. Experimental results show that the system has obtained an average spectral efficiency increase of 13.48 bps/Hz, with respective received power gains of 26.6 dB and 17.5 dB for two users, compared with the case when RIS is powered-off.

Channel aging poses a huge challenge for the MU-MIMO (Multi-user Multiple-Input- Multiple Output) communications. To alleviate the problem, channel prediction is widely regarded as a promising mean. Existing channel prediction methods usually rely heavily on the assumption of parametric models. Severe performance degradation might occur when modeling error exists, which is unfortunately common in practice due to the complicated nature of wireless channels. To address the problem, we propose to use nonparametric regression methods for prediction. Nonparametric methods enjoy the advantage of not relying on the model assumption at all, enabling it very suit for making inference from complex channel data. In this paper, we present two nonparametric regression methods for MU-MIMO’s channel prediction, i.e., k-nearest neighbors (kNN) regression and its improved version, local polynomial regression (LPR). In addition, we propose to employ the Bayesian Information Criteria (BIC) to select the parameters in LPR, whereby we get the conclusion that local linear regression is recommended in practical applications. Furthermore, for the case of predicting the right singular matrix of the channel in specific, we provide a useful preprocessing procedure. Simulation results illustrate that our proposed nonparametric regression methods can outperform significantly the conventional parametric methods for channel prediction.

Abstract An intelligent‐reflecting‐surface aided multigroup multicast communication system is investigated in this paper. We develop energy‐efficient designs for both the precoding matrix at the base station and passive reflecting beamforming at intelligent‐reflecting‐surface, subject to the minimum signal‐to‐interference‐plus‐noise ratio constraint of each user and the unit‐modulus constraint imposed by passive intelligent‐reflecting‐surface. This leads to a complex non‐convex problem for which to tackle we propose two novel algorithms based on alternating optimization techniques. Specifically, one algorithm employs semi‐definite relaxation to obtain the suboptimal reflection coefficient vector and suboptimal precoding vector for each group. In order to reduce the computational complexity, the second algorithm transforms this non‐convex problem into two second‐order cone programming problems. Numerical results show that intelligent‐reflecting‐surface can significantly reduce the power consumption of the base station.

Interferences due to non-orthogonality of signals usually exist in wireless networks when the number of users is larger than the sequence length, such as non-orthogonality of the pilots in multi-cell systems and non-orthogonality of the signature sequences in overloaded code-division-multiple-access (CDMA) systems. We address this effect from the perspective of non-orthogonal sequence design in a two-cell multiple-antenna network. Specifically, we aim at designing pilot sequences to minimize the sum mean-squared-error (MSE) of channel estimation with a given sequence length$\tau $where$\tau \in [K,2K]$and$K$is the number of users per cell. Considering the strength disparity between channels originating from the home cell and the neighbor cell, this problem boils down to minimizing the sum of squares of weighted correlations among sequences, whose lower bound is obtained inclosed formand can be regarded as a generalization of the well-known Welch bound (Welch, 1974). We prove this extended Welch bound is achievable, and design an algorithm based on the Davies-Higham method to generate the interference-minimizing sequences. Three fundamental properties of the proposed sequences are presented. Finally, we derive closed-form expressions of the average signal-to-interference-plus-noise-ratio (SINR) and rate for data transmission, based on which the optimal training duration can be found.

Massive MIMO is a key technology for 5th generation (5G) mobile communications. The large excess of base station (BS) antennas brings unprecedented spectral efficiency. However, during the initial phase of industrial testing, a practical challenge arises which undermines the actual deployment of massive MIMO and is related to mobility. In fact, testing teams reported that in moderate-mobility scenarios, e.g., 30 km/h of UE speed, the performance may drop 50% compared to the low-mobility scenario, a problem not foreseen by theoretical papers on the subject. In order to deal with this challenge, we propose a Prony-based angular-delay domain (PAD) prediction method, which is built on exploiting the angle-delay-Doppler structure of the multipath. Our theoretical analysis shows that when the number of base station antennas and the bandwidth are large, the prediction error of our PAD algorithm converges to zero for any UE velocity level, provided that only two accurate enough previous channel samples are available. Simulation results show that under the realistic channel model of 3GPP in rich scattering environment, our proposed method even approaches the performance of stationary scenarios where the channels do not vary at all.

Device-to-device (D2D) communication is a key enabling technology to facilely realizing the Internet of Things (IoT) due to its spectral and energy efficiencies features. Exploiting the physical-layer network coding (PNC) and energy harvesting (EH) technology, two-way relaying (TWR) D2D communication can achieve significant performance for IoT in terms of data rate and energy efficiency (EE). In this article, we investigate the EH-aided TWR D2D communication sharing the uplink (UL) spectrum of the traditional cellular networks. We assume that the D2D transmitters, receivers, and participating relays can collect renewable energy (RE) from natural resources. Also, the relays are considered to be powered by radio-frequency (RF) signals utilizing the power splitting (PS) protocol. Subject to the Quality of Service (QoS), power, subchannel assignment, EH, and maximum practical power constraints, two nonconvex mixed-integer nonlinear programming (MINLP) problems are formulated. The two problems provide a tradeoff on either maximizing the TWR D2D link (TDL) rate or its EE depending on the IoT application needs. Based on the particle swarm optimization (PSO) algorithm, we propose the rate and EE tradeoff EH-based algorithm (REET-EH) to deal with these problems. The proposed algorithm can optimally perform the resource allocation (RA), PS factors determination, power allocation (PA), and relay selection processes. The numerical results investigate the performance of the REET-EH algorithm and show its consistency over several parameters. Also, the results illustrate that our proposed algorithm improves the system performance compared with other state-of-the-art algorithms with regard to the D2D link rate and EE.

Massive MIMO is widely touted as an enabling technology for 5th generation (5G) mobile communications and beyond. On paper, the large excess of base station (BS) antennas promises unprecedented spectral efficiency gains. Unfortunately, during the initial phase of industrial testing, a practical challenge arose which threatens to undermine the actual deployment of massive MIMO: user mobility-induced channel Doppler. In fact, testing teams reported that in moderate-mobility scenarios, e.g., 30 km/h of user equipment (UE) speed, the performance drops up to 50% compared to the low-mobility scenario, a problem rooted in the acute sensitivity of massive MIMO to this channel Doppler, and not foreseen by many theoretical papers on the subject. In order to deal with this “curse of mobility”, we propose a novel form of channel prediction method, named Prony-based angular-delay domain (PAD) prediction, which is built on exploiting the specific angle-delay-Doppler structure of the multipath. In particular, our method relies on the high angular-delay resolution which arises in the context of 5G. Our theoretical analysis shows that when the number of base station antennas and the bandwidth are large, the prediction error of our PAD algorithm converges to zero for any UE velocity level, provided that only two accurate enough previous channel samples are available. Moreover, when the channel samples are inaccurate, we propose to combine the PAD algorithm with a denoising method for channel estimation phase based on the subspace structure and the long-term statistics of the channel observations. Simulation results show that under a realistic channel model of 3GPP in rich scattering environment, our proposed method is able to overcome this challenge and even approaches the performance of stationary scenarios where the channels do not vary at all.

This paper studies the non-orthogonal multiple access (NOMA)-based spatial modulation (SM) systems with multiple subchannels. A mixed multicast and unicast transmission is considered in each channel, in which a common content is multicasted in the transmit antenna (TA) domain to all the users, and the unicast contents are transmitted as amplitude- phase modulated (APM) symbols in the classical signal domain using NOMA via the active antenna. First, we obtain the achievable unicast rate for each user and an upper bound for the achievable multicast rate in the TA domain. Then, the subchannel assignment and power allocation schemes are designed to maximize the system sum rate. Specifically, the subchannel assignment is formulated as a many-to-one matching with peer effect, and we propose a suboptimal but efficient algorithm incorporating the swap operation to solve it. We then optimize the power allocation subproblem by employing the successive convex approximation approach, which iteratively approximates the original nonconvex problem to a convex one. Finally, numerical results are presented to demonstrate the effectiveness of the proposed schemes.

This paper studies the multicast precoding problem in frame-based multigateway multibeam satellite communications with feeder link interference. We formulate a sum rate maximization problem that incorporates the minimum signal-to-interference-and-noise-ratio (SINR) requirement for each user, the sum power constraint at each gateway as well as the per feed power constraints at the satellite. We propose two algorithms to solve the formulated problem. In the first algorithm, by employing the successive convex approximation (SCA) approach, we iteratively approximate the original nonconvex problem to a second-order cone program (SOCP) which can be solved by modern solvers efficiently. For the second, we propose a modified joint power control and beamforming algorithm which computes QoS beamforming and geometric programming (GP) based power allocation iteratively. Compared to the traditional method [5] which uses a subgradient and projection based approach for the power control, the GP based solution is more implementation friendly and practical appealing which also achieves a slightly better sum rate performance. Finally, numerical results are presented to validate the efficiency of the proposed schemes.

The traditional fully‐digital beamforming is realised by the full radio‐frequency (RF) chain configuration, which will be impracticable in massive multiple‐input multiple‐output (MIMO) systems because of the overburden energy consumption of RF chains at millimetre wave frequencies. To address this issue, a series of hybrid beamforming schemes have been proposed to reduce the number of RF chains. Although the hybrid beamforming schemes for single‐user MIMO (SU‐MIMO) have been studied extensively, the performance of hybrid beamforming for multi‐user MIMO (MU‐MIMO), especially for partially‐connected hybrid architecture, still has room for improvement. In this study, the authors propose a hybrid beamforming scheme for MU‐MIMO systems. Specifically, they focus on the design of analogue beamforming and optimise the analogue RF precoders and combiners jointly. The simulation results show that the proposed hybrid beamforming in fully‐connected structure can achieve a better performance compared with the existing hybrid beamforming schemes in both SU‐MIMO and MU‐MIMO systems. It is also observed that the proposed hybrid beamforming can achieve significant performance advantages compared with the existing hybrid beamforming schemes in partially‐connected structures.

This paper studies the multigroup multicast precoding problem in frame-based multigateway multibeam satellite communications with feeder link interference. We formulate a sum rate maximization problem that incorporates the minimum signal-to-interference-and-noise-ratio requirement for each user, the sum power constraint at each gateway, as well as the per feed power constraints at the satellite. Both transparent payload and payload with on-board processing are considered. In the former case, we propose a centralized algorithm by employing the successive convex approximation (SCA) approach, which iteratively approximates the original nonconvex problem to a second-order cone program. Moreover, in order, for each gateway, to compute its precoding vector locally with local channel state information, we devise a decentralized algorithm by incorporating consensus alternating direction method of multipliers (ADMM) into the SCA framework. For the latter case, we devise a two-stage precoding scheme where, in the first stage, a leakage-based minimum mean-square-error scheme is employed to control the feeder link interference efficiently. In the subsequent second stage, we use the SCA-ADMM approach to deal with the user link interference while maximizing the sum rate. Finally, numerical results are presented to demonstrate the performance of the proposed schemes.

Interferences due to non-orthogonality of training sequences usually exist in cellular networks when the number of all users is relatively large compared to the coherence time, such as the case in massive MIMO systems. In this paper, we address this effect from the perspective of non-orthogonal training sequence design in two-cell interference networks with K users per cell. We relax the general assumption in which the cross-correlations of sequences are restricted to be 0 or 1, and target at designing the training sequences to minimize training phase interference with a given pilot length τ, which is no larger than the total number of users, i.e., τ ∈ [K, 2K]. We note that when large scale fading between different cells β ≠ 1, the strengths of interferences arising from non-orthogonal training sequences within a cell or from the adjacent cell become asymmetric, and optimal design needs to treat the intra-cell sequence correlation and inter-cell correlation differently. To this end, by incorporating β into the design, we extend the Welch bound (Welch 1974 [1]) to the two-cell scenario with asymmetric intra-cell and inter-cell interference, and characterize the lower bound of the interference precisely. Specifically, we obtain the result that the sum of the squares of β -weighted cross-correlations of the training sequences is lower-bounded by [(2K2(1+β2))/(K+(τ-K)β2)], which can be achieved by the proposed training sequence design in closed-form. Particularly, when β = 1, this bound reduces to [((2K)2)/(τ)] which is exactly the Welch bound. This result is applicable for the uplink design of general interference networks such as the pilot design in massive MIMO and the signature sequence design in multicell CDMA systems.

The capacity region of the symmetric two‐pair one‐way relay channel with overhearing links (OLs) is studied. First, the problem is explored in the linear shift deterministic channel model and the capacity region of the deterministic channel model is characterised. The upper bound is proven tightly by authors' designed transmit strategy. With the gains obtained from the deterministic network, the transmit strategy for this relay channel in Gaussian setup is proposed. According to the strength of OLs, this strategy applies the corresponding superposition of nested lattice codes and random Gaussian codes at the source nodes and successive interference cancellation at all receive nodes. The achievable rate of this scheme is analysed according to the quality of these OLs. It is shown that this scheme achieves the single user rate to within 1.15 bits/s/Hz of the upper bound for all channel gains.

In this paper we propose a novel evolutionary detection algorithm for large-scale multiple-input multiple-output (MIMO) systems, utilizing the concepts of quantum bit and quantum rotation gate in quantum computing. Specifically, we consider the detection of BPSK and 4-QAM signals, and the uncertainty on the information bits at the receiver is modeled as a sequence of quantum bits, which is referred to as a quantum particle. The proposed algorithm begins with a population of such particles, each initialized randomly. Then by the aid of quantum rotation gate along with a fitness function, a simple mechanism is proposed to allow all quantum particles to evolve in a guided manner towards a potentially optimal area and finally converges. It is shown by simulations that the proposed algorithm can achieve near-optimal performance.

In this paper, we aim to maximize the total energy efficiency for a multi-cell MIMO broadcast channel with dirty paper coding, where both the base stations and users employ multiple antennas. The EE metric is defined as the ratio of the total sum- rate to the total power consumption. Because the original problem is non-convex and difficult to tackle directly, we employ the fractional programming and iterative linear approximation methods to transform it into a set of sub-problems. After using Lagrange dual decomposition, each sub- problem essentially becomes a precoding design problem in MIMO broadcast channel (BC) and is still non-convex, which is then dealt with via BC- multiple access channel (MAC) duality property. Specifically, we propose a gradient descent (GD) method to compute the uplink precoder in each MAC problem which has low complexity. Thus, the dual BC problem can be solved and each BS can iteratively update its precoding matrices with small amount of information exchange among the base stations. Numerical results validate the better performance of our proposed algorithm over conventional linear precoding method.

In a densely deployed small‐cell network, part of small‐cell base stations (BSs) are activated to serve users while the rest are sleep to save energy. The uplink performance region is studied here for such network employing orthogonal frequency‐division multiple access. The authors characterise the limit of performance region using the Pareto boundary. This boundary describes all achievable performance results of beamformers and power allocation. Enabling BS allocation increases the spatial degree of freedom by cooperative reception but greatly complicates searching boundary of performance region. To overcome the complexity challenge, a fast and customised iterative algorithm is developed to check if an arbitrary given performance target is feasible. Using the proposed feasibility checking, the authors can characterise the Pareto‐optimal boundary of uplink performance with activated BS allocation. The authors further study two types of BS allocation schemes, namely the network‐centric and user‐centric strategies. Comparison of both strategies is illustrated in the authors’ numerical examples.

In this study, the authors propose a low complexity detector for the generalised space shift keying (GSSK) in large‐scale multiple‐input–multiple‐output systems. To be concrete, they propose a sparse K ‐best (SK) detector based on the breadth‐first category of sphere detector (referred to K ‐best sphere decoding). The author's detector is inspired by the fact that the GSSK signal is naturally a sparse zero‐one vector since only a few antennas are activated at the transmitter. Different with the conventional K ‐best detector searching all the transmit antennas, their proposed SK detector investigates only a few promising candidates which are activated antennas at the transmitter. Overall, their proposed SK detector exploits not only the sparsity of the GSSK signal but also the constraint on its non‐zero values. Therefore the restricted isometry property‐based performance analysis shows that is effective in detecting the GSSK signal. Moreover, the empirical results show that their detector performs much better than the sparse algorithms‐based normalised compressive sensing (NCS) detectors while exhibits only slightly higher complexity than the latter (the low‐complexity orthogonal matching pursuit‐based NCS detector).

Equispaced pilot arrangement is the most popular scheme for pilot‐aided transmission in orthogonal frequency division multiplexing systems. In this study, the authors argue that a non‐equispaced pilot pattern may outperform its equispaced counterpart, if they fully take into account the sparsity of the channel impulse response which is inherent in wireless channels. More specifically, a sparsity‐aware pilot arrangement scheme based on the coherence criterion is investigated in this study. To address the resulting non‐deterministic polynomial (NP)‐hard combinatorial optimisation problem, they propose an efficient local search algorithm. For channel estimation, they convert it to a sparse recovery problem. To enhance the applicability of the authors scheme, that is, when there is no prior knowledge about the channel order, they propose to employ the Bayesian information criterion to estimate the channel order first and then recover the sparse channel vector via existing low‐complexity methods, for example, orthogonal matching pursuit. By combining the above pilot arrangement scheme with channel estimation, their scheme exhibits substantially better performance in comparison with the conventional equispaced schemes with linear (or spline) interpolation, in terms of total number of pilot symbols and bit error rate.

A subspace method for channel estimation has been recently proposed for tackling the pilot contamination effect, which is regarded by some researchers as a bottleneck in massive MIMO systems. It was shown in the literature that if the power ratio between the desired signal and interference is kept above a certain value, the received signal spectrum splits into signal and interference eigenvalues, namely, the “pilot contamination” effect can be completely eliminated. However, in the literature an independently distributed (i.d.) channel is assumed, which is actually not much the case in practice. Considering this, a more sensible finite-dimensional physical channel model (i.e., a finite scattering environment, where signals impinge on the base station (BS) from a finite number of angles of arrival (AoA)) is employed in this paper. Via asymptotic spectral analysis, it is demonstrated that, compared with the i.d. channel, the physical channel imposes a penalty in the form of an increased power ratio between the useful signal and the interference. Furthermore, we demonstrate an interesting “antenna saturation” effect, i.e., when the number of the BS antennas approaches infinity, the performance under the physical channel withPAoAs is limited by and nearly the same as the performance under the i.d. channel withPreceive antennas.

Pilot contamination is known to severely limit the performance of large-scale antenna (“massive MIMO”) systems due to degraded channel estimation. This paper proposes a twofold approach to this problem. First we show analytically that pilot contamination can be made to vanish asymptotically in the number of antennas for a certain class of channel fading statistics. The key lies in setting a suitable condition on the second order statistics for desired and interference signals. Second we show how a coordinated user-to-pilot assignment method can be devised to help fulfill this condition in practical networks. Large gains are illustrated in our simulations for even small antenna array sizes.

The two-user multiple-input multiple-output (MIMO) Gaussian Broadcast Channel (BC) with mixed CSIT (i.e., delayed CSIT plus estimated current CSIT) under general antenna configuration is considered in this paper. Unlike the MISO scenario, obtaining tight characterization of the Degrees of Freedom (DoF) region seems difficult for the general MIMO BC settings. Despite that, the inner bound we provide in this paper is believed to be the best achievable bound so far. One novel ingredient of our scheme is the rate-splitting of the interference-encoded symbols, which provides the possible benefit of DoF gain by accommodating the transmission to the asymmetric receivers. Moreover, the outer bound of DoF region is also presented, which coincides our proposed inner bound in some cases.

This paper addresses the problem of channel estimation in multi-cell interference-limited cellular networks. We consider systems employing multiple antennas and are interested in both the finite and large-scale antenna number regimes (so-called "massive MIMO"). Such systems deal with the multi-cell interference by way of per-cell beamforming applied at each base station. Channel estimation in such networks, which is known to be hampered by the pilot contamination effect, constitutes a major bottleneck for overall performance. We present a novel approach which tackles this problem by enabling a low-rate coordination between cells during the channel estimation phase itself. The coordination makes use of the additional second-order statistical information about the user channels, which are shown to offer a powerful way of discriminating across interfering users with even strongly correlated pilot sequences. Importantly, we demonstrate analytically that in the large-number-of-antennas regime, the pilot contamination effect is made to vanish completely under certain conditions on the channel covariance. Gains over the conventional channel estimation framework are confirmed by our simulations for even small antenna array sizes.

The graphical models have been proven to be a very powerful and potential framework for addressing the inference problems. In this paper, we propose a new graphical model based algorithm for the detection of MIMO systems. The main feature of the algorithm lies in that it is implemented as a MRF-like graph, when combined with the Gaussian approximation and vector-based inference, our algorithm can lead to very promising performance, especially when the constellation size is small, with just linear complexity per symbol and memory requirement increases linearly with the number of transmit antennas. Simulation results collaborate with the analytical results, hence verifying the appeal of the algorithm for practical applications.

In this paper, we consider the problem of the capacity limits of the cellular network, which is modeled as a network consisting of two mutually interfering multiple access channel with multiple antennas at each receiver (e.g. the Base Station). By developing a tight outerbound (in the sense of DoF) and an achievable scheme which exploits the idea of interference alignment, we are able to exactly characterize the degrees of freedom (DoF) of the network when the channel coefficients are time- or frequency-varying, which is, the DoF per cell is $\frac{{KM}}{{K + \min (M,K)}}$ (where $M$ and $K$ denotes the number of receiver antennas and number of users in a cell, respectively). From the DoF result, it can be observed that when the number of users is large, we can nearly achieve the interference-free DoF of a multi-antenna cell!

Abstract In this paper, we analyze the impacts of different frame types on the self‐similarity and burstiness characteristics of the aggregated frame traffic in a real 802.11 wireless local area network (WLAN). We find that the impacts of different frame types are related to the mean frame sizes and the proportions of specified frame types in the aggregated frame traffic. Furthermore, we propose an analytical model to capture the relationship of self‐similarity characteristics between the aggregated frame traffic and different frame types. These new results provide an insight of frame traffic characteristics and some practical guidelines for developing new efficient algorithms to improve the common medium utilization and system throughput performance. Copyright © 2009 John Wiley & Sons, Ltd.

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

In this paper, we propose an amicable-orthogonal-design-based differential space-time block code (ADSTBC) for multiple antennas. Compared with the existing differential modulation designs, our scheme imposes no restrictions on underlying signal constellation, and therefore can improve the spectral efficiency by exploiting efficient modulation techniques such as QAM, APSK, etc. We then derive a non-coherent maximum-likelihood decoder (MLD) with linear complexity for flat Rayleigh fading channels. Particularly, when the QAM constellation is used in ADSTBC, MLD can be further simplified to independently detect the real and imaginary parts of each modulated data symbol, and accordingly the implement cost can be greatly reduced; moreover, the computational amount for detecting single modulated data symbol is kept constant in MLD while the order of the QAM constellation increases.