The increasing demand for high-performance wireless communication, due to emerging applications such as augmented reality, virtual reality, and Internet-of-Things (IoT), has highlighted the need for enhanced Peer-to-Peer (P2P) communication in Wi-Fi networks. P2P communication, often implemented through technologies like Wi-Fi Direct and Wi-Fi Aware, plays a crucial role in enabling seamless device-to-device interaction. This paper explores two significant enhancements for improving P2P communication: enhancing base-channel P2P through the optimization of TXOP sharing for P2P groups, and improving off-channel P2P through multi-AP coordination for channel advertisement. First, we examine the enhancement of base-channel P2P communication by introducing a refined transmission opportunity (TXOP) sharing mechanism, where an AP allocates portions of its TXOP to P2P devices within a group. This allocation ensures that devices can transmit data within a controlled, synchronized framework, thereby reducing contention and improving overall throughput. Furthermore, the proposed improvements enable devices to efficiently share resources based on group-level needs, supporting latency-sensitive applications such as real-time media streaming. Second, we address the challenges of off-channel P2P communication in OBSS (Overlapping Basic Service Set) environments, where interference from neighboring networks can severely affect performance. Through multi-AP coordination, APs can advertise recommended P2P channels that minimize overlap with infrastructure operations, thereby providing cleaner and more reliable channels for P2P communication. In addition, this coordination also facilitates faster setup and more efficient operation of P2P links.

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.

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.

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.

Current trends in spectrum regulation show that more and more unlicensed and shared spectrum bands are poised to be opened up for mobile communication. However, the question remains how to best utilize this spectrum and build efficient networks, and if the time has come for newer approaches to be considered for the next generation system. In this work, we propose a coordinated shared spectrum framework that can be considered for next generation cellular standardization. In designing the framework, we aim to improve on the current unlicensed access schemes toward increasing spectral efficiency in highly- dense networks. To this end, we demonstrate that with the proposed framework both throughput and access delay can be significantly improved over the state-of-the-art LAA system. We also show that large statistical multiplexing gains are possible through dynamic sharing instead of static, hard splitting of shared spectrum, as in the current CBRS system.

Analog beamforming is an attractive and cost-effective solution to exploit the benefits of massive multiple-input-multiple-output systems, requiring only one up/down-conversion chain. However, the presence of only one chain imposes a significant overhead in estimating the channel state information required for beamforming, when conventional digital channel estimation (CE) approaches are used. As an alternative, this paper proposes a novel CE technique, called periodic analog CE (PACE), which can be performed by analog hardware. By avoiding digital processing, the estimation overhead is significantly lowered and does not scale with the number of antennas. PACE involves the periodic transmission of a sinusoidal reference signal by the transmitter, the estimation of its amplitude and phase at each receive antenna via analog hardware, and using these estimates for beamforming. To enable such non-trivial operation, two reference tone recovery techniques and a novel receiver architecture for PACE are proposed and analyzed, both theoretically and via simulations. The results suggest that in sparse, wide-band channels and above a certain signal-to-noise ratio, PACE-aided beamforming suffers only a small loss in beamforming gain and enjoys a much lower CE overhead, in comparison to conventional approaches. The benefits of using PACE-aided beamforming during the initial access phase are also discussed.

Analog beamforming greatly reduces the implementation cost of massive antenna transceivers by using only one up/down-conversion chain. However, it incurs a large pilot overhead when used with conventional channel estimation (CE) techniques. This is because these CE techniques involve digital processing, requiring the up/down-conversion chain to be time-multiplexed across the antenna dimensions. This paper introduces a novel CE technique, called continuous analog channel estimation (CACE), that avoids digital processing, enables analog beamforming at the receiver and additionally provides resilience against oscillator phase-noise. By avoiding time-multiplexing of up/down-conversion chains, the CE overhead is reduced significantly and furthermore becomes independent of the number of antenna elements. In CACE, a reference tone is transmitted continuously with the data signals, and the receiver uses the received reference signal as a matched filter for combining the data signals, albeit via analog processing. We propose a receiver architecture for CACE, analyze its performance in the presence of oscillator phase-noise, and derive near-optimal system parameters and power allocation. Transmit beamforming and initial access procedure with CACE are also discussed. Simulations confirm that, in comparison to conventional CE, CACE provides phase-noise resilience and a significant reduction in the CE overhead, while suffering only a small loss in signal-to-interference-plus-noise-ratio.

Many 5G applications require low complexity transceivers that can exploit the benefits of massive antenna arrays while still maintaining low hardware, energy and computation costs. As a solution, this paper proposes a novel multi- antenna frequency shift reference (MA-FSR) receiver that uses only one down-conversion chain, supports single spatial-stream wide-band transmission with non-coherent demodulation, and can perform receive beamforming without requiring phase-shifters, explicit channel estimation, or complicated signal processing. In MA-FSR a reference tone is transmitted along with the data. At each receive antenna, the received signal for the data is correlated with the received signal for the reference, via a squaring operation, thereby compensating for the inter-antenna phase shift. The resulting signals are then summed and fed to a single down-conversion chain. In this paper, performance of the MA-FSR receiver is studied analytically and a near-optimal power allocation policy for the reference and data signals is proposed. Simulations suggest that the signal-to-noise ratio for MA-FSR is around 9 dB lower than that of coherent analog beamforming, although the bandwidth efficiency is also reduced to 50%. Several extensions to MA-FSR that can achieve better performance, though with a higher hardware cost, are also discussed.

This work proposes a novel transmission scheme, namely Reference Tone Aided Transmission (RTAT), that can enable communication in massive MIMO systems with a single analog-to-digital converter at the receiver. Unlike conventional low complexity MIMO transceivers, it can perform receive beamforming without explicit channel estimation or use of phase shifters, thereby leading to a significant reduction in channel estimation overhead and simpler initial access. In RTAT, a sinusoidal reference tone is transmitted along with the data signals. At each receive antenna, the reference tone is recovered, and multiplied by the received signals, to obtain a base-band signal whose inter-antenna phase shift has been compensated. The resulting low-pass signals from all the antennas are then added, emulating maximal ratio combining at the receiver with imperfect channel knowledge. In this work, a receiver architecture for RTAT is proposed, its performance is analyzed and is compared with that of fully digital beamforming. The capacity maximizing power allocation problem is also considered, and one possible initial access protocol for RTAT is suggested. Simulation results show that, in comparison to hybrid beamforming, RTAT suffers only a small loss in signal-to-noise ratio, while providing a significant reduction in the channel estimation overhead.

Auto-correlation receivers, such as frequency shift reference systems, offer a low-complexity alternative to Rake receivers, for low data-rate ultra-wideband applications. In such systems, a data signal and a reference signal are simultaneously transmitted. At the receiver, the received signal corresponding to the data is correlated with the received signal corresponding to the reference signal, thereby accumulating the multi-path channel energies in-phase. For improving bandwidth and energy efficiency, multi- differential schemes have also been proposed where multiple data signals share the same reference signal. With the aim of further boosting the achievable data rates, in this work we propose a multi-differential frequency shift reference receiver that supports higher order modulation formats. We design the corresponding receiver architecture that can exploit both the in-phase and quadrature-phase signal components and analytically characterize the signal and noise components. We also study the problem of optimal bit and power allocation to the multiple data signals, both for uncoded and coded systems, as a function of channel metrics that can be easily tracked at the receiver.

Hybrid preprocessing, where an analogue preprocessing matrix is used to feed a large antenna array to fewer up/down-conversion chains, helps reduce hardware cost of massive Multiple-Input-Multiple-Output systems. Here, we analyze a variant of hybrid preprocessing, namely hybrid preprocessing with selection (HPwS), as an attractive solution to reduce this hardware cost while retaining good performance. In HPwS, the preprocessing matrix, built from radio frequency (RF) hardware, has a larger number of input ports L than the number of up/down-conversion chains K. A bank of RF switches connects the instantaneously best K input ports to the up/down-conversion chains. The preprocessor is designed based on average channel statistics and therefore needs to be updated only infrequently. This allows for a higher diversity-order and/or simpler RF hardware than some conventional hybrid preprocessing systems. In this paper, we propose a generic architecture of HPwS with a possibly non-unitary rectangular preprocessing matrix. A novel preprocessor is designed that maximizes a capacity lower bound for channels with isotropic scattering. In addition, we study how L, the number of users, and the use of low complexity RF hardware, such as discrete phase-shifters, impact system performance. We also present a method to extend the preprocessor design to anisotropic channels.

Many wireless transceivers, such as massive-MIMO and Rake, are designed to provide high diversity order. In order to reduce cost, low-complexity switched transceivers, e.g., hybrid antenna preprocessing with selection, or S-Rake, which employ a limited number of RF chains or correlators, are popular. This paper shows that the presence of limited number of RF chains or correlators in such transceivers introduces an inherent trade-off between system performance and the channel estimation overhead. We prove that to maximize achievable rate, it is optimal to perform channel estimation for only a subset of diversity branches with the highest second moments, if the diversity branches have the same fading parameters but different mean powers. We also prove that the achievable data-rate is a unimodal function of the size of this subset L, which ensures that any locally optimum L is also globally optimum. A computationally efficient approximation for the ergodic capacity is introduced, which reduces the cost of finding the optimal L significantly. Simulation results for some practically important settings suggest that the optimal choice of L can improve the data rate by a factor of 20-30%.

With growing base-station density and decreasing frequency reuse factor, intercell interference and low cell-edge user rates are becoming serious problems. Legacy solutions like fractional frequency reuse are simple to implement but are suboptimal. In this paper, we investigate interlaced clustering as a solution to the edge user problem for a general distributed cellular transmission system. In interlaced clustering, several different coverage patterns coexist on disjoint parts of the spectrum. We demonstrate how various previously suggested network architectures can be interpreted as special cases of interlaced clustering. We then characterize the downlink user throughputs at the proportional fairness operating point of the rate region for both of the cases that the transmitter does, or does not, have channel state information. Based on this derivation, we develop a novel algorithm to solve the resource allocation problem for systems with interlaced clustering. Simulations based on practical cell parameters show that interlaced clustering can provide, on an average, a 100% gain on edge user rate without appreciable loss in rates elsewhere. We also verify that this result is robust to irregular deployment of the remote antenna units.

Many practical communication systems have some form of restricted precoding or postcoding, such as antenna selection, selection combining, beam selection, and limited feedback precoding, to name a few. The capacity analysis of such systems is, in general, difficult and previous works in the literature provide results only for certain simplified cases. This paper derives a novel approach to analyze the capacity for such systems under a very generic setting. The results are based on asymptotic closed-form expressions for the second-order statistics and joint distributions of eigenvalues for a set of coupled, doubly correlated Wishart matrices. A tight approximation to the joint distribution of the eigenvalues in the non-asymptotic regime is also proposed. These results are then used to show that the system capacity can be approximated as the largest element of a correlated Gaussian vector. Showing that this is equivalent to the problem of finding the distribution of sum of lognormals, we propose a novel approach to characterize its distribution. As an application, the capacity for an antenna selection system and a limited feedback precoding system is compared with their respective approximations. This paper also demonstrates how the results can be used to design the precoding codebook in limited feedback systems.

Low signal strength and high interference lead to significantly reduced data-rates at the cell edge. With rising demand for spectrum and systems moving closer to universal frequency reuse, the problem has become more pronounced. Techniques like Fractional Frequency Reuse boost the edgeuser performance at the cost of the spectral efficiency and are therefore sub-optimal. In this paper we investigate interlaced clustering as a solution to the edge user problem for a general distributed cellular transmission system, and explore a multicell Distributed Antenna System as a particular example. In interlaced clustering, different parts of the spectrum use coverage patterns that are spatially shifted (by less than a cell size) replicas of each other. An information theoretic analysis is presented to characterize the proportional fairness boundary point of the achievable rate region. In the process, the joint resource allocation problem is formulated and shown to be convex. As opposed to using interior point methods which are relatively slower, the current paper proposes a novel gradient-search algorithm to solve the resource allocation problem. It is demonstrated that fractional frequency reuse can in fact be represented as a special (albeit sub-optimal) case of interlaced clustering. Simulation results show that interlaced clustering can boost the edge-user rates by a factor of 2 with negligible degradation of rates in the cell interior. Results also show that interlaced clustering outperforms the edge-user rates achieved with fractional frequency reuse by a factor of 1.5. The theoretical results are validated by comparing performance to a practical proportional fairness scheduler.