Wi-Fi has been widely used in commercial facilities for wireless connectivity. However, the evolving network traffic and performance requirements, especially for mission-critical applications demanding millisecond-level latency, have highlighted limitations in Wi-Fi’s traditional channel access protocol, Carrier Sense Multiple Access (CSMA), and the challenges of unlicensed spectrum. These issues result in decreased reliability and connectivity disruptions for critical applications. Moreover, seamless roaming and mobility are typically challenging in Wi-Fi networks. The introduction of private networks using Fourth Generation Long-Term Evolution (4G LTE) and Fifth Generation New Radio (5G NR) protocols has enhanced application reliability through scheduled access and interference-free spectrum usage. These deployments can be tailored for both outdoor and indoor environments. This study examines actual field deployments and their test results in environments like oil refineries, warehouses, outdoor parking areas, and distribution centers. We compare actual measurements with a theoretical model to validate the performance enhancements and understand the practical implications for real-world applications. We analyzed performance factors such as signal strength (Reference Signal Received Power (RSRP) heat maps), coverage, packet latency, and mobility. This research provides insights into the practical performance of private networks and their suitability for supporting mission-critical applications in diverse settings.

When facing the challenges of wireless communications beyond 5G, single-carrier waveforms appear as a promising enabler for energy-efficient communications thanks to their low PAPR. Among the single-carrier waveforms, DFT-s-OFDM plays a privileged role because of its commonalities with CP-OFDM, albeit its flexibility to allocate control signals in the frequency domain is much more limited. This paper describes a technique for insertion of Reference Signals in the frequency domain based on the use of padding sequences appended to the data before the Transform Precoding stage, followed by direct reference signal insertion after DFT. We prove that the use of padding sequences can trade off PAPR vs. performance through proper sequence design, and that a dynamic framework for sequence selection based on feedback from the receiver can be advantageous. Simulations confirm the suitability of the proposed technique as an enabler for flexibly allocating distributed reference signals with a reduced overhead compared to other standard approaches.

Conventional MIMO precoding relying on a predefined codebook exhibits significantly poor performance in terms of bit error ratio (BER). This is because traditionally codebooks are designed relying on the simplifying assumption of having fixed channel probability distribution. However, owing to both hardware imperfections arising due to non-linear components and channel estimation imperfections, as well as channel aging, the fixed channel probability assumption is unrealizable in practical MIMO systems. Therefore, in this paper, we first propose a data-driven minimum BER (MBER) based precoder using deep reinforcement learning (DRL) without relying on channel model distribution. Then to facilitate the finite-rate MIMO feedback systems, we propose a data-driven user-specific codebook design for downlink users (UE), where UE populates MBER precoders and constructs the codebook by invoking an unsupervised clustering based quantizer. We demonstrate by simulations that our proposed precoder selection relying on data-driven codebook performs similarly to that of genie-aided channel state information (CSI), whilst our data-driven precoder design outperforms the conventional MIMO precoding by more than 5 dB for BER of 1$0^{-3}$ with CSI error variance of 0.02.

Conventionally, the user selection for non-orthogonal multiple access (NOMA) transmission is employed by techniques such as clustering, where disparities in channel and effective received power are exploited. However, these methods necessitate having channel state information (CSI) at the transmitter, which imposes significant overhead. Additionally, the successive interference cancellation (SIC) assisted detection at the receiver is sensitive to the CSI impairments which erodes the bit error ratio (BER) due to error propagation. In this paper, we propose a deep Q-network (DQN) based NOMA user selection by exploiting the users’ uplink received signal whilst also simultaneously circumventing the downlink CSI. Furthermore, in order to facilitate the NOMA detection of dynamic users in the cluster, we advocate a codebook design comprising of a plurality of deep neural network (DNN) weights for different numbers of users, where user performs the symbol detection without relying on the CSI. We demonstrate by simulations that our proposed DQN-assisted NOMA user selection despite its low search complexity on the order of $\mathcal{O}\left( 1 \right)$ performs similarly to that of the optimal user selection, where an exhaustive search of all possible user combinations with a complexity on the order of $\mathcal{O}\left( {{2^n}} \right)$ is performed. Additionally, we show by simulations that our DNN aided NOMA symbol detection performs close to that of the conventional SIC with perfect CSI and outperforms the conventional SIC with CSI impairments by more than 5 dB SNR at a BER of 10−4and Doppler of 1 kHz.

In this paper, a novel scheme that utilizes soft decision LLR values of CRC bits to aid BP decoding in polar codes is proposed. We show that the proposed periodic CRC scheduling for the factor graphs of BP decoding, combined with relaxation operations can prevent short cycles and improve FER performance of BP decoding. The proposed scheme provides around 0.75 dB gain over CASCL decoder and 1 dB gain over conventional BP decoder at a FER of 10^-4 for N=1024 and R=0.5.

Code review is a systematic inspection of the code-base. It ensures that the software satisfies the required functionalities and standards; thus, it is an essential stage in the modern development process and is used frequently in the industry. For a successful review, the reviewer should be able to identify defects; therefore, the process is highly dependent on the awareness of the reviewer. Currently, code review is done by comparing the line-by-line differences on the codebase. However, this does not give structural information, such as design pattern changes or dependency changes between services.To address this problem, we propose an augmented next-generation code review experience using visual and holistic approaches to streamline the reviewing process. The proposed code review experience shows the structural representation of the change by visualizing it into a UML-like relationship diagram. Along with this diagram, the risk percentage for each commit is highlighted to emphasize the differences that possibly can be most affected by the change. An artifact map is also produced that includes issue-commit relation for bug tracking, to make reviewers aware of the frequent issues that are brought up. This map also highlights the change frequency of the files, for focusing the reviewers on the possibly vulnerable parts of the software for better quality reviews. The proposed visualizations have been implemented in a plugin-based proof-of-concept tool integrated within GitHub, supporting the visual reviewing of changes in codebases developed with Java language.

The ability to repeat the execution of a program is a fundamental requirement in evaluating computer systems and apps. Reproducing executions of mobile apps has proven difficult under real-life scenarios due to different sources of external inputs and interactive nature of the apps. We present a new practical record/replay framework for Android, RandR, which handles multiple sources of input and provides cross-device replay capabilities through a dynamic instrumentation approach. We demonstrate the feasibility of RandR by recording and replaying a set of real-world apps.

The ability to repeat the execution of a program is a fundamental requirement in many areas of computing from computer system evaluation to software engineering. Reproducing executions of mobile apps, in particular, has proven difficult under real-life scenarios due to multiple sources of external inputs and interactive nature of the apps. Previous works that provide record/replay functionality for mobile apps are restricted to particular input sources (e.g., touchscreen events) and present deployment challenges due to intrusive modifications to the underlying software stack. Moreover, due to their reliance on record and replay of device specific events, the recorded executions cannot be reliably reproduced across different platforms. In this paper, we present a new practical approach, RandR, for record and replay of Android applications. RandR captures and replays multiple sources of input (i.e., UI and network) without requiring source code (OS or app), administrative device privileges, or any special platform support. RandR achieves these qualities by instrumenting a select set of methods at runtime within an application's own sandbox. In addition, to enable portability of recorded executions across different platforms for replay, RandR contextualizes UI events as interactions with particular UI components (e.g., a button) as opposed to relying on platform specific features (e.g., screen coordinates). We demonstrate RandR's accurate cross-platform record and replay capabilities using over 30 real-world Android apps across a variety of platforms including emulators as well as commercial off-the-shelf mobile devices deployed in real life.

Power densities of modern mobile system-on-a-chip designs can quickly exceed the thermal design limits during typical application use such as gaming or Web browsing. Resulting high temperatures lead to frequent thermal throttling and significant loss in quality-of-service (QoS) delivered to users. Thus, a joint consideration of thermal constraints and QoS requirements is essential to maximize the overall user experience. Prior techniques either rely on users to determine the best tradeoff point between QoS and temperature, or greedily utilize the thermal headroom to maximize performance, causing QoS to drop below user tolerable levels over extended durations of use. This paper introduces the MAESTRO framework to automatically manage QoS at runtime depending on application characteristics and thermal constraints. MAESTRO builds on the observation that increased temperatures can be tolerated for applications with bursty compute patterns due to idle periods between activities, while causing large QoS degradations for long-running applications with continuous computations. MAESTRO: 1) detects such continuous computations that are susceptible to throttling; 2) proactively finds a QoS level to balance user experience and temperature; and 3) performs closed-loop DVFS and thermally efficient thread mapping to meet the target QoS on a heterogeneous multicore CPU. Such application-adaptive control of QoS-temperature tradeoffs allows MAESTRO to sustain a target QoS level within a user tolerable range for longer durations without sacrificing the performance of latency-sensitive bursty computations. Evaluations on a real system prototype validates MAESTRO's ability to accurately detect potential throttlinginduced QoS degradations and demonstrates 41% to 6.7× longer durations of sustained QoS compared to state-of-the-art for a set of mobile applications.

Single-ISA heterogeneous mobile processors integrate low-power and power-hungry CPU cores together to combine energy efficiency with high performance. While running computationally demanding applications, current power management and scheduling techniques greedily maximize quality-of-service (QoS) within thermal constraints using power-hungry cores. We show that such an approach delivers short bursts of high QoS, but also causes severe QoS loss over time due to thermal throttling. To provide mobile users with sustainable QoS over extended durations, this paper proposes QScale. QScale is a novel thermally-efficient QoS management framework for mobile devices with heterogeneous multi-core CPUs. QScale leverages two novel observations to provide thermally-efficient QoS: (1) threads of a mobile application exhibit significant heterogeneity, which can be exploited during scheduling; (2) thermal efficiency of core allocation decisions is significantly altered by thermal interactions across system-on-a-chip (SoC) components and application characteristics. QScale coordinates closed-loop frequency control with thermally-efficient scheduling to deliver the desired QoS with minimal exhaustion of processor thermal headroom. Our experiments on a state-of-the-art heterogeneous mobile platform show that QScale meets target QoS levels while minimizing heating, achieving up to 8x longer durations of sustainable QoS.

By means of the emerging technique of dynamic Time Division Duplex (TDD), the switching point between uplink and downlink transmissions can be optimized across a multi-cell system in order to reduce the impact of inter-cell interference. It has been recently recognized that optimizing also the order in which uplink and downlink transmissions, or more generally the two directions of a two-way link, are scheduled can lead to significant benefits in terms of interference reduction. In this work, the optimization of bi-directional scheduling is investigated in conjunction with the design of linear precoding and equalization for a general multi-link MIMO two-way system. A simple algorithm is proposed that performs the joint optimization of the ordering of the transmissions in the two directions of the two-way links and of the linear transceivers, with the aim of minimizing the interference leakage power. Numerical results demonstrate the effectiveness of the proposed strategy.

With the integration of high-performance multicore processors and multiple accelerators into modern mobile system-on-chips (SoCs), power densities have grown substantially. As a result, thermal management policies, which ensure operation at thermally safe conditions, became essential components of state-of-the-art mobile systems. Traditional thermal throttling approaches aim at maximum utilization of the available thermal headroom to minimize the performance loss and maximize user performance. This paper demonstrates that, in a mobile platform, such greedy techniques can lead to significant degradation in the quality-of-service (QoS) levels as the duration of device activity increases, leading to inconsistent user experience over time. We demonstrate that incorporating user/application QoS requirements into mobile power management to provide “just enough” performance (instead of always maximizing performance) allows for more efficient usage of the thermal headroom, which translates to substantially extended durations of sustainable performance. We propose a closed-loop QoS control policy, including an efficient dynamic voltage and frequency scaling (DVFS) state scheduling technique, to minimize the thermal impact for extending the sustainability of desired QoS levels. Experiments on a modern smartphone show that the proposed technique provides up to 74% longer sustainable performance while meeting the target QoS demands for a variety of real-life applications.

In the downlink of cloud radio access networks, a central encoder is connected to multiple multi-antenna base stations (BSs) via finite-capacity backhaul links. At the central encoder, precoding is followed by compression in order to produce the rate-limited bit streams delivered to each BS over the corresponding backhaul link. In current state-of-the-art schemes, the signals intended for different BSs are compressed independently. In contrast, this work proposes to leverage joint compression, also referred to as multivariate compression, of the signals for different BSs in order to better control the effect of the additive quantization noises at the mobile stations (MSs). The problem of maximizing the weighted sum-rate over precoding and compression strategies is formulated subject to power and backhaul capacity constraints. An iterative algorithm is proposed that achieves a stationary point of the problem. From numerical results, it is confirmed that the proposed joint precoding and compression strategy outperforms conventional approaches based on independent compression across the BSs.

This work investigates efficient backhaul compression strategies for the uplink of cloud radio access networks with a general multihop backhaul topology. In these systems, each radio unit (RU) communicates with the managing control unit (CU) through a set of intermediate RUs. A baseline multiplex-and-forward (MF) scheme is first studied in which each RU forwards the bit streams received from the connected RUs without any processing. It is observed that this strategy may cause significant performance degradation in the presence of a dense deployment of RUs. To obviate this problem, a scheme is proposed in which each RU decompresses the received bit streams and performs linear in-network processing of the decompressed signals. For both the MF and the decompress-process-and-recompress (DPR) backhaul schemes, the optimal design is addressed with the aim of maximizing the sum-rate under the backhaul capacity constraints. Based on the analysis, numerical results are provided to compare the performance of the MF and DPR schemes, highlighting the potential advantage of in-network processing.

This work considers a setting in which an encoder wishes to communicate with a decoder through a relay that is connected to the decoder via a finite-capacity link. Motivated by communication on the uplink of a cloud radio access cellular network, it is assumed that the relay compresses and forwards the received signal; moreover, the decoder has side information about the transmitted signal that is subject to fading whose realization is unknown to encoder and relay. A robust transmission and compression strategy is proposed that aims at minimizing the transmitted power under competitive rate optimality constraints. This contrasts with more conventional worst-case or average performance criteria. The transmission strategy is based on a broadcast coding and is parameterized by the maximum tolerable delay in terms of number of fading coherence blocks. Numerical results demonstrate the role of delay and the advantages of broadcast coding over the conventional single-layer transmission.

This paper addresses robust communication on a fading relay channel in which the relay is connected to the decoder via an out-of-band digital link of limited capacity. Both the source-to-relay and the source-to-destination links are subject to fading gains, which are generally unknown to the encoder prior to transmission. To overcome this impairment, a hybrid automatic retransmission request (HARQ) protocol is combined with multi-layer broadcast transmission, thus allowing for variable-rate decoding. Moreover, motivated by cloud radio access network applications, the relay operation is limited to compress-and-forward. The aim is maximizing the throughput performance as measured by the average number of successfully received bits per channel use, under either long-term static channel (LTSC) or short-term static channel (STSC) models. In order to opportunistically leverage better channel states based on the HARQ feedback from the decoder, an adaptive compression strategy at the relay is also proposed. Numerical results confirm the effectiveness of the proposed strategies.

In this work, joint decompression and decoding is studied for the uplink of multi-antenna cloud radio access networks. In this system, a set of multi-antenna mobile stations (MSs) wish to communicate with a “cloud” decoder through a set of multi-antenna base stations (BSs), which are connected to the cloud decoder through digital backhaul links of limited capacity. The BSs compress the received signal and send it to the cloud decoder, which performs joint decoding of the signals from all MSs. While the conventional solution prescribes that the cloud decoder performs first decompression and then decoding, recent work has shown that potentially larger rates can be achieved with joint decompression and decoding (JDD) at the cloud decoder. The sum-rate maximization problem with JDD, under the assumption of Gaussian test channels, is shown here to be an instance of a class of non-convex problems known as Difference of Convex (DC) problems. Based on this observation, an iterative algorithm based on the Majorization Minimization (MM) approach is proposed that guarantees convergence to a stationary point of the sum-rate maximization problem. Numerical results demonstrate the advantage of the proposed algorithm compared to the conventional approach based on separate decompression and decoding.

This work studies distributed compression for the uplink of a cloud radio access network, where multiple multi-antenna base stations (BSs) communicate with a central unit, also referred to as cloud decoder, via capacity-constrained back-haul links. Distributed source coding strategies are potentially beneficial since the signals received at different BSs are correlated. However, they require each BS to have information about the joint statistics of the received signals across the BSs, and are generally sensitive to uncertainties regarding such information. Motivated by this observation, a robust compression method is proposed to cope with uncertainties on the correlation of the received signals. The problem is formulated using a deterministic worst-case approach, and an algorithm is proposed that achieves a stationary point for the problem. From numerical results, it is observed that the proposed robust compression scheme compensates for a large fraction of the performance loss induced by the imperfect statistical information.

A Gaussian Interference Channel (IC) is investigated in which a relay assists two source-destination pairs. The relay is assumed to receive over dedicated orthogonal channels from the sources (e.g., over orthogonal bands or time slots, or over wired links), while it transmits in the same band as the sources. This scenario is referred to as IC assisted by an out-of-band reception/ in-band transmission relay (IC-OIR). An achievable rate region is derived for the IC-OIR that encompasses, besides the standard signal relaying, interference management via interference relaying, cancellation and precoding. The sum-capacity is found in a specific regime defined by the very strong relay-interference conditions. Numerical results validate the performance gains of interference mitigation via the relay.

A Gaussian interference channel (IC) with a relay is considered. The relay is assumed to operate over an orthogonal band with respect to the underlying IC, and the overall system is referred to as IC with an out-of-band relay (IC-OBR). The system can be seen as operating over two parallel interference-limited channels: The first is a standard Gaussian IC and the second is a Gaussian relay channel characterized by two sources and destinations communicating through the relay without direct links. We refer to the second parallel channel as OBR Channel (OBRC). The main aim of this work is to identify conditions under which optimal operation, in terms of the capacity region of the IC-OBR, entails either signal relaying and/or interference forwarding by the relay, with either a separable or nonseparable use of the two parallel channels, IC, and OBRC. Here, “separable” refers to transmission of independent information over the two constituent channels. For a basic model in which the OBRC consists of four orthogonal channels from sources to relay and from relay to destinations (IC-OBR Type-I), a condition is identified under which signal relaying and separable operation is optimal. This condition entails the presence of a relay-to-destinations capacity bottleneck on the OBRC and holds irrespective of the IC. When this condition is not satisfied, various scenarios, which depend on the IC channel gains, are identified in which interference forwarding and nonseparable operation are necessary to achieve optimal performance. In these scenarios, the system exploits the “excess capacity” on the OBRC via interference forwarding to drive the IC-OBR system in specific interference regimes (strong or mixed). The analysis is then turned to a more complex IC-OBR, in which the OBRC consists of only two orthogonal channels, one from sources to relay and one from relay to destinations (IC-OBR Type-II). For this channel, some capacity resuls are derived that parallel the conclusions for IC-OBR Type-I and point to the additional analytical challenges.

A Gaussian interference channel (IC) aided by a half-duplex relay is considered, in which the relay receives and transmits in an orthogonal band with respect to the IC. The system thus consists of two parallel channels, the IC and the channel over which the relay is active, which is referred to as Out-of-Band Relay Channel (OBRC). The OBRC is operated by separating a multiple access phase from the sources to the relay and a broadcast phase from the relay to the destinations. Conditions under which the optimal operation, in terms of the sum-capacity, entails either signal relaying and/or interference forwarding by the relay are identified. These conditions also assess the optimality of either separable or non-separable transmission over the IC and OBRC. Specifically, the optimality of signal relaying and separable coding is established for scenarios where the relay-to-destination channels set the performance bottleneck with respect to the source-to-relay channels on the OBRC. Optimality of interference forwarding and non-separable operation is also established in special cases.

A system with multiple transmitters (i.e. base stations) and multiple receivers (mobile nodes) is considered. The transmitter-receiver pairs are assumed to operate using the same resources without any coordination and hence the receivers unavoidably experience interference from undesired transmitters. A MIMO relaying scheme is proposed to manage the interference in the network by judiciously optimizing the relay precoding matrix and aligning the interference at each receiver simultaneously. It is shown that the proposed scheme provides a closed-form optimal precoding matrix. Moreover, the numerical results for various scenarios show that the scheme performs better than the considered baseline schemes for most of the channel conditions.

A Gaussian interference channel with an infrastructure relay (ICIR) is investigated. The relay has finite-capacity links to both sources and destinations that are orthogonal to each other and to the underlying interference channel. A general achievable rate region is presented by using the relay both to convey additional information from the sources (signal relaying) and to ease interference cancellation (interference forwarding). Outer bounds to the capacity region are also derived, and used to determine a number of regimes of interest where either signal relaying only or both signal relaying and interference forwarding are optimal.

In this paper, an interference relay channel where two independent sources communicate with two destinations by the help of a relay is studied. The relay is full-duplex and employs decode-forward type strategy. Using Carleial's rate splitting where each source transmits common messages to be decoded at both destinations and private messages for the desired destination, an achievable rate region is obtained. The relay decodes both common and private messages and transmits them cooperatively with the sources to the destinations. For the symmetric Gaussian interference relay channel, the results show that the maximum rate sum is achieved when the relay helps the sources in the transmission of common messages only. Moreover, unlike the regular interference channel, when the relay is present, the sources continue to transmit common information even when the interfering links can support much less rate than the direct links.

This paper addresses uplink power control for terminals that transmit multimedia signals in a CDMA cell. The aim of the power control is to minimize total power consumed by the terminals due to signal compression and transmission while the end-to-end distortion for each terminal is kept at a predetermined value. We propose a distributed iterative power control algorithm and prove convergence. The simulations for Gauss-Markov source with transform coder and H.263 encoded video signal show that the proposed algorithm achieves the jointly optimal power values for most of the channel conditions. It is also observed that the algorithm outperforms non-adaptive multimedia transmission in terms of power consumption