Integrated super-resolution sensing and communication (IS2AC) in symbiotic radio (SR) systems enables highly efficient simultaneous localization and information transmission for both primary and secondary user devices. However, accurate estimation of the two-dimensional (2-D) directions of the backscatter devices (BDs) poses a great challenge when they are densely located. To address this issue, in this paper, we propose a sparse multiple-input multiple-output (MIMO)-based IS2AC SR system with L-shaped nested array (NA) deployed at the base station (BS) to achieve both super-resolution 2-D sensing for multiple secondary BDs and efficient communication for all users. To achieve efficient channel estimation, the elevation and azimuth angles of the primary and secondary users in SR systems are firstly estimated and paired. After that, by matching the paired angles to obtain beamforming gain, the channel gains of both direct and backscatter links are estimated. To evaluate the performance of channel estimation with L-shaped NA, the sum mean square error (MSE) of all channels are analyzed. Simulation results are provided to demonstrate that sparse MIMO can provide significant performance gain than conventional compact MIMO.

In this paper, we prove the convergence of the simplified information geometry approach (SIGA), which was proposed for massive MIMO-OFDM channel estimation. For a general Bayesian inference problem, we first show that the iteration of the common second-order natural parameter (SONP) is separated from that of the common first-order natural parameter (FONP). Hence, the convergence of the common SONP can be checked independently. We show that with the initialization satisfying a specific but large range, the common SONP is convergent regardless of the value of the damping factor. For the common FONP, we establish a sufficient condition of its convergence and prove that the convergence of the common FONP relies on the spectral radius of a particular matrix related to the damping factor. We give the range of the damping factor that guarantees the convergence in the worst case. Further, we determine the range of the damping factor for massive MIMO-OFDM channel estimation by using the specific properties of the measurement matrices. Simulation results are provided to confirm the theoretical results.

Multiple-antenna technologies are advancing toward the development of extremely large aperture arrays and the utilization of extremely high frequencies, driving the progress of next-generation multiple access (NGMA). This evolution is accompanied by the emergence of near-field communications (NFCs), characterized by spherical-wave propagation, which introduces additional range dimensions to the channel and enhances system throughput. In this context, a tutorial-based primer on NFC is presented, emphasizing its applications in multiuser communications and multiple access (MA). The following areas are investigated: 1) the commonly used near-field channel models are reviewed along with their simplifications under various near-field conditions; 2) building upon these models, the information-theoretic capacity limits of NFC-MA are analyzed, including the derivation of the sum-rate capacity and capacity region, and their upper limits for both downlink and uplink scenarios; and 3) a detailed investigation of near-field multiuser beamforming design is presented, offering low-complexity and effective NFC-MA design methodologies in both the spatial and wavenumber (angular) domains. Throughout these investigations, near-field MA is compared with its far-field counterpart to highlight its superiority and flexibility in terms of interference management, thereby laying the groundwork for achieving NGMA.

In this paper, we investigate the beam domain statistical channel state information (CSI) estimation for the two-dimensional (2D) beam-based statistical channel model (BSCM) in massive multi-input multi-output (MIMO) systems. The problem is to estimate the beam domain channel power matrices (BDCPMs) based on multiple received pilot signals. A received signal model showing the relation between the statistical properties of the received pilot signals and the BDCPMs is derived. On the basis of the received signal model, we formulate an optimization problem with the Kullback-Leibler (KL) divergence. By solving the optimization problem, a novel method to estimate the statistical CSI without the estimates of instantaneous CSI is proposed. We further reduce the complexity of the proposed method by utilizing the circulant structures of particular matrices in the algorithm. We also showed the generality of the proposed method by introducing another application,i.e., estimation of the angle domain channel power matrix. Simulation results show that the proposed method has good convergence and can obtain sparse BDCPMs. Compared with the regularized multiple measurement vector focal underdetermined system solver (RM-FOCUSS) algorithm, the proposed algorithm obtains overall more accurate statistical CSI with much lower complexity and brings significant performance gains when used in channel estimation.

Integrated sensing and communication (ISAC) is believed to play a vital role for connected intelligence in 6G. Radio waves can be used to sense surrounding and obtain the environment information. Furthermore, the environmental knowledge provided by sensing improves the accuracy of channel estimation, resulting in better communication throughput. This paper provides a study of sensing assisted environment reconstruction and communication in ISAC scenario. We propose a multi transmission reception points (TRP) sensing architecture based on scatter polygon assumption to improve environment sensing accuracy. Then a polygon trace scheme is introduced to reconstruct communication channel hinging on sensing reconstructed environment. Cm-level sensing accuracy and ns-level communication channel reconstruction can be achieved by 140 GHz indoor measurement campaign validation.

In this paper, we investigate the channel estimation for massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. We revisit the information geometry approach (IGA) for massive MIMO-OFDM channel estimation. By using the constant magnitude property of the entries of the measurement matrix and the asymptotic analysis, we find that the second-order natural parameters (SONPs) of the distributions on all the auxiliary manifolds (AMs) are equivalent to each other at each iteration of IGA, and the first-order natural parameters (FONPs) of the distributions on all the AMs are asymptotically equivalent to each other at the fixed point. Motivated by these results, we simplify the iterative process of IGA and propose a simplified IGA for massive MIMO-OFDM channel estimation. It is proved that at the fixed point, the a posteriori mean obtained by the simplified IGA is asymptotically optimal. The simplified IGA allows efficient implementation with fast Fourier transformation (FFT). Simulations confirm that the simplified IGA can achieve near the optimal performance with low complexity in a limited number of iterations.

In massive grant-free transmission, joint user activity detection (UAD) and channel estimation (CE) is essential for data recovery at the receiver, which has been extensively researched in frequency-flat fading scenarios. However, in practical orthogonal frequency division multiplexing (OFDM)-based systems, frequency-selective fading leads to a significant increase in the number of channel coefficients to be estimated, imposing new challenges for the design of joint UAD and CE algorithms. Therefore, this paper investigates joint UAD and CE for OFDM-based massive grant-free transmission over frequency-selective fading channels. Firstly, the discrete cosine transform (DCT) is employed to reformulate the compressed sensing (CS) problem with the reduced dimension of the DCT domain channel response vector. Then, we develop a hybrid message passing (HMP) algorithm under the framework of the constrained Bethe free energy (BFE) minimization to achieve efficient joint UAD and CE. Numerical results confirm the superior joint estimation performance of the proposed algorithm over frequency-selective fading channels.

In massive grant-free transmission, joint user activity detection (UAD) and channel estimation (CE) is essential for data recovery at the receiver, which has been extensively researched in frequency-flat fading scenarios. However, in practical orthogonal frequency division multiplexing (OFDM)-based systems, frequency-selective fading (FSF) leads to a significant increase in the number of channel coefficients to be estimated, imposing new challenges for the design of joint UAD and CE algorithms. Therefore, this paper investigates joint UAD and CE for OFDM-based massive grant-free transmission over FSF channels. Firstly, by employing the discrete cosine transform (DCT), the joint estimation problem is formulated as the compressed sensing (CS) problem with the reduced dimension of the DCT-domain channel response vector. Then, based on the low-dimension sparse channel model, we develop a hybrid message passing (HMP) algorithm under the constrained Bethe free energy (BFE) minimization framework to achieve efficient joint UAD and CE. To deal with the lack of the DCT-domain prior information in practical scenarios, we parameterize it as the Cauchy distribution or the Laplacian distribution and learn their parameters by the proposed HMP algorithm. Numerical results confirm the superior joint UAD and CE performance of the proposed algorithm over FSF channels.

In the massive grant-free orthogonal frequency division multiple access (OFDMA), the timing and frequency offsets between users impose new challenges on joint active user detection (AUD) and channel estimation (CE) for the subsequent data recovery. In the asynchronous OFDMA, the timing and frequency offset effects can be modeled as the phase-shifting on the pilot matrix. As such, by constructing the measurement matrix with timing and frequency offsets, the joint estimation problem can be formulated as a multiple measurement vector (MMV) recovery problem with structured sparsity. However, such structured sparsity cannot be tackled by the existing compressed sensing (CS) techniques. To address this issue, we develop an efficient structured generalized approximate message passing (S-GAMP) algorithm, which includes the parallel AMP-MMV algorithm as a particular case. To deal with the high dimensionality of the measurement matrix, we propose the dynamic S-GAMP algorithm with a dynamic measurement matrix to reduce the computational complexity. Simulation results confirm the superiority of the proposed algorithms in grant-free OFDMA with both timing and frequency offsets.

Non-orthogonal multiple access (NOMA) on shared resources has been identified as a promising technology in 5G to improve resource efficiency and support massive access in all kinds of transmission modes. Power domain and code domain NOMA have been extensively studied and evaluated in both literatures and 3GPP standardization, especially for the uplink where large number of users would like to send their messages to the base station. Though different in the transmitter side design, power domain NOMA and code domain NOMA share the same need of the advanced multi-user detection (MUD) design at the receiver side. Various multi-user detection algorithms have been proposed, balancing performance and complexity in different ways, which is important for the implementation of NOMA in practical networks. In this paper, we introduce a unified variational inference (VI) perspective on various universal NOMA MUD algorithms such as belief propagation (BP), expectation propagation (EP), vector EP (VEP), approximate message passing (AMP) and vector AMP (VAMP), demonstrating how they could be derived from and adapted to each other within the VI framework. Moreover, we unveil and prove that conventional elementary signal estimator (ESE) and linear minimum mean square error (LMMSE) receivers are special cases of EP and VEP, respectively, thus bridging the gap between classic linear receivers and message passing based nonlinear receivers. Such a unified perspective would not only help the design and adaptation of NOMA receivers, but also open a door for the systematic design of joint active user detection and multi-user decoding for sporadic grant-free transmission.

In the mMTC scenario, enormous devices turn active sporadically or frequently to seek for opportunities to transmit short packets. In this highly dynamic situation, it is critical to design efficient random access (RA) procedures to cope both with the flood of simultaneous access requests and with the potential access failures. In this paper, we propose an incremental RA scheme exploiting the nested Reed-Muller (RM) sequences. Specifically, the users who suffer from access failures expand their RM sequences following the given expansion rule, which utilizes both the nested structure and the cross-correlation property of RM sequences. At the receiver, a recursive detection algorithm is proposed, which exploits the discrepancy in the sequence length to detect the retransmission users progressively. On the other hand, new active users continuously spring up in the system, thus causing the incremental number of users seeking for access. In this case, the proposed scheme can detect newly active users together with retransmission ones with great detection capability and low access latency. Our simulation results verify the superior performance of the proposed RA scheme.

The pilot design and the corresponding receiver algorithm are the key issue to support massive access via grant-free uplink transmission. In this paper, we propose a Superposition Bloom Filter (SBF) pilot design, where an SBF pilot is composed of several sequences. A SBF pilot can be distinguished from another one by different combination of sequences, i.e., the signature of the SBF pilot. The key improvement over previous BF-related work is to consider superposed complex hash-coded information instead of binary one. To guarantee the detection performance under large pilot pool size, we give the rule of constructing pilot pool based on (K;D;ρ)-expander and design a receiver algorithm including correlation-based sequence detection and compressed sensing (CS) based signature recognition, which uses the amplitude and phase information of received sequences. The simulation results show our proposed SBF pilot and corresponding receiver algorithm greatly improve the detection performance in terms of missing detection probability and false alarm probability in frequency-selective channels, compared with previous work.

In this paper, we study the equalization design for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems with insufficient cyclic prefix (CP). In particular, the signal detection performance is severely impaired by inter-carrier interference (ICI) and inter-symbol interference (ISI) when the multipath delay spread exceeding the length of CP. To tackle this problem, a deep learning-based equalizer is proposed for approximating the maximum likelihood detection. Inspired by the dependency between the adjacent subcarriers, a computationally efficient joint detection scheme is developed. Employing the proposed equalizer, an iterative receiver is also constructed and the detection performance is evaluated through simulations over measured multipath channels. Our results reveal that the proposed receiver can achieve significant performance improvement compared to two traditional baseline schemes.

True immersion into a remote scenery could not be satisfactory without high definition audiovisual senses and haptic perception of the people and objects in the target environments. We envision the future human-centric applications will evolve with four key trends, namely 1) 360° all-around XR with higher resolution; 2) 3D holographic display integrated with XR; 3) interactive haptic communications; 4) integration of multi-sensory communication. In this paper, we discuss example use cases and typical performance requirements on radio access networks for each of the four trends, hoping to shed a light on the future network design and spectrum utilization.

In this paper, we investigate channel estimation and robust signal detection for uplink massive multi-input multioutput orthogonal frequency division multiplexing systems with in-phase and quadrature-phase imbalances. By processing the real and imaginary parts of the received signal individually, we perform minimum mean square error (MMSE) estimation for the effective channel. Adjustable phase shift pilots (APSPs) are used for reducing the pilot overhead. Motivated by the optimal conditions to achieve the lower bound of the MSE of the effective channel estimation, a pilot scheduling algorithm is provided. We further propose an MMSE criterion based detection scheme which is robust to the channel estimation error. An analytical expression for the asymptotic achievable sum rate of the proposed detection is derived via using operator valued free probability theory. The performance of the channel estimation with APSPs and the robust MMSE detection are demonstrated by numerical results.

Non-orthogonal multiple access (NoMA) as an efficient way of radio resource sharing has been identified as a promising technology in 5G to help improving system capacity, user connectivity, and service latency in 5G communications. This paper provides a brief overview of the progress of NoMA transceiver study in 3GPP, with special focus on the design of turbo-like iterative multi-user (MU) receivers. There are various types of MU receivers depending on the combinations of MU detectors and interference cancellation (IC) schemes. Link-level simulations show that expectation propagation algorithm (EPA) with hybrid parallel interference cancellation (PIC) is a promising MU receiver, which can achieve fast convergence and similar performance as message passing algorithm (MPA) with much lower complexity.

This paper provides a comprehensive survey of the impact of the emerging communication technique, non-orthogonal multiple access (NOMA), on future wireless networks. Particularly, how the NOMA principle affects the design of the generation multiple access techniques is introduced first. Then the applications of NOMA to other advanced communication techniques, such as wireless caching, multiple-input multiple-output techniques, millimeter-wave communications, and cooperative relaying, are discussed. The impact of NOMA on communication systems beyond cellular networks is also illustrated, through the examples of digital TV, satellite communications, vehicular networks, and visible light communications. Finally, the study is concluded with a discussion of important research challenges and promising future directions in NOMA.

As a new multiple access technique, sparse code multiple access (SCMA) combines quadrature amplitude modulation mapper and spreading together and thus significantly improves spectral efficiency. However, the computation complexity of most existing detection algorithms increases exponentially with df (the degree of the resource nodes). The parameter dfmust be designed to be very small, which largely limits the choice of codebooks. Even if the codebooks are designed to have low density, the detection still takes considerable time. In this paper, a new detection algorithm is proposed by discretizing the probability density functions (PDFs) in the layer nodes. Actually, the PDFs are updated according to the constraints in the factor graph after discretization. Compared with the conventional message passing algorithm (MPA) detector, the proposed detector only takes polynomial time to update one message in resource nodes instead of exponential time with negligible performance loss. In particular, it only needs near-linear time, if the real part is independent with the imaginary part in SCMA system. Furthermore, this paper presents a new design for codebooks by greedy strategy and exhaustive search in each step. The search is feasible with the help of discretization, and the resulting codebooks have good performance in the proposed detection algorithm, as well as in the conventional MPA.

In this paper, distributed linear convolutional spacetime coding (DLC-STC) for two relay full-duplex (FD) 2 × 2 × 2 cooperative communication networks is presented. A network topology model which consists of two sources, two destinations and one layer of two relays, is considered. In this communication model, two sources may transmit signals to two different destinations with the help of two relays simultaneously, which will cause inter-user interference. As a result, one has to deal with the interuser interference in addition to the self-loop interference at the relays. Most part of the self-loop interference can be cancelled largely with the self-loop interference techniques at the relays, but the residual part is still a problem that requires proper processing. We consider that the two relays work in FD mode with an amplify-and-forward (AF) protocol, and the relay self-loop interference may not be cancelled completely from the received signals. Distributed space-time coding for this model is studied, for which the residual part of relay self-loop signal is applied to serve as a self-coding signal. With the self-coding property of the AF FD system, a distributed linear convolutional space time code is formed. Simulation experiments are conducted to evaluate the proposed DLC-STC for the considered FD relay communication network model, and the results illustrate the effectiveness of the presented approach.

We propose to use second order Reed-Muller (RM) sequence for user identification in 5G grant-free access. The benefits of RM sequences mainly lie in two folds, (i) support of much larger user space, hence lower collision probability and (ii) lower detection complexity. These two features are essential to meet the massive connectivity (107links/km2), ultra-reliable and low-latency requirements in 5G, e.g., one-shot transmission (≤ Ims) with ≤ 10-4packet error rate. However, the nonorthogonality introduced during sequence space expansion leads to worse detection performance. In this paper, we propose a noiseresilient detection algorithm along with a layered sequence construction to meet the harsh requirements. Link-level simulations in both narrow-band and OFDM-based scenarios show that RM sequences are suitable for 5G.

Due to radio-frequency (RF) circuit mismatch, the channel reciprocity of time-division duplex massive multiple-input multiple-output system is impaired. Under this condition, there exist several different approaches for base station (BS) to obtain the downlink (DL) channel estimate based on the minimum mean-square-error (MMSE) estimation method. We show that with the RF mismatch parameters BS will obtain the same DL channel estimates via these different approaches. As the DL channel estimate is usually imperfect, we propose an MMSE based multiuser precoding technique, which is robust to the estimation error. Furthermore, we derive an asymptotic approximation of the ergodic sum rate for the robust MMSE precoding using the large dimensional random matrix theory, which is tight as the numbers of both antennas at BS and user terminals tend to infinity with a fixed non-zero and finite ratio. Our results are verified by the simulations.

This paper aims to evaluate the system level performance of the newly proposed non-orthogonal multiple access scheme sparse code multiple access (SCMA) with uplink grant-free transmissions for 5G massive machine-type communication (mMTC) scenario. The challenge lies in the modeling and abstraction of physical layer actions as the link-to-system interface, including the features of overloading, grant-free transmission, as well as non-linear receiver. Specifically, we propose to use single bound to simplify the maximum-likelihood (ML) receiver modelling with only linear computational complexity, while still guaranteeing sufficient accuracy requirement. Extensive simulations verify the correctness of the method under a series of practical assumptions. Evaluation results show that non-orthogonal multiple access schemes such as SCMA with grant-free can provide significant system capacity gain over orthogonal multiple access such as grant-free OFDMA (about 3.5 times as shown in the evaluated scenario) in terms of packet arrival rate at given system outage (defined as 1% packet drop rate). It is noted that the proposed method has been just agreed in 3gpp RAN1 86 meeting and included in the technical report.

Full-duplex (FD) networks promise nearly doubled spectrum efficiency for single links with beyond 100 dB self-interference cancellation capability. However, the performance of FD networking is not that straightforward, as there will be several new types of interference introduced. Hence, it is critical to exploit some radio resource management techniques to combat the annoying interference and inevitably needs to reserve part of radio resources in HD mode to obtain the essential auxiliary information. It is naturally to ask which physical channels and signals could be left in the FD mode and how much system gain FD cellular networks could bring? In this paper, we try to answer these questions. We first propose the feasibility to leave one kind of physical channels or signals in the FD mode and then highlight some potential resource management schemes. We will also demonstrate the system gain of FD networks by leveraging our proposed design and the management techniques. Our simulation results show FD communications leads to promising performance improvement.

5G wireless networks are expected to support new applications demanding ultra high reliability and ultra low latency, such as self-driving cars, industrial control, and real-time gaming. Such services are known as ultra-reliable and low-latency communications (URLLC) targeting at 99.999% transmission correctness within 1 ms delay bound. Current Long Term Evolution (LTE) system is far from supporting such services, especially for uplink. Even with around 10 times shortened transmission interval, due to the waste of time in requesting and receiving the scheduling grant, only one shot uplink transmission can be supported within 1 ms, which is very hard to meet the reliability requirement. Grant-Free transmission is thus proposed to effectively save the time of requesting/waiting for grant and thus relax the transmission interval design in frame structure. This paper presents the design of grant-free transmission for URLLC services and also the evaluation results. We shall show that the saved time can be used for more Hybrid Automatic Repeat reQuest (HARQ) retransmissions and thus further enhance the reliability. Moreover, contention based grant-free transmission with proper HARQ design can well meet the reliability requirement. Finally, the performance can be further enhanced with non-orthogonal multiple access schemes such as Sparse Code Multiple Access (SCMA) and advanced receivers such as Message Passing Algorithm (MPA) or Expectation Propagation Algorithm (EPA) receiver.

Non-orthogonal multiple access has been extensively discussed in New Radio (NR), the study item working on 5G air interface in 3GPP. Sparse code multiple access (SCMA) is one of the proposed MA schemes. In this paper, the basic concept of SCMA is introduced, including codebook mapping, multiple access procedure, and advanced receivers. Then, link level simulations are applied to verify the design of SCMA, which show that SCMA has many excellent properties: shaping and diversity gain by sparse codebooks, resilient to inter- user interference, and robust to codebook collision, thus it is a promising candidate MA scheme for 5G.

A user-centric no-cell (UCNC) framework has been proposed which enables different level coordination over a large number of TRPs. This paper presents some key technologies in UCNC framework with the focus on PoC of UL grant- free transmissions in UCNC architecture. Massive connectivity support is one of the three important scenarios in 5G networks. The challenges to support massive connectivity lie in the cost of signaling overhead and transmission latency. An uplink grant- free transmission based on SCMA (sparse code multiple access) design is proposed. The proposed scheme can provide different levels of overloading to efficiently meet the massive connectiv- ity requirements. Extensive lab tests have been conducted to verify the performance matrices. Taken LTE as a baseline for comparison purpose, it is shown that the signaling overhead by using uplink grant-free transmission can be reduced by around 80% and the user plane transmission latency in ECO state can be reduced by around 93%. Moreover, massive connectivity is efficiently supported with SCMA scheme that yields around 230% gain over OFDMA in terms of supported active users.

Sparse code multiple access (SCMA) scheme is considered to be one promising non-orthogonal multiple access technology for the future fifth generation (5G) communications. Due to the sparse nature, message passing algorithm (MPA) has been used at the receiver to achieve close to maximum likelihood (ML) detection performance with much lower complexity. However, the complexity order of MPA is still exponential with the size of codebook and the degree of signal superposition on a given resource element. In this paper, we propose a novel low complexity iterative receiver based on expectation propagation algorithm (EPA), which reduces the complexity order from exponential to linear. Simulation results demonstrate that the proposed EPA receiver achieves nearly the same block error rate (BLER) performance as the conventional message passing algorithm (MPA) receiver with orders less complexity.

Ultrahigh data rate, massive connectivity, ultralow latency, and high reliability, as well as ultraflexible air interface design to support diversified usage scenarios, are the major targets of 5G radio access network design. To meet these targets, advanced new radio transmission technologies, including new waveform, new channel coding, non-orthogonal multiple access, as well as massive antenna techniques, have been proposed and studied worldwide in both academic and industries. Most of the gains claimed in the literature are from simulations or by small scale lab testing. Instead, this paper elaborates on the first hand results and analysis of the large-scale field trials carried out in China for a selection of key 5G technology components as well as some of their combinations, including filtered-orthogonal frequency division multiplexing (OFDM) (f-OFDM), polar code, sparse code multiple access, and massive multi-input multi-output (MIMO) for both uplink and downlink cellular networks. Testing results verify the feasibility and benefit of each technology contributing toward the diversified 5G targets, as well as the feasibility for these technologies to work jointly for higher spectrum efficiency, larger connectivity, and lower latency.

Spectral efficiency is always a key factor to be improved and optimized along mobile communication networks evolving generation by generation. 5G enabling technologies must take spectral efficiency into consideration. In this paper, we show the performance of three key 5G technologies in sense of spectral efficiency improvement. Sparse code multiple access, polar codes, and filtered orthogonal frequency-division multiplexing are novel multiple access technology, channel coding scheme, and waveform, respectively. The combination of them is implemented in a 5G field trial testbed by NTT DOCOMO and Huawei for the first time. According to the field test results, we achieve over 100% spectral efficiency improvement comparison with baseline, where orthogonal frequency-division multiple access and turbo coding as LTE are used.

A 5G mobile network has evolved from a cell centric radio access network to a user centric one. A user-centric no-cell (UCNC) framework has been proposed, which enables physical layer coordination over a large number of transmission and reception points for each mobile user to always experience cell-center-like communications. This paper presents some key technologies in the UCNC framework. The focus is on proof of concept of UL grant-free transmissions in the UCNC architecture. Massive connectivity supporting a huge number of devices is one of the three important scenarios in 5G networks. The challenges to support massive connectivity lie in the cost of signaling overhead and transmission latency. An uplink grant-free transmission based on sparse code multiple access (SCMA) design is proposed. The proposed scheme can provide different levels of overloading to efficiently meet the massive connectivity requirements. Grant-free transmission conducted in RRC connected state can reduce signaling overhead, while in energy conserved operation (ECO) state, it can significantly reduce the user plane transmission latency. Extensive laboratory testing has been conducted in a collaborative project of Huawei and Telefonica to verify the performance matrices. Taking LTE as a baseline for comparison purposes, it is shown that the signaling overhead by using uplink grant-free transmission can be reduced by around 80% and the user plane transmission latency in ECO state can be reduced by around 93%. Moreover, massive connectivity is efficiently supported with the SCMA scheme that yields around 230% gain over OFDMA in terms of supported active users.

In this paper, a delay diversity orthogonal frequency-division multiplexing (DD OFDM) transmission scheme in amplify-and-forward (AF) full-duplex relay systems is investigated. One direct source-to-destination link, one relay forwarding link, and residual self-interference are considered in the system. The necessary cyclic prefix length is investigated and a suitable AF relay protocol in the full-duplex relay OFDM system is proposed. This paper demonstrates that the AF relay link and the direct source-to-destination link can be combined to provide spatial diversity. The key is that the DD OFDM scheme is used to transform the spatial diversity into increased channel frequency diversity that is further exploited by using the bit-interleaved coding. The bit error rate performance of the proposed system is verified by simulation results.

In this paper, we address the tradeoff between energy efficiency (EE) and spectral efficiency (SE) for cellular networks with full-duplex (FD) communications enabled base stations. To be backward compatible with legacy LTE systems, it is assumed that user devices still work in the conventional half-duplex (HD) mode. There usually exists residual self-interference (RSI) in FD communications after advanced interference suppression techniques are applied. In this paper, we consider two different RSI models: constant RSI model and linear RSI model. First, the necessary conditions for an FD transceiver to achieve better EE-SE tradeoff than an HD one are derived for both the RSI models. Then, for the constant RSI model, a closed-form EE-SE expression is obtained in the scenario of single pair of users. We further extend our result and prove that EE is a quasi-concave function of SE in the scenario of multiple user pairs. Accordingly, an optimal algorithm to achieve the maximum EE based on the Lagrange dual decomposition technique is developed. For the linear RSI model, the EE-SE relation is difficult to deal with and we develop a heuristic algorithm by decoupling the problem into two sub-problems: power control and resource allocation. Our analysis and algorithms are finally verified by comprehensive numerical results.

Full-duplex (FD) communications benefit from recent breakthroughs in analog and digital signal processing and demonstrate nearly doubled spectrum efficiency for point-to-point links with beyond 100 dB self-interference cancellation capability. However, FD cellular networks with base stations (BSs) in FD mode and user terminals (UEs) in half-duplex mode also introduce intra-cell inter-UE interference and other kinds of interference, which might even eat up the potential gain. In this paper, we propose to take advantage of power control and inter-UE interference cancellation (IC) techniques to cope with the bothersome intra-cell inter-UE interference. We verify that the power control solution for single-cell FD network with a given user pair exhibits an interesting binary feature, that is, either both the BS and the uplink UE transmit at the maximum power or one of them completely mutes to achieve the optimal sum rate. Moreover, the binary feature holds even when inter-UE IC is applied, thus showing appealing computational efficiency. We also demonstrate significant performance improvement by applying binary power control with inter-UE IC to both singlecell and multi-cell FD networks.

Sparse code multiple access (SCMA) is a code domain non-orthogonal multiple-access technique introduced for future 5G wireless networks. This paper describes the basic ideas of SCMA, the SCMA codebook design, the encoder and decoder, as well as the SCMA enabled transmission schemes for different application scenarios, including uplink grant-free contention-based transmission, downlink multi-user superposition, and downlink open-loop CoMP for ultra-dense networks even with moving users. We shall show from design principles and application examples that SCMA can resolve some major issues of current wireless systems and establish itself as a strong candidate for 5G networks.

In the future Internet of Things (IoT), a multitude of objects are to be provided with always-on-line connections and dynamic access to the network services, which brings new challenges to multiple access protocol design. In this paper, a novel multiple access protocol, termed as "multi-carrier rateless multiple access (MC-RMA)'', is proposed to overcome such challenges. Instead of assigning the available resource elements (REs) to the users in a fixed and centralized manner, the base station (BS) simply assigns a specific distribution profile to each user, with which each user randomly chooses a certain number of coded symbols, linearly combines them, and then transmits the resultant signal to BS until receiving an acknowledgement (ACK) from BS. The transmission process of each user, as well as those of all the users as a whole, resembles a special form of linear superposition rateless coding and the user information can be decoded using the low-complexity belief propagation (BP) algorithm. Due to the inherent rate adaptation property of rateless codes, the proposed RMA protocol is quite suitable for dynamic massive access in future IoT.

Sparse code multiple access (SCMA) is a novel non-orthogonal multiple access scheme, in which multiple users access the same channel with user-specific sparse codewords. In this paper, we consider an uplink SCMA system employing channel coding, and develop an iterative multiuser receiver which fully utilizes the diversity gain and coding gain in the system. The simulation results demonstrate the superiority of the proposed iterative receiver over the non-iterative one, and the performance gain increases with the system load. It is also shown that SCMA can work well in highly overloaded scenario, and the link-level performance does not degrade even if the load is as high as 300%.

The rapid traffic growth and ubiquitous access requirements make it essential to explore the next generation (5G) wireless communication networks. In the current 5G research area, non-orthogonal multiple access has been proposed as a paradigm shift of physical layer technologies. Among all the existing non-orthogonal technologies, the recently proposed sparse code multiple access (SCMA) scheme is shown to achieve a better link level performance. In this paper, we extend the study by proposing an unified framework to analyze the energy efficiency of SCMA scheme and a low complexity decoding algorithm which is critical for prototyping. We show through simulation and prototype measurement results that SCMA scheme provides extra multiple access capability with reasonable complexity and energy consumption, and hence, can be regarded as an energy efficient approach for 5G wireless communication systems.

Puncturing is widely used to generate rate-compatible codes. However, for punctured polar codes, some puncturing patterns may greatly affect the split bit channels and cause considerable performance loss. In this paper, we aim to investigate how the split bit channels are affected by various puncturing patterns, and then evaluate the performances of these patterns. We propose a search algorithm to design good punctured polar codes, and prove that designing the optimal puncturing pattern for output bits is equivalent to finding the optimal puncturing pattern for frozen bits. We also propose a heuristic approach based on the idea of polarization to select the position of each punctured output bit, and simulations show that the puncturing pattern designed this way almost achieves the same performance as the optimal one selected by exhaustive search.

Linear encoders with good joint spectra are suitable candidates for optimal lossless joint source-channel coding (JSCC), where the joint spectrum is a variant of the input-output complete weight distribution and is considered good if it is close to the average joint spectrum of all linear encoders (of the same coding rate). In spite of their existence, little is known on how to construct such encoders in practice. This paper is devoted to their construction. In particular, two families of linear encoders are presented and proved to have good joint spectra. The first family is derived from Gabidulin codes, a class of maximum-rank-distance codes. The second family is constructed using a serial concatenation of an encoder of a low-density parity-check code (as outer encoder) with a low-density generator matrix encoder (as inner encoder). In addition, criteria for good linear encoders are defined for three coding applications: 1) lossless source coding; 2) channel coding; and 3) lossless JSCC. In the framework of the code-spectrum approach, these three scenarios correspond to the problems of constructing linear encoders with good kernel spectra, good image spectra, and good joint spectra, respectively. Good joint spectra imply both good kernel spectra and good image spectra, and for every linear encoder having a good kernel (respectively, image) spectrum, it is proved that there exists a linear encoder not only with the same kernel (respectively, image) but also with a good joint spectrum. Thus, a good joint spectrum is the most important feature of a linear encoder.

In this paper, we consider energy efficient coverage planning in cellular networks. To save energy, each base station (BS) can work in sleep mode when there is no user in its coverage or the users can be served by neighbor base stations. With increasing coverage overlap, there is a tradeoff between the number of BSs per unit area and the proportion of active base stations. Analytical and numerical methods are presented to evaluate coverage planning schemes with different inter-BS distance. Evaluation results show that compared with the minimum coverage overlap scheme, the optimal planning scheme can reduce the network energy consumption by more than 20%, and the performance improvement depends on the user density, network topology, and the power consumption of sleep mode.

To meet the global challenge of reducing greenhouse gas emissions and the explosive demand of wireless data traffic, green architecture design is becoming a critical issue for mobile network operators. Heterogeneous deployments of different cell types have been used to fulfill the challenges mentioned above. In this respect, a critical concern for operators is how to deploy small cells in a green manner such that the global network is spectrum-efficient as well as energy-efficient. In this paper, we characterize the spectrum efficiency (SE) and energy efficiency (EE) for heterogenous wireless networks, taking into account realistic network power consumption model and dynamic network configuration. We give first order closed form analysis to address this issue and show that SE and EE may not be contradictory to each other as in the traditional networks. Our study also provides useful insights for the modeling and deployment of future green wireless networks.

Bandwidth-power tradeoff, as one of the key relations in the green radio research framework, has attracted numerous research attentions in the recent years, due to the refreshed spectrum policies and the popularity of enabling techniques for bandwidth adjustment (such as software defined radio). Previous literatures show some preliminary results on the bandwidth-power tradeoff for heterogeneous networks, however, the practical issues such as site sleeping and inter-cell interference are still out of the research scope. In this paper, we formulate the network energy minimization problem and investigate the optimal bandwidth allocation strategy under the above two issues. Optimal bandwidth-power tradeoff is then derived to show the effects of key system parameters, including cell sizes and interference power levels, followed by some numerical examples to co-verify the analytical results.

In this paper, we investigate adaptive configuration of spatial and frequency resources to maximize energy efficiency (EE) and reveal the relationship between the EE and the spectral efficiency (SE) in downlink multiple-input-multiple-output (MIMO) orthogonal frequency division multiple access (OFDMA) systems. We formulate the problem as minimizing the total power consumed at the base station under constraints on the ergodic capacities from multiple users, the total number of subcarriers, and the number of radio frequency (RF) chains. A three-step searching algorithm is developed to solve this problem. We then analyze the impact of spatial-frequency resources, overall SE requirement and user fairness on the SE-EE relationship. Analytical and simulation results show that increasing frequency resource is more efficient than increasing spatial resource to improve the SE-EE relationship as a whole. The EE increases with the SE when the frequency resource is not constrained to the maximum value, otherwise a tradeoff between the SE and the EE exists. Sacrificing the fairness among users in terms of ergodic capacities can enhance the SE-EE relationship. In general, the adaptive configuration of spatial and frequency resources outperforms the adaptive configuration of only spatial or frequency resource.

The tremendous popularity of smart phones and electronic tablets has spurred the explosive growth of high-rate multimedia services and promptly boomed energy consumption in wireless networks. Therefore, energy-efficient design in wireless networks is very important and is attracting more and more attention, just like the conventional spectral-efficient design. In this paper, we study energy-efficient design in downlink orthogonal frequency division multiple access (OFDMA) networks with effective capacity-based delay provisioning for delay-sensitive traffic. By integrating information theory with the concept of effective capacity, we formulate an energy efficiency (EE) optimization problem with statistical delay provisioning, which is a complicated nonconvex combinatorial fractional programming problem. To solve the problem, we first relax it with an upper bound on the original one and then prove and exploit the quasiconcave property of the EE-versus-transmit power curve, which facilitates the optimal algorithm development. Then, we demonstrate that the resultant solution is quite close to the true optimal value when the number of subcarriers is larger than that of the users. We also analyze the tradeoff between EE and delay, the relationship between spectral-efficient and energy-efficient designs, and the impact of system parameters, including circuit power and delay exponents, on the overall performance. Numerical results show that the proposed energy-efficient design scheme greatly improves EE while maintaining the delay requirement.

To meet the global challenge of reducing greenhouse gas emissions and the explosion demand of wireless data traffic, green architecture design is becoming a critical issue for mobile network operators. Heterogeneous deployments of different cell types have been used to fulfill the challenges mentioned above. In this respect, a critical concern for operators is how to deploy small cells in a green manner such that the global network is cost-effective as well as energy-efficient. In this paper, we characterize the energy efficiency (EE) and deployment efficiency (DE) for heterogenous wireless networks, taking into account realistic network power consumption model and dynamic network configuration. We give first order closed form analysis to address this issue and show that a proper density of small cells is required to obtain the maximum achievable EE/DE. Our study also provides useful insights for the modeling and deployment of future green wireless networks.

In this paper, we analyze the fundamental power-delay tradeoff in point-to-point wireless communication systems with renewable energy source. We consider the max queue-weighted (MWQ) algorithm, where the transmitter determines the rate and power control actions based on the instantaneous channel state information (CSI), the data queue state information (DQSI) and renewable energy storage information (RESI). We exploit a general fluid dynamics of the data queue and renewable energy storage buffer using continuous time dynamic equations. Using the sample-path approach and renewal theory, we decompose the average delay in terms of multiple unfinished works along a sample path, and obtain bounds for the average delay and AC power consumption under the MWQ algorithm, which are asymptotically tight at small delay regime. We show that despite the randomness of the renewable energy arrival and energy buffer size, the average AC power (P) of the MWQ policy is given by P = O(D exp(1/D)) at small delay D regime. We also quantify the impacts of the renewable energy storage size in the scaling coefficient.

In this paper, we investigate adaptive configuration of spatial and frequency resources to maximize energy efficiency (EE) and reveal the relationship between the EE and the spectral efficiency (SE) in downlink multiple-input-multiple-output (MIMO) orthogonal frequency division multiple access (OFDMA) systems. We formulate the problem as minimizing the total power consumed at the base station under constraints on the average data rates from multiple users, the total number of subcarriers, and the number of radio frequency (RF) chains. We develop a two-step searching algorithm to solve this problem, which first finds the near-optimal numbers of subcarriers for multiple users based on Karush-Kuhn-Tucker (KKT) conditions and then optimize the number of active RF chains. Simulation results demonstrate that increasing frequency resource improves both the SE and the EE, and is more efficient than increasing spatial resource. Consequently, there exists tradeoff between the SE and the EE only when the frequency resource is limited. In general, the adaptive configuration of spatial and frequency resources outperforms the adaptive configuration of only spatial resource and that of only frequency resource.

In this paper, we focus on the power-delay tradeoff for downlink transmission systems. In particular, we target to design the adaptive modulation scheme that achieves the optimal power-delay tradeoff under practical concerns, such as the practical power consumption model of a base station and the dynamics of user traffic. The optimal modulation level selection problem is formulated in a Markov decision problem and a cross-layer approach is adopted to take both queue and channel dynamics into consideration. Simulation results show that the proposed policy achieves about 20% power saving for the same delay performance, compared with fixed modulation level schemes. In addition, the impact of the practical power consumption model under dynamic traffic does change the optimal modulation level selection philosophy.

The explosively increasing demand of high-data-rate multimedia wireless services and ubiquitous access has triggered rapidly booming energy consumption at the wireless network operator side. Therefore, energy-efficient design is becoming a mainstream for future wireless networks. In this paper, we study energy-efficient resource allocation in downlink OFDMA networks with partial channel state information at the transmitter (CSIT). To reduce the channel state information (CSI) feedback overhead while maintaining relatively high achievable energy efficiency (EE), we propose a novel CSI feedback scheme, which leads to higher EE with lower feedback overhead compared with the conventional selective feedback (SF) and bit-map based feedback (BF) schemes. Simulation results show that the energy-efficient design greatly improves EE compared with that of the conventional spectral-efficient design and our CSI feedback scheme outperforms the conventional CSI feedback schemes in EE and spectral efficiency (SE) with almost the same feedback overhead.

The widespread application of multimedia wireless services and requirements of ubiquitous access have triggered rapidly booming energy consumption at both the base station side and the user equipment (UE) side. Hence, energy-efficient design in wireless networks is very important and is becoming an inevitable trend. In this paper, we study the energy-efficient resource allocation in both downlink and uplink cellular networks with orthogonal frequency division multiple access (OFDMA). For the downlink transmission, the generalized energy efficiency (EE) is maximized while for the uplink case the minimum individual EE is maximized, both under certain prescribed per-UE quality-of-service (QoS) requirements. For both transmission scenarios, we first provide the optimal solution and then develop a suboptimal but low-complexity approach by exploring the inherent structure and property of the energy-efficient design. For the downlink case, by modifying the original problem, we also find a computationally efficient and numerically tractable upper bound on the EE, which indicates the performance limit and is demonstrated to be quite tight if the number of subcarriers is larger than that of UEs and motivates us to find a near-optimal approach relying on the quasiconcave relation between the modified EE and transmit power. Simulation results show that the energy-efficient design greatly improves EE compared with the conventional spectral-efficient design and the low-complexity suboptimal approaches can achieve a promising tradeoff between performance and complexity.

In this paper, power allocation between pilots and data symbols is investigated to maximize energy efficiency (EE) for downlink orthogonal frequency division multiple access (OFDMA) networks. We first derive an EE function considering channel estimation error, which depends on large-scale channel gains of multiple users, allocated power to pilots and data symbols, and circuit power consumption. Then an optimization problem is formulated to maximize the EE under overall transmit power constraint. Exploiting the quasiconcavity property of the EE function, we propose an alternating optimization method in the low transmit power region and reformulate a joint quasiconcave problem in the high transmit power region. Analysis and simulation results show that the power ratio for pilots decreases with the circuit power. When the circuit power is small, the optimal overall transmit power increases with the circuit power. Otherwise, the optimal transmit power does not depend on it. Transmitting more data symbols to the users with higher channel gains improves the EE but at a cost of sacrificing the fairness among multiple users. Simulation results also demonstrate that compared with spectral efficiency (SE)-oriented design, the EE-oriented design can improve the EE performance significantly with a relatively small SE loss.

The widespread application of multimedia wireless services and requirement of ubiquitous access have triggered rapidly booming energy consumption at both the base station side. Hence, energy-efficient design in wireless networks is very important and is becoming an inevitable trend. In this paper, we study energy-efficient resource allocation in downlink cellular OFDMA networks. For the downlink transmission, the weighted energy efficiency (EE) is maximized under certain prescribed per-user quality- of-service (QoS) requirements. We first obtain the optimal solution then propose a suboptimal approach by exploring the inherent structure and property of the energy-efficient design to reduce complexity. Simulation results show that the energy-efficient design greatly improves EE compared with that of the conventional spectral-efficient design and our low- complexity suboptimal approaches can achieve promising tradeoff between performance and complexity.

In this paper, power allocation between pilots and data symbols is investigated aiming at maximizing energy efficiency(EE) for downlink orthogonal frequency division multiple access (OFDMA) networks. We first derive an EE function when the channel estimation error is considered, which depends on the large-scale channel gains of multiple users, the allocated power to pilots and data symbols, and the circuit power consumption. Then an optimization problem is formulated to maximize the EE under overall transmit power constraint. The relationship between the power for pilots and data symbols is analyzed based on Karush-Kuhn-Tucker (KKT) conditions and the impacts of channel gains on both power allocation and the EE are studied. Exploiting the quasiconcavity property of the EE function, a bisection searching algorithm is developed to find the optimal power allocation. Simulation results demonstrate the performance gain of the proposed optimal power allocation scheme in terms of the EE and the required overall transmit power.

Conventional design of wireless networks mainly focuses on system capacity and spectral efficiency (SE). As green radio (GR) becomes an inevitable trend, energy-efficient design in wireless networks is becoming more and more important. In this paper, the fundamental tradeoff relation between energy efficiency (EE) and SE in downlink orthogonal frequency division multiple access (OFDMA) networks is addressed. We obtain a tight upper bound and lower bound on the optimal EE-SE tradeoff relation for general scenarios based on Lagrange dual decomposition, which accurately reflects the optimal EE-SE tradeoff relation. We then focus on a special case that priority and fairness are considered and derive an alternative upper bound, which is even proved to be achievable for flat fading channels. We also develop a low-complexity but near-optimal resource allocation algorithm for practical application of EE-SE tradeoff. Numerical results demonstrate that the optimal EE-SE tradeoff relation is a bell shape curve and can be well approached with our resource allocation algorithm.

Energy efficiency has become an important metric for the future system design. In traditional design methods, the link transmission strategies were investigated with only transmit power considered. We shall show in this paper that from the whole system's aspect, the radiated energy used for data transmission is only a portion of the overall power consumption, whose ratio depends on many practical issues such as the transmission distance, the modulation level, the non-ideal power amplifier efficiency, as well as the circuit and processing power. Through closed-form formula derivation, analysis remarks, and numerical examples, we shall show the impact of different system parameters and configurations on the whole system's power consumption and energy efficiency, which in turn, sheds a light on the design philosophy for maximizing the energy efficiency of a base station as a whole.

In this paper, both configuration of active radio frequency (RF) chains and resource allocation are investigated for improving energy efficiency of downlink multiple-input-multiple-output (MIMO) orthogonal frequency division multiple access (OFDMA) systems. We first formulate an optimization problem to minimize the total power consumed at the base station with the maximum transmit power constraint and ergodic capacity constraints from multiple users. Then a two-step suboptimal algorithm is proposed. Specifically, the continuous variable optimization problem is first solved, and then a discretization algorithm is presented to obtain the number of active RF chains and the number of subcarriers allocated to each user. Simulation results demonstrate that the proposed algorithm can provide significant power-saving gain over the all-on RF chain scheme and the adaptive subcarrier allocation helps to save more power.

The average weight distribution of a regular low-density parity-check (LDPC) code ensemble over a finite field is thoroughly analyzed. In particular, a precise asymptotic approximation of the average weight distribution is derived for the small-weight case, and a series of fundamental qualitative properties of the asymptotic growth rate of the average weight distribution are proved. Based on this analysis, a general result, including all previous results as special cases, is established for the minimum distance of individual codes in a regular LDPC code ensemble.

Conventional design of wireless networks mainly focuses on system capacity and spectral efficiency (SE). As green radio (GR) becomes an inevitable trend, energy-efficient design is becoming more and more important. In this paper, the fundamental tradeoff between energy efficiency (EE) and SE in downlink orthogonal frequency division multiple access (OFDMA) networks is addressed. We first set up a general EE-SE tradeoff framework, where the overall EE, SE and per-user quality-of-service (QoS) are all considered, and prove that under this framework, EE is strictly quasiconcave in SE. We then discuss some basic properties, such as the impact of channel power gain and circuit power on the EE-SE relation. We also find a tight upper bound and a tight lower bound on the EE-SE curve for general scenarios, which reflect the actual EE-SE relation. We then focus on a special case that priority and fairness are considered and suggest an alternative upper bound, which is proved to be achievable for flat fading channels. We also develop a low-complexity but near-optimal resource allocation algorithm for practical application of the EE-SE tradeoff. Numerical results confirm the theoretical findings and demonstrate the effectiveness of the proposed resource allocation scheme for achieving a flexible and desirable tradeoff between EE and SE.

The pressure of the energy bill from operators and the request of the environmental protection from the governments and publics results in more urgent requirement on the energy consumption and CO2emission. Traditionally, adaptive modulation has been proven to be an efficient solution to improve the system performance over the radio fading channels. To obtain the design insights for energy efficient adaptive modulation scheme, we shall introduce a circuit-level power modeling to analyze the effects and characterize the bandwidth-power-energy efficiency tradeoff relations for wireless communication systems. We show that different bandwidth, power and modulation schemes shall be used under different channel conditions to maximize the energy efficiency and the adaptive modulation scheme can greatly help to improve the tradeoff curves.

In this paper, we quantify the benefits of employing relay station in large-coverage cognitive radio systems which opportunistically access the licensed spectrum of some small-coverage primary systems scattered inside. Through analytical study, we show that even a simple decode-and-forward (SDF) relay, which can hold only one packet, offers significant pathloss gain in terms of the spatial transmission opportunities and link reliability. However, such scheme fails to capture the spatial-temporal burstiness of the primary activities, that is, when either the source-relay (SR) link or relay-destination (RD) link is blocked by the primary activities, the cognitive spectrum access has to stop. To overcome this obstacle, we further propose buffered decode-and-forward (BDF) protocol. By exploiting the infinitely long buffer at the relay, the blockage time on either SR or RD link is saved for cognitive spectrum access. The buffer gain is shown analytically to improve the stability region and average end-to-end delay performance of the cognitive relay system.

In this paper, we consider delay-optimal MIMO precoder and power allocation design for a MIMO Link in wireless fading channels. There are L data streams spatially multiplexed onto the MIMO link with heterogeneous packet arrivals and delay requirements. The transmitter is assumed to have knowledge of outdated channel state information (CSIT) as well as the joint queue state information (QSI) of the L buffers. Using static sorting of the L eigenchannels, we decompose the L-dimensional MDP into L independent 1-dimensional MDP and derived low complexity precoding and power control policies (with linear complexity) to minimize average delays of the L application streams.

Cognitive and cooperative technologies are two core components in the design of next generation wireless networks. One key issue associated with cognitive transmission is the inefficient spectrum sharing of the secondary system, especially for secondary communications separated by long distance. To boost the spectrum sharing efficiency, cognitive multi-relay system appears to be an attractive solution for the cognitive transmission systems. In this paper, we consider a cognitive multi-relay (CMR) system and propose a novel CMR buffered decode-and-forward protocol that exploit the buffers in the source and each relay node. Moreover, we derive the closed-form average end-to-end delay and the stability region by exploiting the birth-death nature of the queue dynamics and the methods of state aggregation and queue dominance. Comparing with the baseline protocols through analytical and numerical results, the proposed CMR-BDF scheme can dynamically adjust the cognitive transmission to exploit the spatial PU burstiness while simultaneously benefits from the advantage of double-sided selection diversity in both the source-relay and relay-destination interfaces.

Spectrum sharing between wireless networks improves the usage efficiency of radio spectrums. This paper addresses spectrum sharing between a cellular uplink and a mobile ad hoc networks. These networks use either all uplink frequency subchannels or their disjoint subsets, called spectrum underlay and spectrum overlay, respectively. Given these methods, the capacity tradeoff between the coexisting networks is analyzed in terms of transmission capacity. For a network with Poisson distributed transmitters, this metric is defined as the maximum density of transmitters subject to an outage constraint for a given signal-to-interference ratio (SIR). Using stochastic geometry, the transmission-capacity tradeoff between the coexisting networks is derived, where both spectrum overlay and underlay as well as successive interference cancellation (SIC) are considered. In particular, for small target outage probability, the transmission capacities of the coexisting networks are proved to satisfy a linear equation. Its coefficients depend on the spectrum sharing method and whether SIC is applied. This linear equation shows that spectrum overlay is more efficient than spectrum underlay.

Spectrum sharing between wireless networks improves the efficiency of spectrum usage, and thereby alleviates spectrum scarcity due to growing demands for wireless broadband access. To improve the usual underutilization of the cellular uplink spectrum, this paper addresses spectrum sharing between a cellular uplink and a mobile ad hoc networks. These networks access either all frequency subchannels or their disjoint subsets, called spectrum underlay and spectrum overlay, respectively. Given these spectrum sharing methods, the capacity trade-off between the coexisting networks is analyzed based on the transmission capacity of a network with Poisson distributed transmitters. This metric is defined as the maximum density of transmitters subject to an outage constraint for a given signal-to-interference ratio (SIR). Using tools from stochastic geometry, the transmissioncapacity trade-off between the coexisting networks is analyzed, where both spectrum overlay and underlay as well as successive interference cancelation (SIC) are considered. In particular, for small target outage probability, the transmission capacities of the coexisting networks are proved to satisfy a linear equation, whose coefficients depend on the spectrum sharing method and whether SIC is applied. This linear equation shows that spectrum overlay is more efficient than spectrum underlay. Furthermore, this result also provides insight into the effects of network parameters on transmission capacities, including link diversity gains, transmission distances, and the base station density. In particular, SIC is shown to increase the transmission capacities of both coexisting networks by a linear factor, which depends on the interference-power threshold for qualifying canceled interferers.

In this paper, we consider delay-optimal MIMO precoder and power allocation design for a MIMO link in wireless fading channels. There are L data streams spatially multiplexed onto the MIMO link with heterogeneous packet arrivals and delay requirements. The transmitter is assumed to have knowledge of the channel state information (CSI) as well as the joint queue state information (QSI) of the L buffers. Using L-dimensional Markov decision problem (MDP), we obtain optimal precoding and power allocation policies for general delay regime, which consists of an online solution and an offline solution. The online solution has negligible complexity but the offline solution has worst case complexity part((N + 1)L) where N is the buffer size. Using static sorting of the L eigenchannels, we decompose the MDP into L independent 1-dimensional subproblems and obtained low complexity offline solution with linear complexity order part(NL) and close-to-optimal performance.

In this paper, we consider a cognitive radio system in fading wireless channels and propose an opportunistic power control strategy for the cognitive users, which serves as an alternative way to protect the primary user's transmission and to realize spectrum sharing between the primary user and the cognitive users. The key feature of the proposed strategy is that, via opportunistically adapting its transmit power, the cognitive user can maximize its achievable transmission rate without degrading the outage probability of the primary user. If compared with the existing cognitive protocols, which usually try to keep the instantaneous rate of the primary user unchanged, our strategy relieves the cognitive users from the burden of detecting and relaying the message of the primary user and relaxes the system synchronization requirements. The achievable rate of a cognitive user under the proposed power control strategy is analyzed and simulated, taking into account the impact of imperfect channel estimation. A modified power control strategy is also proposed to reduce the sensitivity of our strategy to the estimation errors. Its effectiveness is verified by the simulations.

Cooperative communication has emerged as a promising technique to enhance system reliability and performances in resource-limited wireless networks. Before any kind of cooperation ever starts, nodes in the network have to find someone to cooperate with and the helping node is so called partner. This paper concerns about the partner selection problem in centralized networks, where an AP assigns partner for each node and schedules all the transmission. Three optimal partner selection strategies with different aims are proposed and analyzed under fixed and variable transmit power respectively. They are the maximum total utility (MTU) strategy, the maximum minimum utility (MMU) strategy and the maximum product utility (MPU) strategy. Simulation results show that with variable transmit power, both system-level efficiency and node-level fairness can be greatly improved, and the three strategies successfully achieve their goals respectively.

In this paper, the issue of partial channel state information (CSI) based cooperative relaying and partner selection is discussed. First, we propose a new selection relaying scheme which switches between two fixed modes: direct transmission, and regenerative relaying cooperation. Unlike the existing scheme which employs instantaneous source-relay channel gain as criteria of the switching, we utilize only the geographical information of source, relay and destination terminals. Then, in order to apply our proposed scheme to large-scale networks, a cooperative partner selection protocol based on min-max criteria is introduced. We consider the scenario that all the nodes try to transmit packets to the same access point (AP) in a round-robin manner, and the AP tries to select a cooperative partner for each node utilizing the geographical information of nodes. Numerical simulation results show that the proposed selection relaying scheme can efficiently minimize the outage probability by properly choosing the dominant transmission mode. And our partner selection protocol based on the min-max criteria outperforms both the random partner selection strategy and the fixed non-cooperative strategy in terms of outage probability while ensuring fairness among users.

The adaptive subcarrier and bit allocation in OFDMA systems supporting multiple services is investigated. A suboptimal algorithm is developed, which aims to maximize the system throughput under the overall transmit power constraint while guaranteeing the QoS requirement of realtime users and supporting proportional fairness among non-realtime users. Firstly, the algorithm allocates subcarriers and power to realtime users so that the required power for them is minimized and the average power per subcarrier of realtime users is approximately equal to that of non-realtime users. Then, the remaining subcarriers and power are allocated to non- realtime users by a proposed rate adaptive resource allocation algorithm, which achieves a suboptimal overall throughput while providing proportional fairness among users. Simulation results show that the proposed algorithm achieves better performances than existing algorithms.

Cooperative diversity is an effective technique to combat multipath fading. When this technique is applied to ad hoc networks, it is a key issue to design distributed and power-efficient relay selection strategies. In this paper, we propose the power-aware relay selection (PARS) strategies to maximize the network lifetime. The PARS strategies are composed of the optimal power allocation(OPA) and three power-aware selection criteria. The OPA aims at minimizing the overall transmit power, while the three criteria select the relay that best help extend the network lifetime, based on both the OPA results and the residual power levels of the nodes. We combine our strategies with the opportunistic relay model, which is based on a 802.11b-like media access control (MRC) protocol, so the selection process can be done in a totally distributed way. Simulation results show that the proposed strategies have great improvement in both network lifetime and power efficiency

The resource allocation algorithm to minimize the overall required transmit power while satisfying the QoS requirements of real-time services in OFDMA systems is discussed in this paper. We develop the concept of marginal utility for each subcarrier, which corresponds to the maximal power reduction when the very subcarrier is allocated to a user. We also propose a low computational complexity algorithm to calculate the marginal utility. Based on this, a novel subcarrier and bit allocation algorithm is presented. In order to achieve the minimization of overall required power, the subcarrier with the largest marginal utility is assigned to its corresponding user at each subcarrier allocation iteration. Part of the bits required by the user are then redistributed to the newly assigned subcarrier by the proposed marginal utility calculation algorithm. Simulation results show that the proposed algorithm can achieve a better performance than Zhang's algorithm [6] at approximately the same computatotional complexity.

A novel subcarrier and bit allocation algorithm for multiuser OFDM system is presented in this paper. The algorithm aims to achieve a maximum transmit rate subject to the overall transmit power constraint and user rate proportionality constraint. The algorithm consists of two steps. In the subcarrier allocation step, the power is assumed to be uniformly allocated on each subcarrier and the subcarriers are allocated to users under the proportional rate constraint. Then, at each bit assignment iteration of the bit allocation step, the algorithm assigns one bit to the user with the least normalized transmit rate and the user greedily allocates the bit and corresponding power to the subcarrier which requires the least additional power to carry one more bit. The computational complexity of the proposed algorithm is analyzed, as well as the fairness performance. Simulation results show that the algorithm can achieve a better performance than the existing algorithms