Emerging industrial and massive Internet of Things (IoT) applications increasingly rely on private 5G New Radio (NR) deployments operating in the 3.5 GHz Citizens Broadband Radio Service (CBRS) band. General Authorized Access (GAA) provides a low-cost spectrum opportunity for IoT-specific NR networks, but the absence of interference protection makes uplink reliability highly vulnerable to co-channel interference (CCI), especially in dense IoT deployments. Ensuring dependable IoT connectivity requires interference-aware channel pattern allocation coordinated by the Spectrum Access System (SAS) while accounting for IoT traffic characteristics and gateway constraints. This paper presents a game-theoretic optimization framework for channel pattern allocation for IoT NR small cells operating under the CBRS GAA tier. We first formulate CCI minimization as a binary quadratic program (BQP) and show its nonconvex and NP-hard nature. To address this complexity, we develop two complementary solutions: (i) a cooperative hedonic coalition formation algorithm that enables SAS-assisted clustering of IoT NR gateways, and (ii) a distributed potential game approach where IoT gateways autonomously select channel patterns using log-linear learning. Numerical evaluations under realistic IoT deployment and propagation models demonstrate significant gains in packet delivery ratio (PDR), reduced energy consumption of IoT devices through fewer retransmissions, and improved spectrum reliability compared to existing CBRS coexistence mechanisms. The proposed framework is shown to be robust under asymmetric interference conditions and achieves more equitable per-user reliability than existing coexistence mechanisms. The proposed approaches provide practical and scalable solutions for interference-aware private IoT NR networks in shared mid-band spectrum.

ALOHA has become an essential random access protocol for low-power wide-area networks (LPWANs) due to its compatibility with low-cost, low-power consumption and long-range communication requirements. However, its inherent throughput limitation of approximately 0.183 packets per packet transmission time for a large user population significantly restricts its scalability and suitability for the rapidly growing number of IoT devices. To surpass this fundamental bound, we propose a non-orthogonal multiple access (NOMA)-based collision resolution scheme that enables devices to initiate immediate contention-resolving retransmissions following a collision event. This approach requires only an additional collision timer for each user to determine collision resolution participation and appropriate transmission power selection, thereby substantially enhancing throughput without incurring additional hardware costs. We present an analytical framework to evaluate the achievable system throughput and develop an online backoff algorithm that leverages an extended Kalman filter (EKF)-based backlog estimator to dynamically maximize system performance. Numerical results confirm the reliability of the proposed scheme, demonstrating that the system throughput can be improved to approximately 0.5 packets per packet transmission time under ideal conditions. Moreover, the EKF-based approach closely approximates optimal throughput and delay performance under both Poisson and bursty traffic conditions.

This study investigates ALOHA with listen-before-talk (LBT) to enhance the scalability of low-power wide-area networks (LPWANs), such as long range (LoRa). The LBT allows devices to sense the channel prior to accessing so that it can mitigate interference by preventing devices from transmitting during ongoing transmissions. However, its effectiveness is compromised by inherent imperfections in channel sensing, such as false negatives and false positives. A false negative occurs when devices incorrectly find the channel idle while it is actually in use. Thus, this leads devices to unintended interferences with ongoing transmissions. A false positive arises when the channel is erroneously sensed as busy, despite the fact that it is free. This deprives devices of access opportunities. This work analyzes the impact of these imperfections of LBT on the performance of ALOHA in terms of throughput, access delay, and system stability. Additionally, we propose an online backoff control algorithm to optimize system performance under imperfect LBT. The results show that even when devices falsely identify the channel as idle or mistakenly detect it as busy nearly half the time, the throughput still outperforms that of ALOHA without LBT. The proposed backoff control algorithm is also shown to be essential to maximize the throughput in the presence of sensing errors. To demonstrate our analysis and algorithm, we incorporate LoRa’s physical layer parameters into simulations and validate the results accordingly.

ALOHA can be a viable solution as a light-weight medium access control (MAC) protocol in low power wide area networks (LPWANs) for Internet of Things (IoT). However, the maximum throughput of traditional ALOHA is too low to accommodate a large number of IoT devices. To address this limitation, this work proposes an enhanced ALOHA, where successive interference cancellation (SIC) aids in collision resolution. In the proposed system, each user measures the time interval from their transmission epoch to the end of a collision using a collision timer. Upon a collision, the access point (AP) with SIC and the users’ collision timer work jointly to resolve the collision. This work characterizes the throughput of the proposed system and further proposes an online backoff algorithm to maximize the throughput. Numerical results demonstrate that the proposed ALOHA with SIC-aided collision resolution (SACR) can offer significantly improved throughput compared to slotted ALOHA and the other systems.

The rapid expansion of Internet of Things (IoT) applications has led to significant access challenges for radio access networks, potentially causing bottlenecks and degrading service quality. This paper proposes Dual-Core, a novel dual collision resolution mechanism for the four-step random access (RA) procedure of 5G communication systems. Dual-Core integrates slotted ALOHA protocol for random access preamble (RAP) transmission and a custom splitting algorithm for bandwidth request message (Msg 3) transmissions, resolving collisions in both PRACH and PUSCH. Additionally, we introduce an online controlled access class barring (ACB) to maximize the throughput of Dual-Core, with the optimal ACB rate determined by our proposed analytical model. Numerical results demonstrate that the proposed Dual-Core significantly reduces service time compared to the conventional RA procedure operating at its optimal performance, while utilizing the same amount of PUSCH resources. Furthermore, when additional PUSCH resources are available, Dual-Core efficiently utilizes them to serve more devices, a capability that conventional systems lack.

Along with the rapid growth of Internet of Things (IoT), low power wide area networks (LPWANs) based on unslotted ALOHA, without the need for strict synchronization among IoT devices, becomes one of the cost-effective solutions to support wireless access for sensors requiring long-range connectivity, low cost, and low-power consumption. However, the increasing number of IoT devices poses potential congestion for ALOHA with a low throughput limit of 0.183 (data frames per frame transmission time). This work addresses the throughput limitations of ALOHA by implementing immediate collision resolution (ICR) and integrating an online Bayesian backoff algorithm. The focus is on lifting the throughput limit to enhance LPWANs performance. The work further details translating MAC layer modeling into practical implementation, emphasizing the integration of additional functionalities like uplink local sensing (ULS) and receive window. Our implemented ALOHA with ICR shows a substantial 29% throughput improvement, validating theoretical predictions.

The accuracy of localization using global positioning system (GPS) data plays a key role in reliable positioning and control of unmanned aerial vehicles (UAVs). This paper proposes a novel statistically-aided earning-based localization approach, called filtered neural network (FNN) for high-precision localization of UAVs. The proposed FNN framework utilizes an entropy adaptive Kalman filter to fine-tune the inputs to a recurrent neural network, which works in a loop with the filter to generate subsequent robust position estimates. The proposed framework outperforms the state-of-the-art techniques with an nRMSE of ≈ 10−6, ≈ 97% reduced estimation delay, ≈ 73% reduced modeling time, ≤ 100 lag samples for FNN training, and only 4-6 overall model retraining instances per flight trajectory. The results are verified over a wide range of mean GPS noise power.

In machine type communication (MTC), diverse applications requiring high reliability and low latency lead to the consideration of ultra-reliable and low-latency MTC (uMTC) communication scenarios. Consequently, two-step random access procedure (RAP), as an alternative to the four-step RAP, is introduced into the 5th generation (5G) communication systems to reduce the unnecessary latency caused by the multi-round transmissions in the wireless medium. In this paper, we first analyze the delay performance of the two-step RAP based on which we further propose an algorithm to control the number of preambles allocated for the two-step RAP to meet a given average delay requirement. In particular, our proposed algorithm estimates the number of active devices in an online manner and controls the number of preambles to be allocated. Through extensive simulations, we show the effectiveness of our proposed algorithm in satisfying the delay requirement as well as minimizing the preamble resource usage.

To improve the throughput per channel in multichannel nonorthogonal multiple access (NOMA) random access (RA) system, users (re)transmit their packet to one of the channels using transmit power control such that the receive power of the packets at the base station (BS) can be one of the predefined levels called target receive power (TRP). The BS decodes the received packets in the descending order of the TRPs at each slot using successive interference cancellation (SIC). This work proposes the real-time transmission algorithm for users to (re)transmit their packet for maximization of the system throughput. To do this, the BS estimates the number of backlogged users in real time and adjusts and broadcasts the throughput-optimal (re)transmission probability in the algorithm. We analyze the average RA delay performance of the proposed algorithm and demonstrate its performance even with time-varying traffic.

Internet-of-Things (IoT) applications for real-time control gradually increase and become computationally demanding. To provide better quality-of-service (QoS) in random access (RA) system based on slotted ALOHA (S-ALOHA), this work proposes a novel S-ALOHA system with cross-slot successive interference cancellation (SIC). To facilitate SIC, we design each slot with several time offsets (TOs) and one packet transmission time, where the length of overall TOs is a fraction of a packet transmission time. Users (re)transmit at the boundary of a TO randomly selected. This enables the base station (BS) to distinguish who makes the first and last transmissions in a collision slot and ask immediate retransmissions from them in the subsequent one or two slots. With these retransmitted packets, the BS performs SIC for the previously collided packets. We analyze the system throughput and the distribution of RA delay. The results show that the proposed system can achieve throughput from 0.5 (packets per packet transmission time) at minimum to 0.856 at maximum, depending on the number of TOs and the length of TO. In addition, to run this system stably, we propose a Bayesian-optimized backoff algorithm that enables users to use throughput-optimal (re)transmission probability. It is demonstrated that the proposed backoff algorithms can achieve the throughput close to genie-aided (GA) system.

This work considers a bulk transmission scheme for slotted ALOHA (S-ALOHA) systems with a finite population of unsaturated terminals. In the system, once a user makes a successful random access (RA) via S-ALOHA, it can transmit C packets to the traffic channel. We examine an effective capacity, which is defined as the maximally admissible mean arrival rate to the system, subject to the constraint that each user’s queue does not overflow. To obtain this capacity, we use the large deviation theory on the user’s queueing process. For comparison, we also use the dominant pole method in order to obtain the butter overflow probability. We then show that results from the method of using the large deviation theory are asymptotically identical to those from the dominant pole method due to the same decaying rate, and that keeping the traffic load within the effective capacity prevents a user’s queue from overflowing in practice.

A larger capacity of random access (RA) channel is demanded to cope with massive accesses from Internet of Things (IoT) devices. To do this, this work proposes multipacket reception (MPR) S-ALOHA with reservation: When making a successful RA, the user can reserve the channel for the next slot transmission with probability$r$, if its queue is not yet empty. This reservation can be continued with probability$r$until its queue becomes empty. In this system, as the number of the reserved channels grows, the number of available capacity for RA reduces. Thus, the number of RA attempting users needs to be controlled to avoid collision, whereas the unbounded growth of other users’ queue should be prevented. This work analyzes the throughput and stability condition of the proposed system and designs a throughput-optimal backoff algorithm based on the backlog size estimation. In the numerical studies, MPR S-ALOHA without reservation and time division multiple access (TDMA) are compared as benchmarks. As a result, it is proven that as$r\rightarrow 1$, the MPR channel capacity is fully utilized if the proposed RA algorithm is jointly used with the reservation scheme. Moreover, it is demonstrated that the system can be also stabilized by the proposed backoff algorithm.

Unslotted ALOHA has been often employed by several low-power wide-area networks (LPWANs) for Internet of Things (IoT) as a random access (RA) protocol. This work analyzes the performance of unslotted ALOHA systems in terms of throughput and RA delay, and investigates their optimization. Our analysis consists of: 1) two-heterogeneoususer case, whose backoff rate and packet length are different; 2)$N$-homogeneoususer case, whose backoff rate and packet length are identical; and 3) homogeneous users of infinite population model. In the two-user case, we investigate the throughput region of unslotted ALOHA by using a multiobjective optimization problem (MOOP) and derive the Laplace Stieltjes transform (LST) of the probability density function (PDF) of RA delay. For$N$-homogeneous user case, we show how the throughput behaves according to the population size, packet length, and backoff rate. Our work may provide a comprehensive analytical framework for unslotted ALOHA systems.

Proliferation of the urban Internet-of-Things (IoTs) for smart cities has fuelled massive amounts of data over wireless cellular networks. Random access (RA) system of wireless cellular networks, e.g., 5G New Radio (NR), based on S-ALOHA system should cope with ever-growing IoT traffic. This work proposes S-ALOHA system with time offsets (TOs), where one slot consists of K TOs and one packet transmission time. The length of the overall TOs is a fraction of a packet transmission time. In the system users (re)transmit to the boundary of a TO randomly selected. This enables the base station (BS) to inform the users of who transmits the first and the last packets in the slot with collision so that the two users can retransmit successfully in the following two slots respectively. Our throughput analysis compared to simulations shows that adopting even with three and four TOs surpasses the throughput limit of S-ALOHA system without TOs. Additionally, we propose two Bayesian-optimized backoff algorithms for S-ALOHA system with TOs, with which users can apply throughput-optimal (re)transmission probability or uniform backoff window even in unsaturated traffic scenarios. Numerical results demonstrate that the proposed backoff algorithms can achieve the throughput close to an ideal system and drastically reduce the access delay compared to S-ALOHA system.

Unslotted ALOHA protocol has been adopted as a channel access mechanism in commercial low-power wide-area networks (LPWANs), such as Sigfox and long-range (LoRa) alliance. This work examines the throughput and random access (RA) delay distribution of unslotted ALOHA systems by considering exponential random backoff (ERB) or uniform random backoff (URB) algorithm. We further characterize the operating region of the systems as unsaturated stable, bistable, and saturated regions in terms of the new packet arrival and retransmission rates. To run the system stably with the maximum throughput, we propose a Bayesian online backoff algorithm that estimates the number of backlogged devices. Its performance is compared with other algorithms, such as particle filter (PF)-based algorithm, binary exponential backoff (BEB) algorithm, and the algorithm of exploiting exact backlog size information. Through extensive simulations, it is demonstrated that the performance of the proposed algorithm is very close to the upper bound and robust to time-varying traffic condition.

In slotted-ALOHA (S-ALOHA) system, users should contend for the channel to transmit a packet in their queue every single time, irrespective of how many packets they have in their queue. The maximum throughput of S-ALOHA system is known as$e^{-1}\approx 0.3679$(packets/slot) for a large number of users. This work proposes a novel S-ALOHA system, where the user with a successful (re)transmission of the packet at the head of queue is allowed to keep on transmitting his packet with some reservation probability if his queue is not empty. We call this system S-ALOHA system with successive transmission (ST) and show that the throughput of the proposed system becomes one packet per slot as the reservation probability is raised. We analyze the stability region of S-ALOHA systems with ST for two heterogeneous users and also investigate the stability condition of the system with$N$symmetric users. Finally, we develop a Bayesian-learning backoff algorithm, with which users can control their (re)transmission with real-time estimation on the backlogged users. It is shown that the system with the proposed backoff algorithm can achieve the theoretical limit of the throughput.

For slotted random access systems, the slotted ALOHA protocol provides the maximum throughput of 0.368 (packets/slot) while in the category of splitting (or tree) algorithms, the maximum achievable throughput can reach up to 0.487 with the first-come first-serve (FCFS) algorithm. It has been so far demonstrated that the FCFS algorithm can achieve this maximum throughput only for Poisson traffic. This may limit its application in practical systems, where packet arrivals may not be Poissonian. In this paper, we propose a novel online transmission control framework that introduces random splitting upon collisions and controls the transmission probabilities optimally at each slot by estimating the number of active users in the system. The proposed algorithm is said to be online as it estimates the number of active users slot by slot recursively, and thus can adapt to network dynamics. We first show that the splitting algorithm of our interest can achieve the throughput of 0.532 if the number of users involved in a collision could be known, which serves as a guideline for the upper limit for the random access systems with splitting. Then, when the information on the number of collided users is not available, we show that the proposed algorithm can achieve the maximum throughput of 0.487 for Poisson arrivals while achieving shorter access delay than FCFS. When more bursty traffic than Poisson process is applied, the proposed algorithm shows much better throughput and delay performance than FCFS.

For slotted random access systems with a single channel, the slotted ALOHA (S-ALOHA) protocol shows 0.368 (packets/slot) as the maximum throughput, whereas some splitting (or tree) algorithms exhibit 0.487 (packets/slot). The S-ALOHA protocol has been widely adopted even for multi-channel systems such as Long-Term Evolution (LTE), as it is more practically implementable. However, the throughput of each channel in multi-channel S-ALOHA is limited to 0.368. In order to overcome this limit and some implementational drawbacks of the existing splitting algorithms, this paper proposes a novel splitting algorithm for multi-channel slotted systems which can also adapt to dynamic system situations through estimating the number of users who have packet to transmit. We analyze the throughput of our proposed algorithm and show that the proposed algorithm is more practical than the first-come first-serve (FCFS) algorithm and shows smaller access delay than the FCFS algorithm even for a single channel system. For M-channel systems, the proposed algorithm yields the maximum throughput of 0.487M. Extensive simulations validate our analytical results.

This paper considers non-orthogonal multiple access (NOMA) systems in which users randomly transmit packets in each slot while the transmit power is adjusted so that the receive power of each packet at the base station can be one of two predetermined values. While the NOMA random access system can support simultaneous transmissions from up to two users with different target receive powers, it complicates the access protocol design as each user not only has to optimize the transmission probability but also has to suitably choose the target receive power. By considering the fact that users may have different capability of choosing the target receive power due to their diverse locations in a cell, we propose an online distributed channel access algorithm which enables the users to adaptively choose the target receive power and control the transmission probability over time through observing channel outcomes such as idle, success and collision. Numerical results demonstrate that the proposed algorithm can optimize the system performance in terms of throughput and access fairness.

To move towards low-latency wireless communication while taking care of elastic traffic, proper resource allocation is very important to provide acceptable quality of service (QoS) to all users. This work solve two optimization problems which aim at utility-proportional fairness maximization by optimizing power and bandwidth (BW) allocation among heterogeneous users having real-time and elastic traffic. Noting that both problems are non-convex, local optimal solutions in terms of BW and power are found by alternating optimization algorithm. Numerical results validate analysis, represent schedulability and average utility of real-time traffic user with respect to available resources, and compare utility fairness performance of both schemes.

In this paper, we consider hybrid uplink non-orthogonal multiple access (NOMA) that can support more users by exploiting the notion of power-domain NOMA. In hybrid uplink NOMA, we do not consider centralized power control as a base station (BS) needs instantaneous channel state information (CSI) of all users which leads to a high signaling overhead. Rather, each user is allowed to perform power control under fading in accordance with a truncated channel inversion power control policy. Due to the lack of coordination of centralized power control, users in the same resource block compete for access. To analyze users' behavior, evolutionary game can be considered so that each user can choose transmission strategies to maximize payoff in hybrid uplink NOMA with power control. Evolutionarily stable strategy (ESS) is characterized with fixed costs as well as costs that depend on channel realizations, and it is also shown that hybrid uplink NOMA can provide a higher throughput than orthogonal multiple access (OMA). To update the state in evolutionary game for hybrid uplink NOMA, the replicator dynamic equation is considered with two possible implementation methods.

In low power wide area (LPWA) networks, low-cost end devices (EDs) such as sensors communicate with a central base station (BS) using pure ALOHA protocol. In particular, half-duplex is considered wherein the BS switches from receiving to transmitting mode after receiving a packet successfully from an ED in order to send the acknowledgement. Since the EDs can not know at the moment when the BS turns to the receiving mode, the packets transmitted by the EDs during the receiving mode of the BS are lost. To investigate performance of such half-duplex pure ALOHA systems, this paper develops a semi-Markov (SM) process model for pure ALOHA channel with infinite population. Furthermore, the SM process is approximated with an M/M/1 system in order to analyze the system with finite population. As results, this paper presents how the performance of half-duplex system is influenced by switching time, backoff interval of EDs, and stability criteria of the system, which are verified through system simulations.

This paper proposes efficient backoff algorithms for uplink multi-packet reception (MPR) capable IEEE 802.11 systems in order to maximize the system throughput. According to the proposed algorithms, each station (STN) estimates, in a Bayesian manner under an unsaturated channel traffic condition, the number of backlogged STNs sharing the multiple access channel to obtain an optimal (re)transmission probability. Additionally, an access point and associated STNs cooperate by exchanging information piggybacked in transmitted data packets and the corresponding acknowledgment packets. The mean and variance of the queuing delays of the proposed algorithms are extensively evaluated via simulations under various environments such as time-varying populations and various asymmetric traffic conditions and compared to those of the conventional binary exponential backoff (BEB) algorithm. Furthermore, the queuing performance of the proposed algorithms is compared to the queuing delay lower bound obtained from a system that has perfect knowledge of the backlog size. Numerical results demonstrate the robustness of the proposed algorithms in various environments, and that they outperform the BEB algorithm.

This paper introduces the Cloud-based Radio Access Network (C-RAN) architecture into heterogeneous network (HetNet), in which distributed antennas are connected to a cloud-based baseband processing unit through optical fibers. Among various opportunities realized by this architecture, our focus in this paper is on the spectral efficiency (SE) advantages achieved by cooperative transmission and its associated power consumption that may affect the energy efficiency (EE) of the system. A simple but efficient pre-coding scheme is proposed to reduce the computation complexity of cooperative transmission, thus lowering the associated power consumption, and a detailed power model is then developed to benchmark the various sources of energy consumption in C-RAN. Through detailed simulation, an early performance evaluation of the potentially energy efficient C-RAN implementation was demonstrated.

When multiple users are communicating with an access point based on a random access, the stability region is known as all possible combinations of the mean packet arrival rates to keep their queue lengths bounded. This paper investigates the stability region of p-persistent carrier sense multiple access (CSMA) systems, where two users have different mean packet arrival rates and (re)transmission probabilities. We then extend our results to p-persistent CSMA systems with N users and discuss applicability of our results to IEEE 802.11 systems with the basic and request-to-send/clear-to-send access mechanisms. In numerical studies, we show the stability region by varying system parameters such as packet arrival rates, (re)transmission probabilities, and radio channel errors, and how the stability region of p-persistent CSMA systems gets close to that of a time-sharing system.

In this paper we propose a new Markovian model for p-persistent carrier sense multiple access (CSMA) systems with a finite population of unsaturated single-buffered terminals. Focused on the distribution of the number of backlogged terminals in the steady state, our model allows the optimal persistent probability p from the number of backlogged terminals, which enables us to determine the throughput upper-bound (or mean access delay lower-bound) of slotted CSMA systems. We compare the performance of slotted CSMA systems with binary exponential backoff (BEB) algorithm and with p-persistent protocol against the throughput upper-bound and examine the stability of these systems. We show how closely slotted CSMA systems with BEB algorithm or p-persistent protocol approaches the throughput upper-bound in accordance with the minimum contention window size or the persistent probability p. Further, we propose a generalized Bertsekas' (backoff) algorithm (GBA) based on backlog size estimation, which is a generalization of the existing algorithm proposed by Bertsekas, in order to achieve the throughout upper-bound. Our study shows that in slotted CSMA systems, the access fairness of BEB algorithm is worse than those of p-persistent protocol and GBA algorithm, while the BEB and GBA algorithms show throughput performance close to optimality.

In this paper we examine the feasibility of semi-persistent scheduling (SPS) for voice over IP (VoIP) by random access and evaluate its performance in terms of throughput of random access and traffic channels, and random access delay. We further investigate system stability issues and present methods to stabilize the system. To see the VoIP capacity gain, we show the maximum number of acceptable VoIP terminals without exceeding some front-end packet dropping (i.e., voice clipping) probability. In addition, we examine the effect of the parameter called implicit release after in the LTE standard on the system performance, which is used for silence period detection. Our performance evaluation model based on Equilibrium Point Analysis is compared to simulations.

In this paper, we propose contention resolution algorithms for immediate and deferred first-transmission protocols in a multi-packet reception slotted ALOHA system based on code division multiple access. The system employs a central base station that broadcasts the retransmission probability for each slot to all mobile terminals within its coverage. The base station estimates the system backlog by exploiting cross-layer information on multiple access interference. Based on this information, the retransmission probability is chosen in order to maximize the expectation of the system throughput conditioned on the number of retransmitting terminals. The performance of our algorithms and the system stability are evaluated by theoretical analysis and compared to simulations. Under perfect power control it is shown that our algorithms are stable and their performance very closely approaches the analytical upperbound of the system throughput. Under imperfect power control, the simulation result shows that the algorithms remain robust in maintaining 50% throughput efficiency in a coded system with 2 dB in power control errors.

This paper examines throughput and delay performances of multipacket reception (MPR) slotted ALOHA systems with the exponential backoff (EB) algorithm which consists of an initial transmission probability, exponentially decaying factor and a maximum number of backoff stages. We assume a finite population model and the saturated traffic condition where every terminal always has a packet to transmit. To show the general impacts of the EB algorithm's parameters on the system performance, we consider two MPR channels. In the first channel, all the packets transmitted cannot be successfully received, if the number of packets simultaneously transmitted exceeds a predefined threshold. In the second one, some of packets concurrently transmitted can be probabilistically received (captured). In numerical studies, we show how to adjust the parameters of EB algorithm given the MPR channel in order to achieve close-to-maximal system throughput, and discuss fair channel use.

In this paper, we examine the effect of retransmission cutoff in slotted ALOHA systems employing the binary exponential backoff (BEB) algorithm, in terms of throughput and delay performance, system stability and packet dropping probability. For comparison, the performance of the BEB algorithm without retransmission cutoff is presented. We consider a finite population of users competing for channel access under unsaturated traffic condition. Analytical results are compared against simulations. By dropping packets to keep channel traffic at an acceptable level, BEB with retransmission cutoff improves delay performance at the expense of system throughput.

In this paper, we propose a distributed contention resolution algorithm for multi-packet reception slotted ALOHA systems, which is based on backlog estimation. In the algorithm, a base station broadcasts a binary-valued collision indicator and the identifications of the packets successfully received, when some of packets simultaneously transmitted collide. Then, mobile terminals (MTs) estimate the expected number of MTs involved in the collision given the number of packets successfully transmitted and the collision indicator. Based on this estimation, MTs construct the backlog information and choose their retransmission probability in order to maximize the system throughput. In simulations, the proposed algorithm shows 67% throughput efficiency against the maximum achievable system throughput. The stability of the algorithm is evaluated by examining negative drift from simulation.

In this paper, we propose contention resolution algorithms for multi-packet reception slotted ALOHA systems with immediate- and delayed-first-transmission protocols, in which a base station broadcasts the retransmission probability for each slot to all mobile terminals. As a cross-layer approach, the base station estimates the system backlog by exploiting information on multiple access interference. Based on this information, the algorithm chooses the optimal retransmission probability to maximize the expectation of the system throughput conditioned on the number of retransmitting terminals. The performance of our algorithms and the system stability are evaluated by theoretical analysis and compared to simulations. Results show that our algorithm achieves around 68.3% throughput efficiency under ideal channel conditions.

In this paper we consider a centralized cross-layer splitting algorithm of a multi-packet reception slotted ALOHA system for contention resolution. In the algorithm, a base station estimates the number of backlogged mobile terminals by using a first-order auto-regressive (AR) model based on the number of packets successfully transmitted over previous contention resolution intervals (CRIs). Estimation errors are continuously corrected every slot during a CRI by exploiting multiple access interference (MAI). The retransmission probability broadcasted to MTs every slot is optimized to maximize the system throughput. The performance of this algorithm is analyzed in terms of throughput and delay, and verified by simulations. The algorithm shows around 60% throughput efficiency compared to the maximum achievable throughput.

In this paper we consider a playout buffer with two thresholds based on its occupancy. Playout begins when the occupancy exceeds one threshold, sayLo, while it slows down the playout rate in order to prevent buffer starvation whenever the occupancy drops below the other threshold, sayLu(lesLo). The design parameters here are two thresholds and the reduction factor of the playout rate whose optimal values are sought by minimizing a cost function with constraints. The cost function consists of queueing performance metrics, such as starvation probability, frame loss probability and the quality degradation probability by slowing down playout rate, while the constraints are given as excess preroll delay distribution, excess slow-down playout time distribution and the coefficient of variation of the departure process from the playout buffer. In numerical studies, we present optimal values of those parameters.

In this paper, we examine source rate control (SRC) via multiple buffer thresholds over a Markovian wireless channel. In the SRC scheme, a multimedia source reduces its packet rate (thus degrading the encoding quality) gradually whenever the queue length exceeds certain thresholds. When the queue length drops below a specific threshold, the respective preset source rate is restored. The performance of this scheme is analyzed by defining a loss function of a source in the queueing system, which consists of packet dropping at the end of queue, the throughput degradation and the quality degradation by a source encoder. This function is evaluated by modeling the system as a level dependent M/G/l queue. Numerical examples are presented, which show that the loss function and average packet delay are minimized at an optimal rate reduction factor.

In this paper, we consider the queueing performance of a subscriber station for IEEE802.16e random access protocol with bulk transmissions which is allowed with an ARQ protocol according to resource allocation scheme of a base station or adaptive modulation scheme. The queueing model of this system is modelled by using an M/G/1 type queue with set-up times and exhaustive batch service, which is solved by a generating function approach. Additionally, a piggyback probability in accordance with batch size is also obtained by analyzing a busy period.

We consider a source rate control scheme for a finite buffer of size C in a medium access control layer of a wireless system over a Markovian channel. Our rate control scheme can reduce the mean number of packets produced by a source when the queue length exceed K (K<C), and increase again (by recovering the original source rate) when the queue length drops below L (L

In this paper, we consider the queueing performance of a subscriber station for IEEE802.16e random access protocol with piggyback operation and an ARQ. The random access protocol of IEEE802.16 is based on orthogonal frequency-division-multiple-access and code-division-multiple-access with time division duplexing mode. It is a type of demand-assigned multiple access with piggyback, in which a bandwidth request can be allowed either before transmitting data or at the end of data transmission. The queueing model of our interest is an M/G/l type queue with set-up times and exhaustive service, which is solved by a generating function approach. From the queueing analysis, we obtain the piggyback probability and the output process from the queue. The performance is presented in terms of the first and second moments of queue-size by varying number of subscriber stations in a cell, number of PN codes for bandwidth request ranging and number of slot-subchannels.

In this paper, we consider the transient queueing response of IEEE802.16 random access protocol with piggyback and automatic repeat request. The random access protocol of IEEE 802.16 is physically based on orthogonal frequency- division-multiple-access (OFDMA)-code-division-multiple-access (CDMA) with time division duplexing (TDD) mode. In medium access control (MAC) layer, the protocol is a type of demand- assigned multiple access (DAMA) with piggyback, in which a bandwidth request can be allowed either before transmitting data or at the end of data transmission. The model of our interest is an M/G/l type queue with set-up times and exhaustive service, which is solved by a generating function approach. In the analysis, we obtain the time dependent mean queue length, the time dependent empty probability of the queue and the time dependent busy period. The random access success probability is derived from the system equilibrium. Retransmission probability is also derived by including a binary exponential backoff algorithm.

In this paper, we consider the queueing performance of periodic polling services for VoIP in an IEEE802.16 system, which is modeled by an MIGIC type queue with periodic batch service. We carry out an analysis for this system, using a generating function approach with supplementary variable technique. As a service discipline, the size of batch, the transmission duration in units of frames, i.e., contiguous frames, and the vacation period correspond to the bandwidth assigned to a subscriber station and the period to poll other subscriber stations, respectively. In numerical examples, the mean queue lengths are given, according to various system parameters.

In this paper, we investigate the performance of IEEE802.16d random access protocol based on orthogonal code-division-multiple-access and frequency-division-multiple-access in time division duplexing mode using equilibrium point analysis. The random access protocol of IEEE802.16d is similar to demand-assigned multiple access with piggyback, in which bandwidth request is allowed at the end of data transmission. In the analysis, we consider the retransmission probability derived from a binary exponential backoff algorithm. The performance is presented in terms of initial access delay, its throughput, message transmission delay and the system throughput with piggyback by varying number of subscriber stations, number of PN codes for bandwidth request ranging, number of slot-subchannel and the parameters of the binary exponential backoff algorithm.

In this paper, we consider the queueing performance of IEEE802.16 random access protocol for sporadic data transmission with a binary exponential backoff algorithm. The random access protocol of IEEE 802.16 is physically based on orthogonal frequency-division-multiple-access and code- division-multiple-access with time division duplexing mode, i.e., multichannel- and multicode- slotted Aloha. In a medium access control layer, the protocol is a type of demand-assigned multiple access with and without piggyback, in which a bandwidth request may be allowed either before transmitting data or at the end of data transmission. We analyze the queueing performance of the random access protocol without piggyback, which is non- exhaustive service of an M/G/1 type queue with set-up times by taking a binary exponential backoff mechanism into consideration. The performance is presented according to traffic load, number of subscriber stations and retransmission probability.

In this paper, we consider a capacity allocation scheme of periodic polling services for a multimedia traffic in an IEEE 802.16 system. In the scheme of our interest, a base station assigns a subscriber station contiguous M uplink subframes for uplink traffic transmission and v vacation frames for saving power and opportunities for other subscriber stations. Among M uplink subframes, the subscriber station returns its remaining frames, when its queue is empty. The returned bandwidth will then be allocated to other subscriber stations. We carry out an analysis on this queueing system, by using a generating function approach. The performance of the proposed capacity allocation scheme is presented in terms of the first moment of queue length, the mean vacation and transmission period. We also compare its performance with that of a deterministic capacity allocation scheme, in which allocated bandwidth would not be returned

In this paper, we capture the impact of an ARQ scheme in the MAC/LLC layer with a forward error-correcting code on upper-layer packet transmissions over a Markovian channel by examining the performance of a single service data unit which may be either a TCP segment or a UDP packet. We assume that the ACK/NACK delay of ARQ blocks belong to a single service data unit is less than the transmission period of each ARQ block. The performance measures of a single service data unit transmission are derived in terms of the transmission success and failure probabilities given a retransmission timeout value, retransmission timeout probability and the moments of transmission delay. Further, the queueing performance of a service data unit transmitter is also presented by varying average received signal-to-noise ratio, mobile velocity, number of ARQ blocks needed to transmit a single service data unit, number of retransmissions for each ARQ block and error-correcting capability applied to an ARQ block. From numerical examples, renegotiation between a serving base station and a mobile station is needed, when the mobile station is far from the serving base station in order that a service data unit is fragmented into a larger number of ARQ blocks in order to apply high error-correcting capability for each one.

In this paper, the queueing performance of a type-II hybrid ARQ (automatic repeat request) scheme in a TDMA system over a Markovian channel is analyzed by assuming infinite queue size. The system of interest can be described by an embedded Markov chain of M/G/1 type, which can be solved by a well-known matrix analytic method of M/G/1 type. In the numerical examples, the mean delay and the packet dropping probability obtained using the infinite queue assumption are examined under varying the mobile velocity, average received signal-to-noise ratio (SNR) and coding rate.