Beam management is central in the operation of beamformed wireless cellular systems such as 5G New Radio (NR) networks. Focusing the energy radiated to mobile terminals (MTs) by increasing the number of beams per cell increases signal power and decreases interference, and has hence the potential to bring major improvements on area spectral efficiency (ASE). This paper proposes a first system-level stochastic geometry model encompassing major aspects of the beam management problem: frequencies, antenna configurations, and propagation; physical layer, wireless links, and coding; network geometry, interference, and resource sharing; sensing, signaling, and mobility management. This model leads to a simple analytical expression for the effective rate that the typical user gets in this context. This in turn allows one to find the number of beams per cell and per MT that maximizes the effective ASE by offering the best tradeoff between beamforming gains and beam management operational overheads and costs, for a wide variety of 5G network scenarios including millimeter wave (mmWave) and sub-6 GHz. As part of the system-level analysis, we define and analyze several underlying new and fundamental performance metrics that are of independent interest. The numerical results discuss the effects of different systemic tradeoffs and performance optimizations of mmWave and sub-6 GHz 5G deployments.

Automotive radar is a critical feature in advanced driver-assistance systems. It is important in enhancing vehicle safety by detecting the presence of other vehicles in the vicinity. The performance of radar detection is, however, affected by the interference from radars of other vehicles as well as the variation in the target radar cross-section (RCS) due to varying physical features of the target vehicle. Considering such interference and random RCS, this work provides a fine-grained performance analysis of radar detection. Specifically, using stochastic geometry, we calculate the meta distribution of the signal-to-interference-and-noise ratio that permits the reliability analysis of radar detection at individual vehicles. We also evaluate the delay aspect of radar detection, namely, the mean local delay which is the average number of transmission attempts needed until the first successful target detection. For a given target distance, we obtain the optimal transmit probability that maximizes the density of successful radar detection while keeping the mean local delay below a threshold. We also provide several system design insights in terms of the fraction of reliable radar links, transmission delay, the density of vehicles, and congestion control.

Mobility is a key challenge for beam management in 5G cellular networks due to the overhead incurred at beam switching and base station (BS) handover events. This paper focuses on a network that has a multi-tier structure with two types of BSs operating in the same frequency bands, namely macro BSs that are sparser but with higher transmit power, and micro BSs that are denser and with lower transmit power. We propose a downlink user association policy which is a function of the user mobility. Typically, high mobility users should associate with macro BSs so as to incur less beam switching overhead, whereas low mobility ones should be associated with micro BSs. The main contribution of the paper is a formalization of the optimal threshold association policy, when the optimality is understood with respect to the mean effective Shannon rate. The analysis is based on stochastic geometry and on an exact representation of the mean effective Shannon rate of the typical user in this beamforming multi-tier context. Two models are discussed. The simplest one focuses on a single-user optimization problem. We also discuss a more realistic model with bandwidth sharing between all users in the cell. Finally, we identify the mobility and user-density patterns where the velocity-based threshold association policy outperforms the classical best mean power association policy.

Wireless communications aided by reconfigurable intelligent surfaces (RISs) is a promising way to improve the coverage for cellular users. The controlled reflection of the signal from RISs is especially useful in mm-wave networks when the direct link between a cellular user and its serving base station (BS) is weak or unavailable due to blockages. But the joint blockage of the user-RIS and the user-BS links may significantly degrade the performance of RIS-aided transmissions. This paper aims to study the effect of joint blockages on downlink performance. When the RIS locations are coupled with BS locations, using tools from stochastic geometry, we obtain an optimal placement of RISs either to minimize the joint blockage probability of the user-RIS and the user-BS links or to maximize the downlink coverage probability. The results show that installing RISs on the street intersections improves the coverage probability. For users associated with BSs that are deployed sufficiently close to intersections, the intersection-mounted RISs offer a better coverage performance compared to BS-coupled RISs.

Beam management is central in the operation of dense 5G cellular networks. Focusing the energy radiated to mobile terminals (MTs) by increasing the number of beams per cell increases signal power and decreases interference, and has hence the potential to bring major improvements on area spectral efficiency (ASE). This benefit, however, comes with unavoidable overheads that increase with the number of beams and the MT speed. This paper proposes a first system-level stochastic geometry model encompassing major aspects of the beam management problem: frequencies, antennas, and propagation; physical layer, wireless links, and coding; network geometry, interference, and resource sharing; sensing, signaling, and mobility management. This model leads to a simple analytical expression for the effective ASE that the typical user gets in this context. This in turn allows one to find, for a wide variety of 5G network scenarios including millimeter wave (mmWave) and sub-6 GHz, the number of beams per cell that offers the best global trade-off between these benefits and costs. We finally provide numerical results that discuss the effects of different systemic trade-offs and performances of mmWave and sub-6 GHz 5G deployments.

Inspired by a new feature in 5G NR called bandwidth part (BWP), this paper presents a bandwidth allocation (BA) model that allows one to adapt the bandwidth allocated to users depending on their data rate needs. Specifically, in adaptive BA, a wide bandwidth is divided into chunks of smaller bandwidths and the number of bandwidth chunks allocated to a user depends on its needs or type. Although BWP in 5G NR mandates allocation of a set of contiguous bandwidth chunks, our BA model also allows other assumptions on chunk allocation such as the allocation of any set of bandwidth chunks, as in, e.g., LTE resource allocation, where chunks are selected uniformly at random. The BA model studied here is probabilistic in that the user locations are assumed to form a realization of a Poisson point process and each user decides independently to be of a certain type with some probability. This model allows one to quantify spectrum sharing and service differentiation in this context, namely to predict what performance a user gets depending on its type as well as the overall performance. This is based on exact representations of key performance metrics for each user type, namely its success probability, the meta distribution of its signal-to-interference ratio, and its Shannon throughput. We show that, surprisingly, the higher traffic variability stemming from adaptive BA is beneficial: when comparing two networks using adaptive BA and having the same mean signal and the same mean interference powers, the network with higher traffic variability performs better for all these performance metrics. With respect to Shannon throughput, we observe that our BA model is roughly egalitarian per Hertz and leads to a linear service differentiation in aggregated throughput value.

This paper studies the effect of bandwidth partitioning (BWP) on the reliability and delay performance in infrastructureless wireless networks. The reliability performance is characterized by the density of concurrent transmissions that satisfy a certain reliability (outage) constraint and the delay performance by so-called local delay, defined as the average number of time slots required to successfully transmit a packet. We concentrate on the ultrareliable regime where the target outage probability is close to 0. BWP has two conflicting effects: while the interference is reduced as the concurrent transmissions are divided over multiple frequency bands, the signal-to-interference ratio (SIR) requirement is increased due to smaller allocated bandwidth if the data rate is to be kept constant. Instead, if the SIR requirement is to be kept the same, BWP reduces the data rate and in turn increases the local delay. For these two approaches with adaptive and fixed SIR requirements, we derive closed-form expressions of the local delay and the maximum density of reliable transmissions in the ultrareliable regime. Our analysis shows that, in the ultrareliable regime, BWP leads to the reliability-delay tradeoff.

Compared to the standard success (coverage) probability, the meta distribution of the signal-to-interference ratio (SIR) provides much more fine-grained information about the network performance. We consider general heterogeneous cellular networks (HCNs) with base station tiers modeled by arbitrary stationary and ergodic non-Poisson point processes. The exact analysis of non-Poisson network models is notoriously difficult, even in terms of the standard success probability, let alone the meta distribution. Hence, we propose a simple approach to approximate the SIR meta distribution for nonPoisson networks based on the ASAPPP (“approximate SIR analysis based on the Poisson point process”) method. We prove that the asymptotic horizontal gap G0between its standard success probability and that for the Poisson point process exactly characterizes the gap between the bth moment of the conditional success probability, as the SIR threshold goes to 0. The gap G0allows two simple approximations of the meta distribution for general HCNs: 1) the per-tier approximation by applying the shift G0to each tier and 2) the effective gain approximation by directly shifting the meta distribution for the homogeneous independent Poisson network. Given the generality of the model considered and the fine-grained nature of the meta distribution, these approximations work surprisingly well.

Recently a new fundamental performance metric, called the meta distribution of the signal-to-interference ratio (SIR), has been proposed for cellular networks. Compared to the standard success (coverage) probability, the meta distribution provides much more fine-grained information about the network performance. In this paper, we consider general (non-Poisson) network models. However, the exact analysis of non-Poisson network models is notoriously difficult, even in terms of the standard success probability, let alone the meta distribution. Hence we propose a simple approach to approximate the meta distribution for non-Poisson networks, which is based on the ASAPPP (“approximate SIR analysis based on the Poisson point process”) method. For a stationary and ergodic point process model, we prove that the asymptotic horizontal gap G0between its standard success probability and that of the Poisson point process exactly characterizes the gap between the th moment of the conditional success probability, as the SIR threshold goes to G0. Using detailed simulations, we confirm that the meta distribution of an arbitrary stationary and ergodic point process can be approximated by applying the horizontal shift of G0to the meta distribution of the Poisson point process.

We consider a wireless network where multiple energy harvesting transmitters communicate with the common receiver in a time-sharing manner. In each slot, a transmitter can either harvest energy or send its data to the receiver. Given a time deadline, the goal is to maximize the sum rate of transmitters under random energy arrivals with both perfect and imperfect channel state information at the receiver. The original sum-rate maximization (SRM) problem is a non-convex mixed integer non-linear program (MINLP). To obtain the optimal scheduling policy, we first reduce the original optimization problem to a convex MINLP and solve it using the generalized Benders decomposition algorithm. We observe that the SRM problem results in an unfair rate allocation among transmitters, i.e., the transmitter closer to the receiver achieves a higher rate than that by the transmitter farther from the receiver. Hence, to induce fairness among transmitters, we consider the minimum-rate maximization (MRM) problem. For the bounded channel estimation error, we obtain a robust scheduling policy by solving the worst-case SRM and MRM problems. Finally, we compare the proposed policies with myopic policies studied in the literature and show that the former outperform the latter in terms of achievable rates.

We address a fundamental question in wireless networks that, surprisingly, has not been studied before: what is the maximum density of concurrently active links that satisfy a certain outage constraint? We call this quantity the spatial outage capacity (SOC), give a rigorous definition, and analyze it for Poisson bipolar networks with ALOHA. Specifically, we provide exact analytical and approximate expressions for the density of links satisfying an outage constraint and give simple upper and lower bounds on the SOC. In the high-reliability regime where the target outage probability is close to zero, we obtain an exact closed-form expression of the SOC, which reveals the interesting and perhaps counter-intuitive result that all transmitters need to be always active to achieve the SOC, i.e., the transmit probability needs to be set to 1 to achieve the SOC.

We focus on a scenario where two wireless source nodes wish to exchange confidential information via an RF energy harvesting untrusted two-way relay. Despite its cooperation in forwarding the information, the relay is considered untrusted out of the concern that it might attempt to decode the confidential information that is being relayed. To discourage the eavesdropping intention of the relay, we use a friendly jammer. Under the total power constraint, to maximize the sum-secrecy rate, we allocate the power among the sources and the jammer optimally and calculate the optimal power splitting ratio to balance between the energy harvesting and the information processing at the relay. We further examine the effect of imperfect channel state information at both sources on the sum- secrecy rate. Numerical results highlight the role of the jammer in achieving the secure communication under channel estimation errors. We have shown that, as the channel estimation error on any of the channels increases, the power allocated to the jammer decreases to abate the interference caused to the confidential information reception due to the imperfect cancellation of jammer's signal.

Reliable communication is a key requirement in wireless networks. For ad hoc networks, satisfying this requirement is challenging due to the interference caused by uncoordinated concurrent transmissions. In this regard, we provide a simple distributed way for a transmitter to meet the target reliability in an interference-limited network. Specifically, for the Poisson bipolar network with Rayleigh fading, we propose a method for a transmitter to decide on its rate (or, equivalently, the signal-to-interference ratio threshold) such that each link in the network achieves a certain reliability. Here the distributed means that the transmitter only knows the distance from its receiver to the nearest interferer and the fading statistics. Based on this local information, we present a semi-heuristic approach to find the distribution of the signal-to- interference ratio (SIR) threshold corresponding to the total interference power from all interferers. For this purpose, we use the property of the interference that it follows a stable distribution for the standard path loss model. We show that the SIR threshold follows the Weibull distribution.

We introduce a new notion of capacity, termed spatial outage capacity (SOC), which is defined as the maximum density of concurrently active links that have a success probability greater than a predefined threshold. For Poisson bipolar networks, we provide exact analytical and approximate expressions for the density of concurrently active links satisfying an outage constraint. In the high-reliability regime, we obtain an exact closed-form expression of the SOC, which gives its asymptotic scaling behavior.

We integrate a wireless powered communication network with a cooperative cognitive radio network, where multiple secondary users (SUs) powered wirelessly by a hybrid access point (HAP) help a primary user relay the data. As a reward for the cooperation, the secondary network gains the spectrum access where SUs transmit to HAP using time division multiple access. To maximize the sum throughput of SUs, we present a secondary sum-throughput optimal resource allocation (STORA) scheme. Under the constraint of meeting target primary rate, the STORA scheme chooses the optimal set of relaying SUs and jointly performs the time and energy allocation for SUs. In particular, by exploiting the structure of the optimal solution, we find the order in which SUs are prioritized to relay primary data. Since the STORA scheme focuses on the sum throughput, it becomes inconsiderate toward individual SU throughput, resulting in low fairness. To enhance fairness, we investigate three resource allocation schemes, which are: 1) equal time allocation; 2) minimum throughput maximization; and 3) proportional time allocation. Simulation results reveal the tradeoff between sum throughput and fairness. The minimum throughput maximization scheme is the fairest one as each SU gets the same throughput, but yields the least SU sum throughput.

We consider a spectrum sharing scenario, where a secondary network coexists with a primary network of multiple transceivers. The secondary network consists of an energy-constrained decode-and- forward secondary relay which assists the communication between a secondary transmitter and a destination in the presence of the interference from multiple primary transmitters. The secondary relay harvests energy from the received radio- frequency signals, which include the information signal from the secondary transmitter and the primary interference. The harvested energy is then used to decode the secondary information and forward it to the secondary destination. At the relay, we adopt a time switching policy due to its simplicity that switches between the energy harvesting and information decoding over time. Specifically, we derive a closed-form expression for the secondary outage probability under the primary outage constraint and the peak power constraint at both secondary transmitter and relay. In addition, we investigate the effect of the number of primary transceivers on the optimal energy harvesting duration that minimizes the secondary outage probability. By utilizing the primary interference as a useful energy source in the energy harvesting phase, the secondary network achieves a better outage performance.

Energy harvesting (EH) cognitive relays are an exciting solution to the problem of inefficient use of spectrum while achieving green communications and spatial diversity. In a spectrum sharing scenario, we investigate the performance of a cognitive relay network, where a secondary source communicates with its destination over Nakagami-m channels via decode-and-forward EH relays while maintaining the outage probability of the primary user below a predefined threshold. Specifically, we derive a closed-form expression for the secondary outage probability and show that it is a function of the probability of an EH relay having sufficient energy for relaying, which in turn, depends on the energy harvesting and consumption rates of the EH relay and the primary outage probability threshold. We also show that relaxing the primary outage constraint may not always benefit the cognitive EH relay network due to the limitations imposed on the relay's transmit power by the energy constraint.

This paper considers the cooperation between primary and secondary users at information and energy levels when both users are energy harvesting nodes. In particular, a secondary transmitter helps relaying the primary message, and in turn, gains the spectrum access as a reward. Also, the primary transmitter supplies energy to the secondary transmitter if the latter is energy-constrained, which facilitates an uninterrupted cooperation. We address this two-level cooperation over a finite horizon with the finite battery constraint at the secondary transmitter. While promising the rate-guaranteed service to both primary and secondary users, we aim to maximize the primary rate. We develop an iterative algorithm that obtains the optimal offline power policies for primary and secondary users. To acquire insights about the structure of the optimal solution, we examine specific scenarios. Furthermore, we investigate the effects of the secondary rate constraint and finite battery on the primary rate and the probability of cooperation. We show that the joint information and energy cooperation increases the chances of cooperation and achieves significant rate gains over only information cooperation.

Block outlier detection methods, based on Tietjen- Moore (TM) and Shapiro-Wilk (SW) tests, are proposed to detect and suppress spectrum sensing data falsification (SSDF) attacks by malicious users in cooperative spectrum sensing. First, we consider basic and statistical SSDF attacks, where the malicious users attack independently. Then we propose a new SSDF attack, which involves cooperation among malicious users by masking. In practice, the number of malicious users is unknown. Thus, it is necessary to estimate the number of malicious users, which is found using clustering and largest gap method. However, we show using Monte Carlo simulations that, these methods fail to estimate the exact number of malicious users when they cooperate. To overcome this, we propose a modified largest gap method.

Energy detection (ED) is a popular spectrum sensing technique in cognitive radio to detect the primary user. But the detection performance of ED deteriorates in the presence of noise uncertainty and exhibits associated SNR wall phenomenon. In this paper, the generalized energy detector (GED) is investigated, where the squaring operation of amplitude of received samples in conventional energy detector (CED) is replaced by an arbitrary positive operation p. Our aim is to study the effect of noise uncertainty on the detection performance of GED. We consider different distributions of noise uncertainty. Initially, uniform distribution of noise uncertainty is considered and an expression of the SNR wall for the same is derived. It is shown that the SNR wall for uniformly distributed noise uncertainty is independent of p. The study of the detection performance of GED is further extended for log-normally distributed noise uncertainty, where the SNR wall is calculated numerically.