Affine frequency division multiplexing (AFDM), an emerging multi-carrier modulation scheme, has garnered significant attention due to its resilience to Doppler shifts and capability to achieve full diversity in doubly dispersive channels. However, existing data detection algorithms for AFDM systems face a significant trade-off between computational complexity and accuracy. In this paper, a novel low-complexity data detection scheme, termed the soft-feedback detector (SFD), is proposed. Particularly, building upon a maximum ratio combining (MRC) estimator framework, the SFD leverages the a priori symbol distribution to mitigate error propagation during iterative detection. Specifically, soft-decision feedback is incorporated as extrinsic information derived from the log-likelihood ratios of the transmitted symbols. As a result, the proposed detector significantly enhances detection accuracy while maintaining low computational complexity. Simulation results demonstrate that the SFD consistently outperforms benchmark decision-feedback detectors. In particular, compared with the conventional MRC detector, the proposed scheme achieves approximately a 3 dB signal-to-noise ratio (SNR) gain at the bit error rate (BER) of $10^{-3}$.

Cooperative device-free localization, which involves node cooperation for location-awareness services, represents pivotal opportunities for forthcoming integrated sensing and communication (ISAC) networks. In this paper, a theoretical framework is established to quantify the performance limits of device-free localization accuracy in ISAC networks. It is achieved through Fisher information analysis, accounting for signal randomness, transceiver deployment, multi-target, multipath, etc. To explore node cooperation, the theoretically optimal linear fusion, inspired by the best linear unbiased estimator, is derived. It linearly combines independent target location estimates from multi-static transceivers for localization accuracy enhancement. The optimal weights are determined by the corresponding effective Fisher information, which later constitutes the definition of theFisher Information Field(FIF), as a spatial field to quantify useful information that can be gained through location estimation. An FIF-based practical location estimator using multi-source estimates is further developed. Finally, a case study on an orthogonal frequency division multiplexing ISAC network is conducted. The target is independently localized from multi-static transceivers using joint range-angle maximum likelihood estimation; closed-form evaluations of the Fisher information are derived for performance limits study and FIF-based cooperative localization. Numerical results offer insights into the Fisher information analysis and demonstrate low complexity and high accuracy of the FIF-based estimator compared with benchmarks.

Delay guarantee is essential for wireless communication technology, which is beneficial for real-time data processing. Such guarantee manifests as hard delay constraint, whose resolution facilitates reliable task fulfillment within strict deadline while enabling efficient communication resource scheduling. In this paper, we investigate the problem of transmitting a certain amount of data within a deadline and optimizing the expected total energy in an Orthogonal Frequency Division Multiplexing (OFDM) system. We represent the problem as a finite-horizon stochastic dynamic optimization problem, and aim to derive decision rule for allocating communication transmission rates across subcarriers. We propose a method named Multicarrier Deterministic Approximation Method (MDA). First, to address the complexity of expectation computation, we approximate the expected value function. Subsequently, for the resulting deterministic optimization problem, we introduce auxiliary variable, and adopt low-complexity two-layer optimization framework. For the proposed method, we derive performance upper bound for deterministic parameter with specific value and asymptotic performance upper bound for deterministic parameter with general value. The performance bound theoretically demonstrates the superiority of the proposed method over both the equal rate method and the first slot method. Furthermore, it proves that increasing the deterministic parameter under relaxed delay constraint can achieve enhanced theoretical performance guarantee. Finally, the effectiveness of the proposed method is validated through simulations.

Cell-free massive multiple-input multiple-output (CF-mMIMO) systems provide enhanced coverage and capacity for next-generation wireless networks. However, CF-mMIMO systems face significant challenges in downlink power allocation (PA) due to imperfect channel state information (CSI), severe multi-user interference (MUI), and high computational complexity. To address these issues, rate-splitting multiple access (RSMA) is adopted as a robust interference management strategy. Accordingly, this paper proposes an unsupervised and scalable graph neural network (GNN) framework for PA in rate-splitting CF-mMIMO (RS-CF-mMIMO) systems, relying exclusively on large-scale fading (LSF) coefficients without instantaneous CSI. To resolve the dimensionality mismatch in dynamic networks, we introduce a slice-based adaptive layer that projects variable-dimension features into a fixed latent space. This mechanism enables a unified model to generalize across diverse topologies without retraining. Within this architecture, the sum spectral efficiency (SE) is maximized under per-AP power constraints, assuming maximum-ratio precoding for common streams and regularized zero-forcing precoding for private streams. We also derive a weighted minimum mean-square error-alternating direction method of multipliers (WMMSE-ADMM) algorithm as a performance upper bound. Extensive simulations verify that the proposed GNN framework achieves near-optimal SE and outperforms unsupervised deep neural networks (DNNs) across diverse system sizes and pilot assignment schemes. Furthermore, the scalable variant maintains robust performance while reducing the trainable parameter count by over 57% relative to DNNs and decreasing inference latency by up to three orders of magnitude compared with WMMSE-ADMM.

Integrated sensing and communication (ISAC) has garnered significant attention in recent years. In this paper, we delve into the topic of sensing-assisted communication within ISAC systems. More specifically, a novel sensing-assisted channel estimation scheme is proposed for bistatic orthogonal-frequency-division-multiplexing (OFDM) ISAC systems. A framework of sensing-assisted channel estimator is first developed, integrating a tailored low-complexity sensing algorithm to facilitate real-time channel estimation and decoding. To address the potential sensing errors caused by low-complexity sensing algorithms, a sensing-assisted linear minimum mean square error (LMMSE) estimation algorithm is then developed. This algorithm incorporates tolerance factors designed to account for deviations between estimated and true channel parameters, enabling the construction of robust correlation matrices for LMMSE estimation. Additionally, we establish a systematic mechanism for determining these tolerance factors. A comprehensive analysis of the normalized mean square error (NMSE) performance and computational complexity is finally conducted, providing valuable insights into the selection of the estimator’s parameters. The effectiveness of our proposed scheme is validated by extensive simulations. Compared to existing methods, our proposed scheme demonstrates superior performance, particularly in high signal-to-noise ratio (SNR) regions or with large bandwidths, while maintaining low computational complexity.

Integrated sensing and communications (ISAC) has attracted great attention in recent years. Thanks to the large available bandwidth and large antenna array, terahertz (THz) ISAC has the potential to achieve high range and angle resolution for wireless sensing. Considering the cost and complexity of implementing THz radio frequency (RF) chains, analog beamforming with a single RF chain is a more practical choice. However, due to the extremely narrow THz beamwidth, the time-division sweeping for angle estimation may lead to unacceptable overhead. Therefore, how to achieve efficient angle estimation under a single RF chain is a challenging problem. In this paper, we leverage the ThzPrism architecture to enhance the angle coverage, which can spread the beams at different subcarriers in different directions. We show that the angle and range estimations are coupled under the ThzPrism architecture. To this end, a joint angle and range estimation problem is formulated. By utilizing the sparse feature of target distribution, a multi-measurement vector (MMV) compressed sensing algorithm is proposed to resolve this problem. Moreover, to reduce the complexity of the algorithm, we leverage the beam-spreading feature of ThzPrism to partition the whole angle coverage area into many small subareas, which can effectively reduce the dimension of the original compressed sensing problem. The effectiveness of our proposed scheme is validated by simulation results, which show a better performance of both angle and range estimations than the existing scheme.

Integrated sensing and communications (ISAC) is considered a promising technology in the B5G/6G networks. The channel model is essential for an ISAC system to evaluate the communication and sensing performance. Most existing channel modeling studies focus on the monostatic ISAC channel. In this paper, the channel modeling framework for bistatic ISAC is considered. The proposed channel modeling scheme extends the current 3GPP channel modeling framework and ensures the compatibility with the communication channel model. To support the bistatic sensing function, several key features for sensing are added. First, more clusters with weaker power are generated and retained to characterize the potential sensing targets. Second, the target model can be either deterministic or statistical, based on different sensing scenarios. Furthermore, for the statistical case, rays are generated considering spatial coherence for different reflection models. The effectiveness of the proposed bistatic ISAC channel model is validated by both Ray-tracing simulations and experiment studies. The compatibility with the 3GPP communication channel model is also demonstrated.

Terahertz (THz) communications in 6G offer high data rates and large bandwidths, but the effective range is limited by atmospheric absorption and scattering. Beamformer forms narrow beams to improve directional gain, but beamforming alone has limited accuracy, resulting in degraded communication quality, so beam alignment is necessary. Traditional linear scanning methods for beam alignment suffer from low accuracy due to restrictions on the beam sweeping step size. This problem can be mitigated in an integrated sensing and communication (ISAC) system that has strong built-in sensing ability to localize the target. To this end, this paper builds theoretical foundation on discussion of the relationship between estimation accuracy and beam misalignment displacement, and proposes an iterative sensing-assisted beam alignment method that iteratively aligns the beam directly to the target location estimated from the sensing system for continuous refinement of beam orientation. To support the proposed method, an ISAC hybrid bandwidth-time resource allocation scheme between communication and sensing sub-systems is designed. Numerical results validate that if allocating sufficient bandwidth-time resources to the sensing sub-system, the proposed method improves the system's capacity by 18.6% and 142.5% compared with traditional beam alignment and beamforming-only approaches, respectively.

Recently, integrated sensing and communications (ISAC) design has attracted great attention for B5G/6G networks. Existing ISAC studies are mainly focused on monostatic sensing with a full-duplex radio. However, full-duplex radio requires complicated self-interference cancellations and many current devices are half-duplex radios. Therefore, it is still interesting to investigate passive bistatic sensing with half-duplex radios. In this paper, integrating passive bistatic sensing into mmWave B5G/6G networks is investigated. The public reference signals are leveraged to extract the frequency-domain channel state information (CSI) for passive sensing. To address the problems of sampling-timing-offset and random phase error, a line-of-sight (LoS) path aided calibration mechanism is first developed. To achieve accurate localization for multiple targets, a novel super-resolution channel impulse response (CIR) based mechanism is then developed. The super-resolution CIR is achieved from CSI through a joint design of spatial-smoothing multiple signal classification (MUSIC) algorithm and template-based tap estimation. With the super-resolution CIR, multiple targets can be first distinguished in CIR taps and then localized by further estimating their angle of arrivals (AoAs). The proposed design is implemented and validated on a prototype system at 28 GHz with 500 MHz bandwidth in indoor environments. Experimental results show that our design can achieve accurate single-target and multi-target localization and tracking. The localization error is 26 cm at 80thpercentile for single-person and 29 cm on average for multi-person setups. The average error to the planned path after the moving-averaged filter is 11 cm and 19 cm for single-person and two-person tracking, respectively.

The integrated sensing and communications (ISAC) design has attracted great attention in recent years. Existing studies mainly focus on the monostatic radar sensing based on the assumption of full-duplex radio. However, since current legacy devices are all half-duplex radios, it is still important to investigate bistatic radar sensing based on half-duplex radios. In this paper, the 5G mmWave communication system with a hybrid beamforming architecture is considered, and the joint transmit and receive beamforming is designed for incorporating bistatic radar sensing function into the multi-user multi-input multi-output (MU-MIMO) communications. To overcome the critical problem of carrier-frequency-offset (CFO) and sampling-time-offset (STO) in bistatic sensing, the line-of-sight (LoS) signal is regarded as a virtual target for eliminating the CFO and STO. Therefore, the transmitter and radar receiver beamformers are jointly designed to maximize the radar signal-to-interference-plus-noise ratio (SINR), while satisfying the SINR constraints of both communication users and the virtual radar target. A hierarchical solution algorithm is developed to resolve this problem, in which a combined fully digital beamformer is first optimized for transmitter/sensing-receiver, and then the analog and digital beamformers for hybrid beamforming are derived from the fully digital beamformer. Simulation results show that compared to the separate beamforming design and the passive opportunistic sensing design, our joint design can achieve more than 5 dB and 7-22 dB radar sensing gain, respectively.

In recent years, full-duplex communications in a single frequency channel have become a practical technology. However, existing full-duplex medium access control (MAC) protocols can hardly take full advantages of full-duplex transmission opportunities, because they are not capable of adding one more half-duplex link on an on-going half-duplex link to form a full-duplex link. Besides, the exposed node and hidden node issues in multi-hop wireless networks limit flexible establishment of full-duplex links. Such limitations waste full-duplex transmission opportunities, and thus degrade network performance. In this paper, an efficient full-duplex link establishment protocol called full-duplex opportunity exploitation (FDOE) is developed to exploit full-duplex transmission opportunities in a wireless mesh network. FDOE leverages rateless coding and pseudo-noise (PN) sequences to establish a full-duplex link whenever a full-duplex transmission opportunity is available. Such a distinct feature leads to significant improvement of network performance. FDOE is evaluated through theoretical analysis and extensive simulations. Performance results show that FDOE exploits full-duplex transmission opportunities efficiently in wireless mesh networks, and thus significantly outperforms existing MAC protocols.

Carrier-free pulse-based waveforms are desired in terahertz (THz) communications since pulse-based systems have simpler transceiver architectures than carrier-based systems. For such a pulse-based THz communication system, there still lacks pulse waveforms that can support high-order modulation and flexible multiple access. In this paper, a pulse-based waveform with continuously tunable center frequencies and bandwidths is designed for carrier-free THz communications. More specifically, a Gaussian pulse is utilized as the basic pulse, and then the weighted sum of its high-order derivatives is used to generate waveforms with tunable frequencies and bandwidths according to the probability density function of Rice distribution. Such a pulse-based waveform is called frequency and bandwidth continuously tunable (FBCT) pulse. By making the derivative orders of all weighted terms odd or even, a pair of orthogonal FBCT pulses can be generated. Based on the pair of orthogonal FBCT pulses, a basic pulse-based$M$-ary quadrature amplitude modulation ($\text{P}M$QAM) scheme can be readily achieved. Moreover, with frequency and bandwidth tunability, FBCT pulse can support pulse division multiple access (PDMA) with tunable bandwidth, through which frequencies with high molecular absorption loss can be avoided. Numerical results demonstrate that FBCT pulse is significantly effective and flexible in supporting carrier-free THz communications.

Backscatter communication is visioned as one of the promising technologies for future ultra-low power Internet of Things (IoT). The orthogonal-frequency-division-multiplexing physical-layer (OFDM-PHY) WiFi backscatter communications attract great attention in recent years. However, the severe tag transmission collisions highly degrade the system performance in a backscatter network, since complicated multiple access mechanisms, e.g., carrier sense multiple access, cannot be applied on backscatter tags. To address this problem, a novel parallel decoding design called FreeCollision is developed to enable concurrent OFDM-PHY WiFi backscatter communications. Without the prior knowledge of the number of collided tags, their channel state information, and modulation types, FreeCollsion can just use the I-Q symbols to resolve the collision by the design of a series of mechanisms: collided constellation recovery, concurrent virtual channel estimation, QPSK tag detection, and parallel demodulation. Simulation results verify the effectiveness of our proposed scheme. The successful decoding rate is more than 95% for 4 collided tags with BPSK modulation. The maximum successful transmission probability can be improved from 36.7% to 88% for slotted Aloha.

Carrier-free pulse-based waveforms are desired in terahertz (THz) communications, since pulse-based systems have simpler transceiver architectures than carrier-based systems. Nowadays, Gaussian pulses and higher time order derivative (HTOD) Gaussian pulses are commonly used in pulse-based communications. However, existing Gaussian pulses span a large consecutive spectrum, so the communication distance is constrained by those frequencies with high molecular absorption loss. HTOD Gaussian pulses can avoid the frequency band of molecular absorption peak by tuning its center frequency, but the center frequency cannot be adjusted continuously. To resolve these issues, a pulse-based waveform with continuously tunable center frequencies and bandwidths is designed for carrier-free THz communications. More specifically, a Gaussian pulse is utilized as the basic pulse, and then the weighted sum of its high-order derivatives is used to generate waveforms with tunable frequencies and bandwidths according to the probability density function of Rice distribution. In this paper, such a pulse-based waveform is called frequency and bandwidth continuously tunable (FBCT) pulse. Moreover, with frequency and bandwidth tunability, FBCT pulse can support pulse division multiple access (PDMA) with tunable bandwidth, through which frequencies with high molecular absorption loss can be avoided. The basic mechanisms of multiple access based on FBCT pulse are analyzed. Numerical results demonstrate that FBCT pulse is significantly effective and flexible in supporting carrier-free THz communications.

Intelligent vehicles require both communications and radar sensing. Recently, the development of joint communication and automotive radar sensing has attracted great attention. In this paper, the reference signal (RS) in the orthogonal frequency division multiplexing (OFDM) waveform is leveraged for radar sensing in 5G vehicle-to-everything (V2X) communications. Unlike the existing studies that use the whole subcarriers as pilot subcarriers for radar sensing, the scattered RS pattern adopted in the 5G system is considered in our design. More specifically, the scattered RS placement and power allocation between RS and data are optimized to minimize the total transmission power for energy-efficient joint signal transmission, while satisfying both the communication and radar sensing requirements. The optimization problem is a mixed-integer non-linear programming (MINLP) problem. The optimal solution is solved by enumerating all possible RS placements with the optimal allocated power by solving a transformed convex optimization problem. Such an approach suffers a high computing complexity when there are a large number of possible RS placements. Therefore, an alternative heuristic approach is further developed to resolve the problem via successive convex approximation (SCA) method. Simulation results show that the heuristic method can well approach the optimal solution. Compared with existing studies, our proposed scheme can effectively improve energy efficiency.

It remains a challenging issue to localize blocked RFID objects. Existing solutions rely on non-line-of-sight (NLOS) signals reflected from environments, which are not reliable and cannot be accurately measured. To eliminate such limitations, this paper develops a new approach (called MetaSight) that localizes blocked objects by modulating NLOS signals via metasurfaces. More specifically, a programmable metasurface is designed such that each reflecting element can dynamically change frequencies and phases of reflected signals. With proper setting of frequencies and phases within a certain range of reflecting elements (i.e., the reflecting window) by a reflection beamforming algorithm, a unique NLOS signal path (called metasurface path or MS path) is established between an object and its reader. By shifting the reflecting window in time domain, multiple MS paths and the corresponding channel coefficients can be obtained sequentially. To make the sequential process fast, both the preamble and the payload of a frame are utilized for channel estimation. Based on the estimated channel coefficients of multiple MS paths, the 3D direction of the object viewed from the metasurface is derived through a 3D direction estimation algorithm. By consolidating the 3D directions from two separate metasurfaces, the object's 3D location is finally determined. MetaSight is implemented as a prototype system. Extensive experiments show that MetaSight can localize a blocked object with an average accuracy of 15 cm.

Localizing RFID-tagged objects by a mobile robot plays an important role in many Internet of Things (IoT) applications. Existing RFID localization systems are infeasible, since they either demand bulky RFID infrastructures or cannot achieve sufficient localization accuracy. In this article, a portable localization (POLO) system is developed for a mobile robot to locate RFID-tagged objects. POLO consists of an RFID reader, a tag array, and a lightweight receiver. The reader is used for interrogating the RFID tag on an object. The tag array is designed to reflect the RFID signal from an object into multipath signals. The receiver captures such signals and estimates their multipath channel coefficients by a tag-array-assisted channel estimation (TCE) mechanism. Such channel coefficients are further exploited to determine the object’s direction by a spatial smoothing direction estimation (SSDE) algorithm. To resist the impact of multipath reflections from surroundings, more spatial information is exploited by placing the tag elements densely and collecting the channel coefficients during the robot’s movement. Based on the object’s direction, POLO guides the robot to approach the object. When the object is in proximity, its 3-D location is finally determined by a near-range positioning (NRP) algorithm. Moreover, POLO is designed to be compatible with commercial RFID systems. POLO is prototyped and evaluated via extensive experiments. Results show that the average angular error is within 1 degree when the object is in the far range (2–6 m), and the average location error is within 6 cm while the object is in the near range (~1 m).

Considering network slicing in a cellular network, one of the most intriguing tasks is slice enforcement over air interfaces across multiple cells. The challenges lie in several aspects. First, resources allocated to different slices must achieve soft isolation at the link level. Second, users’ diverse QoS requirements must be satisfied even when communication links experience fading and interference. Third, long-term slicing policies must be conformed, no matter how unbalanced they are. To address these challenges, link-level slice enforcement is first formulated as a resource allocation problem that minimizes radio resource consumption while ensuring link-level soft slice isolation, guaranteeing users’ diverse QoS requirements, and conforming to slicing policies. Next, this problem is tackled via a deep reinforcement learning (DRL) based approach, through which LinkSlice is designed as an iterative two-stage algorithm. The first stage determines transmission rates for each link based on DRL. It is embedded with a graph neural network (GNN) to characterize link interference. Based on the transmission rates from the first stage, the second stage allocates resources to each slice. Performance results show that LinkSlice converges quickly to a near-optimal solution. It gracefully tackles the three challenges of link-level slice enforcement while further improving throughput by 18.5%.

Coded caching has become a hot research topic in recent years. However, existing studies rarely consider the delay constrained transmissions over wireless fading channels. For strict delay constrained content delivery over wireless fading channels, power optimization is usually required to achieve energy-efficient transmissions. Moreover, in coded caching, since the data to different users is transmitted via many coded multicast transmissions, the scheduling of these transmissions over fading channels is also critical for minimizing energy consumption. To this end, a joint scheduling and power optimization problem is formulated to minimize the expected energy consumption over wireless fading channels under strict delay constraints. Causal channel state information (CSI) is considered in this paper for practical cases, which nonetheless makes the optimal solution to this problem hard to achieve due to the uncertain future channel states. Therefore, a heuristic approach is developed to solve this problem. The number of slots for each coded multicast transmission is first determined by obtaining the optimal result of a nonlinear integer programming (NLIP) problem based on statistical channel estimations. Next, a closed-form inverse-waterfilling algorithm is carried out to allocate power for the coded transmissions under delay constraints, and then the one that saves the maximum energy consumption is scheduled for transmission. The heuristic approach is proved to achieve a result that is upper-bounded by a parameter times the optimal result of the original problem under non-causal CSI. Simulation results further show that compared to the benchmark with only slot allocation for each coded transmission, our approach with joint scheduling and power optimization can significantly save energy consumption while ensuring strict delay constraints.

In many Internet-of-Things (IoT) applications, various RFID-tagged objects need to be localized by mobile robots. Existing RFID localization systems are infeasible, since they either demand bulky RFID infrastructures or cannot achieve sufficient localization accuracy. In this paper, a portable localization (POLO) system is developed for a mobile robot to locate RFID-tagged objects. Besides a single RFID reader on board, POLO is distinguished with a tag array and a lightweight receiver. The tag array is designed to reflect the RFID signal from an object into multi-path signals. The receiver captures such signals and estimates their multi-path channel coefficients by a tag-array-assisted channel estimation (TCE) mechanism. Such channel coefficients are further exploited to determine the object's direction by a spatial smoothing direction estimation (SSDE) algorithm. Based on the object's direction, POLO guides the robot to approach the object. When the object is in proximity, its 2D location is finally determined by a near-range positioning (NRP) algorithm. POLO is prototyped and evaluated via extensive experiments. Results show that the average angular error is within 1.6 degrees when the object is in the far-range (2~6 m), and the average location error is within 5 cm while the object is in the near-range (~1 m).

Coded caching can achieve significant caching gain by leveraging coded multicast for content delivery, which attracts great attentions in recent years. However, the coded multicast delivery design for decentralized coded caching with heterogeneous cache sizes and link rates is still an open problem. To this end, joint-cache-channel (JCC) coding is exploited in this paper. More specifically, a novel coded multicast delivery scheme that explores JCC coding is developed for the practical case with a finite number of packets per file. Instead of directly constructing the conflict graph at each packet level in existing studies, a packet-merging mechanism and a color-merging mechanism are developed to achieve the conflict graph in packet cluster level for coded multicast delivery so as to harvest the performance gain of JCC coding. The information-theoretic analysis of the transmission time via JCC coding is further conducted in the asymptotic regime when the number of packets per file approaches infinity, which provides the theoretical coded caching gain by JCC coding. Simulation results further show that compared to the existing method, the transmission time can be effectively reduced by our proposed scheme.

How to achieve efficient multiple access for Terahertz (THz) networks is still an open problem. The key obstacle is the extremely narrow angular coverage by a highly directional THz beam. Thus, a hybrid THzPrism beamforming (HTB) architecture is designed in this paper to greatly enlarge angular coverage by spreading frequency subcarriers to different directions. Based on the HTB architecture, a spatial-spread orthogonal frequency division multiple access (SS-OFDMA) scheme is developed. To serve users dispersed in a large angular range, a user grouping mechanism is first designed for SS-OFDMA to utilize SDMA and suppress the inter-group interference. To improve the spectrum and energy efficiency for sporadic users within a group, a non-uniform beam spreading mechanism is then developed for SS-OFDMA by joint design of digital and analog beamforming. Finally, a resource allocation algorithm is designed to minimize the transmit power of the base station by optimizing the subarray allocation among groups as well as the subcarrier and power allocation for each user within a group. Compared with the existing schemes, SS-OFDMA increases the achievable data rate by up to 124%, reduces the power consumption by up to 71%, and increases the average number of concurrently served users by up to 147%.

The IEEE 802.11ad based joint communication and radar sensing has attracted great attentions for vehicles in recent years. The existing studies all assume full duplex communications between the transmitter and radar receiver based on perfect self-interference cancellation. However, the self-interference may not be fully cancelled due to the limitation of self-interference cancellation capability in practical cases, which will significantly degrade the sensing capability of the radar function, especially for the detection range. In this paper, the imperfect self-interference cancellation is considered and a novel joint communication and automotive long range radar sensing design is proposed based on OFDM frame structure in 802.11ad standard. The received signal model in the frequency domain synchronized with the self-interference is derived, in which the target reflection signal suffers inter-carrier-interference (ICI) and inter-symbol-interference (ISI). However, we show that the ISI can be leveraged for enhancing radar parameter estimation. Based on the received signal model, a novel pilot signal design is first developed to combat the self-interference for accurate velocity and coarse range estimation. Then, a few self-interference-free OFDM symbols at the end of the data frame are utilized to achieve accurate range estimation. Simulation results show that the decimeter-per-second level velocity estimation and centimeter level range estimation can be achieved for up to 200-meter radar sensing.

Integrated access and backhaul (IAB) networking is envisioned as a key technology to support more flexible and dense deployment of base stations (BSs). However, existing directed acyclic graph (DAG) based IAB networking highly limits the flexibility and efficiency of link scheduling, due to the fixed parent-to-child relation between two adjacent IAB nodes. In this paper, a mesh-architecture based approach is developed to improve the efficiency of IAB networking. The key idea of the mesh architecture is to make the relationship between two adjacent IAB nodes configurable. Based on the mesh architecture, the two-stage scheduling scheme of IAB networks is revised. The typical scenarios where the mesh-architecture based IAB networking outperforms the DAG-based one are analyzed. Simulation results show that the mesh-architecture based IAB networking can effectively improve the throughput by 6.70%40.56% and substantially reduce the delay under various traffic loads, as compared to the DAG-based IAB networking.

Existing physical layer security schemes are inapplicable to legacy devices operating on long coherence-time channels, since they demand changes in the physical layer. To this end, a new physical-layer security scheme is developed to secure legacy clients. In this scheme, secret keys are generated by a client and transmitted to the access point (AP). To protect these keys, a separated device called secrecy protector (SP) transmits jamming signals to prevent eavesdroppers from overhearing the keys. The SP is equipped with multiple antennas, each of which transmits an independent jamming stream with a pseudo-preamble. Since the SP can share jamming signals with the AP secretly, the AP can use a certain analog network coding scheme to remove jamming signals and decode the keys. In contrast, eavesdroppers have no knowledge of jamming signals to decode the keys. However, the long coherence-time channels are vulnerable to eavesdroppers, as they can guess the channel coefficients in a brute force way and then remove the jamming signals. Thus, frequency diversity is exploited to enhance the security of the system. Moreover, the design of jamming mechanisms resists collusion among eavesdroppers. Therefore, secret keys can be secretly shared between the AP and a client. The developed scheme is implemented on a software-defined radio platform. Performance results demonstrate that it can effectively deliver secure communications without any changes to the physical layer of legacy clients.

IEEE 802.11ax has adopted orthogonal frequency division multiple access (OFDMA) to support multi-user (MU) transmissions. There exist two uplink MU OFDMA access methods in IEEE 802.11ax. The first one is uplink OFDMA random access (UORA) in which stations randomly select resource units (RUs) to send physical protocol data units (PPDUs), so its access efficiency is low. The second one is uplink OFDMA nonrandom access (UONRA) in which the access point (AP) schedules MU transmissions based on buffer status reports (BSR) from stations. However, stations usually rely on UORA to send BSRs, so the low efficiency of UORA can be a bottleneck of UONRA. Thus, UORA needs to be renovated to improve the performance of itself and UONRA. To this end, a multi-dimensional busy-tone arbitration (MBTA) mechanism is developed in this paper to reduce collisions among stations contending the same RU. Since there lacks an algorithm in IEEE 802.11ax to support coexistence of UORA and UONRA, a dynamic access-method selection (DAMS) algorithm is designed for the AP and stations to choose an optimal access method. Both MBTA and DAMS are analyzed rigorously and are further validated via simulations. The analytical and simulation results show that: 1) The MBTA dramatically improves the access efficiency of UORA; 2) DAMS always achieves a higher throughput than both UORA and UONRA.

The joint considering of caching and coded multicasting can significantly improve caching gain, which has become a promising approach to address the explosive growth of wireless traffic demand. In this paper, the design of caching placement and coded multicasting for wireless backhaul networks is explored under heterogeneous file preferences, where the file preference of each small base station (BS) is assumed to be aware at the macro BS. To address the heterogeneous file preferences, a group-based caching and cooperative coded multicasting scheme is developed in this paper. By utilizing the spectral clustering method, the small BSs are first clustered into different groups with similar file preferences. Moreover, a cooperative caching strategy with symmetric file division is designed for each group, where the suboptimal caching proportion of the most popular files is achieved by an approximation analysis. By utilizing the group-based caching structure, an efficient greedy-based two-level cooperative coded multicasting algorithm is then developed. The proposed algorithm not only utilizes the coded multicasting opportunities within each group, but also explores the cooperative coding opportunities among groups. The effectiveness of our proposed scheme is verified by simulation results, which show that our proposed scheme can significantly reduce the traffic load compared to the existing schemes.

In this paper, a multi-user concurrent beam training scheme, named AnalogMUSIC, is designed for mmWave systems with a single radio frequency (RF) chain. In AnalogMUSIC, a one-RF chain based multiple signal classification (MUSIC) algorithm is developed for accurate direction estimation, where the training beams are switched in a time-division manner. To pursue a short training time, the training beams are switched at rough directions predefined by a coarse- resolution codebook. We prove that the traditional MUSIC algorithm can be achieved in the frequency domain. For orthogonal frequency division multiple access (OFDMA) systems, the multi-user training signals can be decoupled in the frequency domain. Thus, AnalogMUSIC achieves the concurrent beam training for multiple users by allocating different subcarriers to different users. To further adapt the number of switched training beams to various channel conditions, two coarse-resolution codebooks and a beam mapping mechanism are designed. Extensive simulations show that AnalogMUSIC can effectively improve beam training accuracy and substantially reduce delay overhead, compared with the existing schemes.

RFID-based passive positioning is essential for many Internet-of-Things applications. However, existing RFID-based passive positioning systems either have low precision or require multiple antenna arrays, which are bulky and expensive. In this paper, a 3D passive positioning scheme is developed based on RFID tag arrays. One of the tags can harvest the energy of the surrounding electromagnetic waves to power the embedded orientation sensors (an accelerometer and a magnetometer) and then send the orientation information of an object to an RFID reader. When an object is equipped with such RFID tag arrays, its direction can be estimated by exploring the phases of the signals reflected by the tags and the orientation of the object. Furthermore, based on the triangulation principle, the 3D location of an object can be determined by using multiple RFID tags on the object and two antennas at the RFID reader. Thus, compared with existing schemes, the required number of RFID antennas for 3D passive positioning is greatly reduced. The proposed scheme is implemented based on a COTS RFID platform. The experimental results show that the 90-percentile accuracy of the proposed system is within 9 centimeters.

Visible light communication by light-emitting diodes (LEDs) has become one of the promising technologies to boost the capacity of mobile networks, due to the low cost and high energy efficiency of LED lamps and the vast unregulated visible light bandwidth. However, VLC is only suitable for downlink data transmissions from LED illumination infrastructures to mobile users. Moreover, due to the line-of-sight (LOS) transmission feature of visible light, the VLC downlink can be easily interrupted by blockage of obstacles or rotation of receivers, which leads to unreliable coverage of mobile users. To overcome the above two drawbacks of VLC, an integrated VLC and WiFi system is designed in this paper. In our proposed system, a 2.5 sublayer called link convergence (LC) layer between the IP layer and the data link layer is designed to integrate WiFi radio and VLC radio. The following key mechanisms are designed in the LC layer to support efficient communications of the integrated system: (a) VLC radio access; (b) VLC ARP table; (c) VLC selective ARQ; (d) VLC link maintenance; (e) handover between WiFi and VLC. Since the integration is above date link layer, no MAC driver or protocol modifications are needed for WiFi in our system. A prototype of our designed integrated system is implemented. Experiment results validate the feasibility and effectiveness of our proposed system.

Coded caching has emerged as a promising component of solutions to the exponential growth in network traffic. Previous approaches to network coding are all based on (simple) XOR coding, which is appropriate when links have the same rate. However, in typical wireless networks, different users experience different link rates. Thus, XOR coding is sub-optimal and cannot achieve full broadcast gain. In this paper, we consider the coded caching design for wireless networks with unequal link rates. More specifically, the backhaul networks of LTE-A or 5G system are considered, in which the link rates between the macroBS and microBSs are different. We leverage a new network coding scheme nested coded modulation (NCM) in the delivery phase and develop a novel file partition scheme for the placement phase based on unequal cache size allocation. This scheme adapts to unequal link rates for increasing broadcast gains. The achievable transmission time and the information-theoretic lower bound are derived; we show that transmission time of the NCM-based coded caching can achieve a constant gap to the lower bound. Moreover, the NCM-based coded caching can achieve significant performance improvement over the XOR-based coded caching; the example with normalized link rates from 1 to 6 achieves up to 250% throughput improvement. Numerical results also show that the NCM-based coded caching can well utilize the unequal link rates, which cannot be achieved by the XOR-based coded caching.

In this paper, a centralized coded caching scheme is developed for wireless backhaul networks with heterogeneous channel conditions, i.e., the wireless channels between microBSs and a macroBS have different link rates and packet loss rates. To address these heterogeneous channel conditions, a joint random linear network coding and nested coded modulation (RLNCNCM) encoding scheme is designed for the delivery phase. Based on RLNC-NCM encoding, broadcast opportunities can be fully utilized for each coded subfile transmission. To support RLNC-NCM transmission, unequal file partition and cache size allocation are conducted in the placement phase. It is shown that a microBS with weaker channel condition needs to be allocated with more cache size. The theoretical achievable transmission time of the RLNC-NCM coded caching scheme is also derived. Moreover, numerical results show that the coded caching scheme developed in this paper significantly reduces the transmission time as compared to the existing schemes.

Charging electric vehicles (EVs) at home is attractive to EV users. However, when the penetration level of EVs becomes high, a distribution grid suffers from problems such as under-voltage and transformer overloading. EV users also experience a fairness problem, i.e., the limited capacity is unfairly shared among EVs. To solve these problems, a physical fair-queueing framework is established for EV charging. In this framework, a distribution sub-grid is first mapped to a multi-server queueing system, and then a fluid-model based queueing scheme called physical multi-server generalized processor sharing (pMGPS) is designed. pMGPS ensures perfect fairness but cannot be used practically due to its nature of fluid model. To this end, a packetized scheme called physical start-time fair queueing (pSTFQ) is developed to schedule tasks of EV charging. The fairness performance of the pSTFQ scheduling scheme is characterized by the ratio of energy difference between pSTFQ and pMGPS. This critical performance metric is studied through theoretical analysis and is also evaluated via simulations. Performance results show that the pSTFQ scheduling scheme achieves an energy difference ratio of less than 4 percent in various scenarios without causing under-voltage and transformer overloading problems.

Recently, device-to-device (D2D) communication and full duplex communication are both considered key technologies for 5G networks. In this paper, two novel cooperative modes are developed for full duplex D2D communication underlaying cellular networks: the network MU-MIMO based mode (N-mode) and the sequential forwarding mode (S-mode). In the N-mode, two D2D users work as network MIMO to forward the data to cellular users and thus leverage the channel diversity. In the S-mode, the spatial distribution of D2D users and cellular users is explored to improve the transmission rate. Based on these modes, two D2D users share the downlink resources with two nearby cellular users simultaneously to achieve both proximity gain and reuse gain. Moreover, these modes are well suited for edge cellular users. To optimize the performance of the two modes, optimal power allocation is conducted by considering the influence of residual self-interference at full duplex radios and the requirements of the minimum transmission rate for both cellular users and D2D users. Simulation results show that, compared with the dedicated mode, the sum rate of cooperative modes in the perfect self-interference cancellation case can achieve 32% overall improvement and about 70% improvement in the scenario with non-edge D2D users and edge cellular users. With residual self-interference at a full duplex radio, the overall performance improvement is reduced to 15%. However, the cooperative modes can still achieve 40% improvement in the scenario with non-edge D2D users and edge cellular users.

Analog network coding (ANC) is effective in improving spectrum efficiency. To coordinate ANC among multiple nodes without relying on complicated scheduling algorithm and network optimization, a new random access MAC protocol, called ANC-ERA, is developed to dynamically form ANC-cooperation groups in an ad hoc network. ANC-ERA includes several key mechanisms to maintain high performance in medium access. First, network allocation vectors (NAV) of control frames are properly set to avoid over-blocking of channel access. Second, a channel occupation frame (COF) is added to protect vulnerable periods during the formation of ANC cooperation. Third, an ACK diversity mechanism is designed to reduce potentially high ACK loss probability in ANC-based wireless networks. Since forming an ANC cooperation relies on bi-directional traffic between the initiator and the cooperator, the throughput gain from ANC drops dramatically if bi-directional traffic is not available. To avoid this issue, the fourth key mechanism, called flow compensation, is designed to form different types of ANC cooperation among neighboring nodes of the initiator and the cooperator. Both theoretical analysis and simulations are conducted to evaluate ANC-ERA. Performance results show that ANC-ERA works effectively in ad hoc networks and significantly outperforms existing random access MAC protocols.

As full-duplex wireless communication evolves into a practical technique, it will be built into communication nodes in many application scenarios. However, it is difficult to do so for legacy communication nodes. Thus, full-duplex communication nodes will coexist with half-duplex communication nodes in the same application environment. In this paper, a wireless local area network with a full-duplex access point (AP) and half-duplex clients is studied, and a media access control (MAC) protocol called asymmetrical duplex (A-Duplex) is developed to support efficient coexistence between half-duplex clients and the full-duplex AP. A-Duplex explores packet-alignment-based capture effect to establish dual links between the AP and two different clients. In this way, the capability of a full-duplex AP can be utilized by half-duplex clients, which leads to much improved network throughput. Moreover, to ensure fairness of the MAC protocol, a virtual deficit round-robin algorithm is proposed for the AP to select appropriate half-duplex clients for dual-link setup. A-Duplex does not require any change in the physical layer of half-duplex clients; only an update of MAC driver is necessary. Thus, it is well suited for coexistence between half-duplex clients and a full-duplex AP. Both analysis and simulations are conducted to evaluate performance of A-Duplex. Results show that it improves the throughput by 48% and 188% and reduces the average packet delay by 26% and 22%, as compared to the IEEE 802.11 Distributed Coordination Function with and without RTS/CTS, respectively. Moreover, the throughput remains steady as the number of clients grows. A-Duplex also maintains a high level of fairness.

Full duplex wireless communications have become practical in recent years. However, there exist many communication nodes that only support half duplex communications. Thus, it is important to consider coexistence between full duplex and half duplex communications. In this paper, a wireless LAN with a full duplex AP and half duplex stations is studied, and a media access control(MAC) protocol is developed to ensure effective operation of such a wireless LAN. The MAC protocol explores capture effect to establish dual links between two different stations and the AP to improve network throughput. Furthermore, it does not need any change in the physical layer of half duplex stations; only an update of MAC driver is required. Thus, the MAC protocol is well suited for supporting legacy half duplex stations. Simulation results show that the new MAC protocol improve throughput by 39% and 180% as compared to the MAC protocol of 802.11 DCF with and without RTS/CTS, respectively. More interestingly, throughput performance of the new MAC protocol remains steady as the number of stations grows.