In this paper, we propose a hybrid precoding/combining framework for communication-centric integrated sensing and full-duplex (FD) communication operating at mmWave bands. The designed precoders and combiners enable multiuser (MU) FD communication while simultaneously supporting monostatic sensing in a frequency-selective setting. The joint design of precoders and combiners involves the mitigation of self-interference (SI) caused by simultaneous transmission and reception at the FD base station (BS). Additionally, MU interference needs to be handled by the precoder/combiner design. The resulting optimization problem involves non-convex constraints since hybrid analog/digital architectures utilize networks of phase shifters. To solve the proposed problem, we separate the optimization of each precoder/combiner, and design each one of them while fixing the others. The precoders at the FD BS are designed by reformulating the communication and sensing constraints as signal-to-leakage-plus-noise ratio (SLNR) maximization problems that consider SI and MU interference as leakage. Furthermore, we design the frequency-flat analog combiner such that the residual SI at the FD BS is minimized under communication and sensing gain constraints. Finally, we design an interference-aware digital combining stage that separates MU signals and target reflections. The communication performance and sensing results show that the proposed framework efficiently supports both functionalities simultaneously.

This paper introduces a novel hybrid analog/digital transceiver design for full-duplex (FD) integrated sensing and communication (ISAC) systems operating at mmWave band. The proposed scheme simultaneously supports downlink (DL) and uplink (UL) multiuser communication along with monostatic sensing, while suppressing the self-interference (SI). Considering that high SI levels may lead to saturation at low-noise amplifiers (LNAs), our design incorporates two SI mitigation constraints: one imposed at the receiver (RX) antennas before LNAs and another after the analog combining stage before analog-to-digital converters (ADCs). By formulating optimization problems that balance the trade-offs between spectral efficiency and beam-pattern error, we leverage a projected gradient ascent (PGA) algorithm with penalty-based methods to iteratively design hybrid precoders and combiners. Simulation results show that the proposed architecture strikes a balance between communication and sensing performance while effectively mitigating SI.

Knowledge understanding is a foundational part of envisioned 6G networks to advance network intelligence and AI-native network architectures. In this paradigm, information extraction plays a pivotal role in transforming fragmented telecom knowledge into well-structured formats, empowering diverse AI models to better understand network terminologies. This work proposes a novel language model-based information extraction technique, aiming to extract structured entities from the telecom context. The proposed telecom structured entity extraction (TeleSEE) technique applies a token-efficient representation method to predict entity types and attribute keys, aiming to save the number of output tokens and improve prediction accuracy. Meanwhile, TeleSEE involves a hierarchical parallel decoding method, improving the standard encoder-decoder architecture by integrating additional prompting and decoding strategies into entity extraction tasks. In addition, to better evaluate the performance of the proposed technique in the telecom domain, we further designed a dataset named 6GTech, including 2390 sentences and 23747 words from more than 100 6G-related technical publications. Finally, the experiment shows that the proposed TeleSEE method achieves higher accuracy than other baseline techniques, and also presents 5 to 9 times higher sample processing speed.

Network slicing plays a critical role in enabling multiple virtualized and independent network services to be created on top of a common physical network infrastructure. In this paper, we introduce a deep reinforcement learning (DRL)-based radio resource management (RRM) solution for radio access network (RAN) slicing under service-level agreement (SLA) guarantees. The objective of this solution is to minimize the SLA violation. Our method is designed with a two-level scheduling structure that works seamlessly under Open Radio Access Network (O-RAN) architecture. Specifically, at an upper level, a DRL-based inter-slice scheduler is working on a coarse time granularity to allocate resources to network slices. And at a lower level, an existing intra-slice scheduler such as proportional fair (PF) is working on a fine time granularity to allocate slice dedicated resources to slice users. This setting makes our solution O-RAN compliant and ready to be deployed as an ‘xApp’ on the RAN Intelligent Controller (RIC). For performance evaluation and proof of concept purposes, we develop two platforms, one industry-level simulator and one O-RAN compliant testbed; evaluation on both platforms demonstrates our solution’s superior performance over conventional methods.

We introduce two classes of measurement-based wireless schedulers. The Opportunistic Guaranteed Rate Scheduler (OGRS) meets a user’s delay constraints by opportunistically allocating the user the equivalent of a fixed service rate, which for a leaky-bucket constrained traffic ensures the delay requirements are met. By contrast, the Opportunistic Guaranteed Delay Schedulers (OGDS) schedules data transmissions when the current channel is better than what is expected in the time window before packet deadlines expire. Meeting such delay requirements requires a complementary admission control policy. We exhibit a simple measurement based policy, that indirectly accounts for heterogeneity in traffic, channel, and delay constraints by monitoring the statistics of user’s aggregate resource usage. We show that the spectral efficiency of our proposed approach is stochastically better than a wireless guaranteed rate scheduler. We bound spectral efficiency by considering an optimal offline policy with access to future channel rates and show via extensive simulations that OGRS can be within 10%-40% of the bound whereas OGDS is within 10% of the bound for a range of delay constraints. Additionally, we demonstrate that OGDS can exhibit better spectral efficiency at higher delay deadlines than schedulers leveraging neural network based predictions for future channel rates.

Human respiration rate (RR) estimation is essential for various health care applications, such as sleep apnea detection and chronic obstructive pulmonary disease early diagnose. Recently, radio frequency based RR estimation has achieved high accuracy for single-person RR detection. However, multi-person RR estimation is still the obstacle blocking the wide commercialization of RF sensing based RR solution. In this paper, a novel multi-person RR estimation algorithm that can overcome the frequency resolution limit is present. The proposed algorithm is not only analytically justified but also verified in a real test-bed involving commercial off-the-shelf WiFi devices. Extensive experiment results show a 98% accuracy in people-counting and a root mean square error (RMSE) of 0.13 breath per minute (bpm) on RR detection. To the best of our knowledge, this is the first WiFi sensing work that can detect different people who share the same RR by only using a single pair of transmit and receive antenna.

In this paper, we propose a novel indoor localization algorithm that exploits the angle and delay information of the sparse channel paths at mmWave. We consider that the user and the access point (AP) are not perfectly synchronized, which results in an unknown clock offset for the estimated delays. The proposed algorithm comprises two stages where the initial stage is to estimate the unknown clock offset and the locations of the users by leveraging the properties of the indoor environment. Then, the initial location estimates of users are collected and used to learn the virtual anchor locations. Finally, we propose a one-shot anchor-based localization algorithm that outperforms the initial one.

In the context of integrated sensing and communication (ISAC), a full-duplex (FD) transceiver can operate as a monostatic radar while maintaining communication capabilities. This paper investigates the design of precoders and combiners for a joint radar and communication (JRC) system at mmWave frequencies. The primary goal of the design is to guarantee certain performance in terms of some sensing and communication metrics while minimizing the self-interference (SI) caused by FD operation and taking into account the hardware limitations coming from a hybrid MIMO architecture. Specifically, we introduce a generalized eigenvalue-based precoder design that considers the downlink user rate, the radar gain, and the SI suppression. Since the hybrid analog/digital architecture degrades the SI mitigation capability of the precoder, we further enhance SI suppression with the analog combiner. Our numerical results demonstrate that the proposed architecture achieves the required radar gain and SI mitigation while incurring a small loss in downlink spectral efficiency. Additionally, the numerical experiments also show that the use of orthogonal frequency division multiplexing (OFDM) radar with the proposed beamforming architecture results in highly accurate range and velocity estimates for the detected targets.

Although Wi-Fi is an ideal technology for many ranging applications, the performance of current methods is limited by the system bandwidth, leading to low accuracy of ~1 m. For many applications, measuring differential range, viz., the change in the range between adjacent measurements, is sufficient. Correspondingly, this work proposes WiDRa - a Wi-Fi based Differential Ranging solution that provides differential range estimates by using the sum-carrier-phase information. The proposed method is not limited by system bandwidth and can track range changes even smaller than the carrier wavelength. The proposed method is first theoretically justified, while taking into consideration the various hardware impairments affecting Wi-Fi chips. In the process, methods to isolate the sum-carrier phase from the hardware impairments are proposed. Extensive simulation results show that WiDRa can achieve a differential range estimation root-mean-square-error (RMSE) of$\approx 1$mm in channels with a Rician-factor$\geq 7$(a$100 \times $improvement to existing methods). The proposed methods are also validated on off-the-shelf Wi-Fi hardware to demonstrate feasibility, where they achieve an RMSE of <1 mm in the differential range. Finally, limitations of current investigation and future directions of exploration are suggested, to further tap into the potential of WiDRa.

Reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) wireless systems offer robustness to blockage and enhanced coverage. In this paper, we develop an algorithmic solution that shows how RISs can also enhance the positioning performance in a joint localization and communication setting, even when hardware impairments are considered. We propose a realistic system architecture that considers the clock offset between the transmitter and the receiver, impairments at transmit and receive arrays, and mutual coupling between the RIS elements. We formulate the estimation of the composite channel in a RIS-aided mmWave system as a multidimensional orthogonal matching pursuit problem, which can be solved with high accuracy and low complexity, even when operating with large antenna arrays as required at mmWave. In addition, we introduce a dictionary learning stage to calibrate the hardware impairments at the user array. To complete our design, we devise a localization scheme that exploits the estimated composite channel while accounting for the clock offset between the transmitter and the receiver. Numerical results show how RIS-aided mmWave systems can significantly improve the localization accuracy in a realistic 3D indoor scenario simulated by ray tracing.

Due to its ubiquitous and contact-free nature, the use of WiFi infrastructure for performing sensing tasks has tremendous potential. However, the channel state information (CSI) measured by a WiFi receiver suffers from errors in both its gain and phase, which can significantly hinder sensing tasks. By analyzing these errors from different WiFi receivers, a mathematical model for these gain and phase errors is developed in this work. Based on these models, several theoretically justified preprocessing algorithms for correcting such errors at a receiver and, thus, obtaining clean CSI are presented. Simulation results show that at typical system parameters, the developed algorithms for cleaning CSI can reduce noise by 40% and 200%, respectively, compared to baseline methods for gain correction and phase correction, without significantly impacting computational cost. The superiority of the proposed methods is also validated in a real-world test bed for respiration rate monitoring (an example sensing task), where they improve the estimation signal-to-noise ratio by 20% compared to baseline methods.

Next generation wireless schedulers will support increasingly heterogeneous devices/applications in terms of their traffic characteristics and service requirements. Particularly challenging is the need to deliver traffic subject to delay and reliability constraints in a spectrally efficient manner. We propose a new measurement-based Opportunistic Guaranteed Deadline Scheduler (OGDS) that meets strict delay deadlines on users’ packets. This is achieved by scheduling packet transmissions when the current channel rate is better than that expected in the time window before packet deadlines expire. In order to meet such requirements one must have a complementary admission control policy. We exhibit a simple, once again measurement based policy, that indirectly accounts for heterogeneity in traffic, channel and delay constraints by monitoring statistics of OGDS’s resource usage. We show via extensive synthetic and trace driven simulations that OGDS requires at most 10−25% more resources compared to an optimal offline scheduling policy with complete knowledge of future channel rates, and performs much better than standard baselines including the state-of-the-art MLWDF scheduler. Finally, we propose a modification to OGDS that enables one to control the jitter at a possible loss in spectral efficiency.

Next generation wireless schedulers will support increasingly heterogeneous users/devices in terms of their traffic characteristics and service requirements. Particularly challenging is the need to deliver low latency traffic with strict deadlines in a spectrally efficient manner. We introduce a class of wireless schedulers, Opportunistic Guaranteed Rate (OGRS) that exploits the temporal variability in users' channel capacity with a view on maintaining delay guarantees. OGRS meets the user's delay constraints by opportunistically allocating the user the equivalent of a fixed service rate, which given a dual leaky bucket constraint on its traffic will ensure the delay requirements are met. We consider offline policies with access to future channel rates, which establishes a bound to the wireless spectral efficiency. We show via extensive simulations that OGRS can be within 10%-40 % of this bound for a range of delays that were considered. These gains translate to more than a two fold enhancement in eMBB users' throughput, when URLLC and eMBB traffic share resources. Finally, we propose a measurement based admission control strategy for latency constrained URLLC users, so that the network can guarantee QoS to all its users - existing as well as newly admitted ones.

Mobility load balancing (MLB) aims to solve the problem of uneven resource utilization in cellular networks. Since network dynamics are usually complicated and non-stationary, conventional model-based MLB methods fail to cover all scenarios of cellular networks. On the other hand, deep reinforcement learning (DRL) can provide a flexible framework to learn to distribute cell load evenly without explicit modeling of the underlying network dynamics. In this paper, we introduce a novel decentralized DRL-based MLB method where each cell has a DRL agent to learn its handover parameters and antenna tilt angle. As the number of cells increases, the decentralized framework is more computationally efficient than its centralized counterpart by dividing the action space. Furthermore, our designed decentralized DRL architecture only requires readily known information defined in existing cellular standards, and it can achieve a more balanced cell load distribution than the centralized DRL one by using individual reward functions. To provide realistic performance evaluation, a network simulator is introduced strictly following the Third Generation Partnership Project (3GPP) specifications. Furthermore, field data is used to construct the underlying cellular environment. Extensive evaluations have been conducted to demonstrate the fact that the introduced decentralized DRL-based MLB method can achieve a more balanced cell load distribution and a better performance of edge users than the state-of-the-art MLB methods.

Next-generation virtual radio access networks (vRAN) will benefit from the flexibility provided by virtualization in proposed Cloud-RAN configurations. These systems for 5G and beyond may consist of commodity hardware such as GPUs in data centers with multiple connected base stations (gNBs) flexibly receiving allocated resources depending on time-varying, real-time demands. In this paper, parallel reconfigurable algorithms and architectures for channel decoding are proposed. In particular, flexible rate and block length LDPC decoders for the new radio (NR) physical layer on GPU are characterized. We implement these GPU decoders using reduced word lengths of 8-bits to represent the log-likelihood ratios during decoding, and we utilize multiple GPU streams to process multiple blocks of codewords in parallel. These techniques allow our implementation to reduce the device transfer overhead and achieve the low-latency or high-throughput targets for 5G and beyond. Moreover, we integrate our decoder into the Open Air Interface (OAI) NR software stack to investigate virtualization capabilities when containerizing vRAN functionality such as the LDPC decoder.

Current studies that apply reinforcement learning (RL) to dynamic spectrum access (DSA) problems in wireless communications systems mainly focus on model-free RL (MFRL). However, in practice, MFRL requires a large number of samples to achieve good performance making it impractical in real-time applications such as DSA. Combining model-free and model-based RL can potentially reduce the sample complexity while achieving a similar level of performance as MFRL as long as the learned model is accurate enough. However, in a complex environment, the learned model is never perfect. In this article, we combine model-free and model-based RL, and introduce an algorithm that can work with an imperfectly learned model to accelerate the MFRL. Results show our algorithm achieves higher sample efficiency than the standard MFRL algorithm and the Dyna algorithm (a standard algorithm integrating model-based RL and MFRL) with much lower computation complexity than the Dyna algorithm. For the extreme case where the learned model is highly inaccurate, the Dyna algorithm performs even worse than the MFRL algorithm while our algorithm can still outperform the MFRL algorithm.

Beamforming in multiple input multiple output (MIMO) systems is one of the key technologies for modern wireless communication. Creating appropriate sector-specific broadcast beams are essential for enhancing the coverage of cellular network and for improving the broadcast operation for control signals. However, in order to maximize the coverage, patterns for broadcast beams need to be adapted based on the users' distribution and movement over time. In this work, we present self-tuning sectorization: a deep reinforcement learning framework to optimize MIMO broadcast beams autonomously and dynamically based on users' distribution in the network. Taking directly UE measurement results as input, deep reinforcement learning agent can track and predict the UE distribution pattern and come up with the best broadcast beams for each cell. Extensive simulation results show that the introduced framework can achieve the optimal coverage, and converge to the oracle solution for both single sector and multiple sectors environment, and for both periodic and Markov mobility patterns.

Spatial spectrum sensing (SSS) enables mobile devices to sense the spatial spectrum holes and reuse the scarce spectrum opportunistically. In this paper, we model and analyze the SSS in uplink two-tier user-centric deployed heterogeneous networks (HetNets) where secondary users (SUs) sense the spectrum holes of cellular users. In the two-tier user-centric deployed HetNets, small cell base stations (SBSs) are deployed in hotspots with high user density, and macro base stations (MBSs) are deployed uniformly. Based on the semi-static power control mechanism, the average transmit power of cellular users associated with MBS and SBS are derived, respectively. Furthermore, the spatial false alarm probability and the spatial miss detection probability of a typical SU are obtained, respectively. Moreover, we characterize the coverage probability and the area spectral efficiency (ASE) of SU and cellular networks. The SUs' optimal SSS radius is obtained to maximize the ASE of the entire network while guaranteeing the ASE of cellular networks above a certain threshold. Simulation results show that when the density of SUs is small, a decrease in SUs' SSS radius reduces the coverage probability of SUs. However, it improves the ASE of SUs networks, although the inter-SU interference increases.

In cognitive radio systems based on periodic sensing and transmission, a secondary user (SU) senses the activity of a primary user (PU) in the sensing phase and then makes use of the detected spectrum opportunity in the transmission phase. Under the constraint that the PU should be sufficiently protected, it is challenging for the SU to accurately sense and utilize spectrum opportunities in low signal to noise ratio (SNR) environments. However, in existing spectrum sensing schemes, the time-frequency resource blocks available for data transmissions are always wasted once a false alarm event happens in the sensing period where the SU is doing nothing but waiting for another sensing opportunity. By reutilizing these initially wasted time-frequency resource blocks for spectrum sensing, the SU can get more accurate information on the activities of the PU, which enables the SU to make more efficient use of the spectrum opportunities left by the PU. In this paper, a decision-driven time-adaptive spectrum sensing scheme is proposed to improve both spectral efficiency and energy efficiency based on improved resource usage. Specifically, if the PU is detected to be absent in the sensing phase of a frame, the SU transmits data in the transmission phase of the frame; otherwise, the SU will sense the spectrum for a duration of one frame period, where the frame period consists of the transmission phase of the current frame and the sensing phase of the next frame. When both signal and noise are Gaussian, the optimal maximum likelihood ratio detector is derived. Furthermore, the performance upper-bounds of spectrum utilization and secondary throughput are obtained. Finally, both simulation and theoretical results show that the developed scheme can improve spectrum utilization, secondary throughput and energy efficiency effectively in extremely low SNR environments.

Orthogonal frequency division multiplexing (OFDM) signals are widely employed in most wireless comm- unication systems, and the problem of spectrum sensing for OFDM signals has been extensively investigated. However, most of the available research reports on spectrum sensing for OFDM signals ignore the existence of pilots, which are usually inserted in the OFDM signals for synchronization, channel estimation and control information delivery. To sense weak OFDM signals with periodic pilots reliably in cognitive radio networks, we propose two likelihood ratio test (LRT) detectors for the scenarios with and without time synchronization information. In the ideal scenario that an unlicensed secondary user is perfectly synchronized with a licensed primary user, we propose a near-optimal LRT (NOLRT) detector, which constructs the test statistic by correlating the locally generated pilots with the received OFDM signal samples. Since the NOLRT detector relies on perfect time synchronization that is difficult to realize in practice, we further propose a more practical buffer-aided LRT (BALRT) detector that does not require time synchronization. To formulate the test statistic, the BALRT detector stores the averages of historical OFDM signal samples in a buffer and then correlates them with the currently received OFDM signal samples. Theoretical closed-form expressions for the probability of detection and probability of false alarm are derived for the proposed detectors. To alleviate the negative effects of noise uncertainty in low SNR scenarios, we also propose an effective method based on noise power estimation. Monte Carlo simulation results match with theoretical results very well and show that the NOLRT detector outperforms the counterparts significantly in additive white Gaussian noise (AWGN) channel with perfect time synchronization, while the BALRT detector outperforms the counterparts noticeably in multipath propagation environments without time synchronization.

This paper develops a novel framework for the modeling and analysis of spatial spectrum sensing (SSS) for device-to-device (D2D) communications in uplink two- tier user-centric deployed heterogeneous networks (HetNets), where small cell base stations (SBSs) are deployed in the places with high user density termed hotspots introduced by 3GPP. We study the average transmit power of uplink users, the probability of spatial false alarm and the probability of spatial miss detection of a typical D2D transmitter (D2D-Tx) during SSS. Based on the results, we further characterize the coverage probability of a typical D2D user and the area spectral efficiency (ASE) of D2D networks. Simulation results verify our analysis and demonstrate the advantages of SSS-based D2D communications in future wireless networks.

Spectrum access and interference management are amongst the most challenging issues in device-to-device (D2D) cellular networks. In order to address these issues, this paper introduces spatial spectrum sensing (SSS) for D2D cellular networks to facilitate cellular spectrum sharing by D2D users while providing a quality of service guarantee for cellular users. In order to assess the performance of the proposed scheme, we adopt a stochastic geometry approach in which the locations of base stations and D2D devices are modeled as independent Poisson point processes (PPPs). Assuming that the locations of the active cellular transmitters form another independent PPP, we characterize the area spectral efficiency of D2D networks under cellular users' outage probability constraint. The use of SSS prohibits D2D transmissions around the active cellular users because of which the locations of the active D2D transmitters are modeled as a Poisson hole process driven by the PPP of active cellular user locations. Our analysis carefully accounts for this spatial separation between active cellular users and active D2D devices. Extensive simulation and numerical results are presented to verify our analysis and demonstrate the advantages of SSS-based D2D cellular networks.

In a massive machine type communication (massive MTC) network, wireless connections between machine type devices (MTDs) and eNodeBs are unreliable due to the interference caused by uncoordinated access of other MTDs. Furthermore, it is with a high probability that the retransmission from the outage source MTD will fail again due to the spatiotemporally correlated interference. In this paper, we design and analyze a location-based cooperative strategy to improve the performance of massive MTC networks. In the cooperative strategy, an inactive MTD is selected as a relay if it has successfully decoded the packet and if it is located within a circular area around the eNodeB. Considering the spatial and temporal correlation of interference, the outage probability of the designed cooperative strategy is derived using stochastic geometry. Both the simulation and numerical results demonstrate that spatiotemporal correlation of interference significantly affects the performance analysis of cooperative massive MTC networks and our designed cooperative strategy can significantly reduce the outage probability compared to conventional retransmission.

Energy efficiency is one of the major challenges of 5G networks. As data rates are expected to increase by 1000x from 4G to 5G, energy efficiency will need to improve by about the same amount. Recently, energy harvesting techniques have attracted lots of attention from the scientific community, due to their ability to increase network lifetime. More specific, energy harvesting from ambient radio frequency (RF) signals is of special importance, especially with the recent RF circuit advancements. In this paper, we consider a device-to-device (D2D) communication in underlay cellular networks, where D2D users reuse the spectrum occupied by cellular users. We introduce the concept of spatial RF energy harvesting, where D2D users harvest RF power from uplink cellular transmissions, if it exceeds a predesigned threshold, in a spatial region. Using tools from stochastic geometry, we obtain a closed form expression for the probability of activating RF power conversion circuit by making full use of spatial locations of ambient RF signals. Subsequently, we study the impact of RF energy harvesting region radius to harvest sufficient power on the signal-to-interference (SIR) ratio of D2D network. Simulation results provide insights for the required advancements to design highly efficient RF harvesting circuits.

Ultra-densification is one of the main features of 5G networks. In an ultra-dense network, how to conduct interference management and spectrum allocation is a challenging issue. Spectrum sensing in cognitive radio networks is a distributed and efficient way to resolve this issue in ultra-dense networks. However, most of the studies on spectrum sensing only focus on sensing temporal spectrum opportunities where one or multiple primary users are active, which does not make full use of spectrum opportunities in the spatial location domain. To overcome the shortcomings of conventional temporal spectrum sensing, we study the problem of spatial spectrum sensing, which senses spatial spectrum opportunities in wireless networks. In this paper, the performance of spatial spectrum sensing and its application in sensing-based device-to-device (D2D) cellular networks are analyzed using stochastic geometry. Specifically, by modeling the locations of active transmitters as a Poisson point process, the spatial spectrum sensing problem is formulated using the framework of a detection theory. Closed-form expressions are obtained for the sensing threshold, probabilities of spatial detection, and false alarm. Furthermore, analytical throughput for D2D users and cellular users under both channel inversion and constant power allocation cases are derived. The optimal sensing radius that maximizes the defined network metric is obtained numerically. Finally, the simulation and numerical results are presented to verify our theoretical analysis.

In underlay cognitive radio networks (CRNs), secondary users (SUs) have to dynamically control their transmit powers so that the interference to primary users (PUs) is tolerable. Under this constraint, SUs' data link usually suffers from either high error rate with limited transmission range or long end-to-end delay caused by multi-hop transmissions. To improve the performance of SUs, cooperative routing using mutual-information accumulation is introduced in CRNs for the first time in this paper. To be specific, the routing and resource allocation problem in underlay CRNs is investigated and is factored into two sub-problems, each of which can be solved efficiently. Furthermore, a distributed algorithm is introduced and simulation results show that both the centralized and distributed algorithms can reduce upto 77% of the end-to-end delay compared to the traditional multi-hop delay-optimal routing in CRNs. Finally, theoretical analysis on the end-to-end delay for a one-dimensional (1-D) network in the low signal-to-interference ratio (SIR) region is conducted. Both the analytical and simulation results show that mutual-information accumulation can significantly decrease the delay of underlay CRNs, especially in the scenario where PUs have a tight interference power constraint.

To improve the spectral efficiency of a cognitive radio system, sensing based spectrum sharing (SBSS) technique combines the advantages of spectrum overlay and spectrum underlay. In this paper, we study the performance of SBSS under primary users' (PUs') rate loss constraint. To be specific, efficient algorithms are introduced to find the optimal sensing time and power allocation in both single-carrier and multi-carrier systems. Performance evaluation is conducted to compare the system throughput between SBSS and spectrum overlay techniques. Simulation results suggest that SBSS outperforms spectrum overlay when the transmitter of the secondary user (SUT) is far away from the receiver of the primary user (PUR) in the single-carrier case. In the multi-carrier case, SBSS can achieve 15.2% increase of spectral efficiency over spectrum overlay when all the channel gains are independent and identically distributed.