For supporting ever-growing demands on low-latency services in Machine-to-Machine (M2M) communications, it is crucial to optimize the delay performance of random access. To that end, much effort has been made on the optimal tuning of access parameters with a given access strategy. For further optimization of access strategy, learning-based approaches may have great potential, with which nodes learn the best access strategy based on their own observations and experience. Yet how to leverage learning-based approaches for delay optimization has remained largely unexplored. In this paper, three main challenges in delay-optimal learning-based access design are identified and tackled for sensing-free random access networks. First of all, the minimum mean queueing delay with central coordination is derived, which serves as a delay bound for the distributed case. For each node independently making decisions, aQ-learning algorithm, DORA-GS, is proposed, and demonstrated to be able to achieve the delay bound if each node has the global information about the occupancy of all the others’ queues. When such information is unavailable, a Multi-Armed-Bandit (MAB)-based access scheme, DORA-MAB, is further developed, with which finite mean queueing delay can be achieved for the whole input rate region of (0, 1). Gains over the existing representative sensing-free random access schemes are shown to be significant especially when the aggregate input rate is high.

Clustered cell-free networking has appeared as a promising technique to overcome the cell-edge problem in cellular networks. This is achieved by dynamically decomposing the whole network into a number of non-overlapping weakly-interfered subnetworks and conducting joint processing among the base-stations (BSs) in the same subnetwork. For optimizing clustered cell-free networking, i.e., network decomposition, existing works usually assumed that all the BSs are involved in signal transmission/reception. However, switching a BS far away from user equipments (UEs) into sleep mode could improve the data rate due to the reduced inter- Ueinterference. In this paper, we aim to maximize the downlink sum rate of the network by optimizing clustered cell-free networking while considering dynamic BS sleeping. By decomposing the problem into a BS assignment subproblem and a UE assignment subproblem, an alternating optimization based clustered cell-free networking (AO-C2F -Net) algorithm is proposed. Simulation results corroborate that enabling BS sleeping improves the sum rate performance. In addition, the proposed AO-C2F -Net algorithm outperforms existing benchmarks in terms of optimizing both clustered cell-free networking and BS sleeping.

For delay-sensitive applications, codewords become outdated with a diminishing amount of information as they age. In this paper, the above information aging process is incorporated into the characterization of information output rate, a combined measure of information timeliness and reliability. The analysis shows that by optimally tuning the information encoding rate of codewords, the maximum information output rate may significantly drop when the information ages fast, indicating a high penalty caused by the information aging process.

At the core of stability analysis of random-access networks is the characterization of the stability region of input rates and the operating region of transmission probabilities for achieving stability. In Dai (2022), a theoretical framework was proposed to tackle the above long-standing issues for the sensing-free Aloha case. In this paper, the analysis is extended to Carrier Sense Multiple Access (CSMA) to demonstrate the effects of carrier sensing on the stability performance. CSMA fundamentally differs from Aloha in that nodes need to sense the channel, which takes time. Meanwhile, they are closely related as given that the channel is idle, the contention process of nodes in a CSMA network is identical to that in an Aloha network. That leads to an interesting mapping between the stability regions with CSMA and Aloha. The analysis shows that with CSMA, the service rates of nodes’ queues, stability region of input rates, and complete operating regions of initial transmission probabilities for given backoff function are all crucially determined by the normalized sensing time and the failed transmission time. When the normalized sensing time is large, the stability region could be smaller than that with Aloha, indicating that carrier sensing may not always be beneficial for short-packet transmissions.

The channel-capture phenomenon has long been observed and deemed undesirable in random access networks. When it occurs, one node would monopolize transmission for an extended period despite the contention from others, leading to high network throughput but serious short-term unfairness among nodes. Harnessing the throughput gains without compromising the fairness requirements requires a comprehensive understanding of the throughput-fairness tradeoff caused by the channel-capture phenomenon, which unfortunately cannot be accounted for by the existing analytical models. The problem is rooted in a key assumption of state-independent probability of successful transmission of Head-Of-Line (HOL) packets that is adopted in the existing models, but no longer holds when the channel-capture phenomenon occurs. In this paper, by incorporating capture states into the HOL-packet model for slotted Aloha, the probability of successful transmission of HOL packets is shown to be crucially dependent on whether they are in a capture or non-capture state. The analysis reveals that with the simplest collision receiver, the maximum network throughput of slotted Aloha can reach 1 as long as the number of capture states is no smaller than 2. The short-term fairness, on the other hand, would be worsened with more capture states. The optimal tradeoff between network throughput and short-term fairness is characterized, and shown to be achieved by the proposed Double-Capture Backoff.

Slotted Aloha has been widely adopted in various communication networks. Yet if the transmission probabilities and traffic input rates of transmitters are not properly regulated, their data queues may easily become unstable. For stability analysis of Aloha networks with multiple transmitter-receiver (T-R) pairs, the focus of previous studies has been placed on the maximum input rate of each transmitter, below which the network is guaranteed to be stabilized under any given topology. By assuming a fixed and identical transmission probability across the network, however, network stability is found to be unachievable when the input rate exceeds zero. As we will demonstrate in this paper, the key to stabilizing the network lies in proper selection of transmission probabilities according to the traffic input rates and locations of all transmitters and receivers. Specifically, for an Aloha network with multiple capture receivers, by establishing and solving the fixed-point equations of the steady-state probabilities of successful transmissions of Head-of-Line packets, the exact service rates of all transmitters’ queues are obtained, based on which the operating region of transmission probabilities for achieving stability and the stability region of input rates are further characterized. The results are illustrated in various scenarios of multi-cell and ad-hoc networks. Simulation results validate the analysis and corroborate that the network can be stabilized as long as the traffic input rates are within the stability region, and the transmission probabilities are properly adjusted according to the traffic input rates and network topology.

With the ever-growing demand for low-latency services in machine-to-machine (M2M) communications, the delay performance of random access networks has become a primary concern, which critically depends on the sensing capability of nodes. To understand the effect of sensing on the optimal delay performance, the challenge lies in unifying the delay analysis of sensing-free Aloha and sensing-based Carrier Sense Multiple Access (CSMA) with various design features such as backoff and connection-free or connection-based. In this paper, based on a unified analytical framework, the mean queueing delay of data packets with Aloha and CSMA is characterized and optimized, with which the upper-bound of sensing time for CSMA to outperform Aloha in terms of the minimum mean queueing delay is further obtained. The analysis is also applied to the Random Access-Based Small Data Transmission (RA-SDT) schemes in 5G networks to investigate when and how significant their delay performance can be improved by sensing, which sheds important insights into practical access protocol design.

For large-scale wireless networks, the optimal network decomposition has been proposed from a graph theoretical perspective to maximize the number of decomposed subnetworks with an interference constraint. When each user is associated with multiple base-stations (BSs) to form its virtual cell, however, the optimal network decomposition may lead to highly unbalanced subnetwork sizes if the key system parameters are not properly set. In this paper, an analytical framework is proposed to characterize the distribution of subnetwork size in terms of the number of users in a randomly chosen subnetwork for virtual-cell-based optimal decomposition. The analysis reveals that the distribution of subnetwork size is crucially determined by the virtual cell size, i.e., the number of BSs associated with each user, and the ratio of the number of users to the number of BSs, both of which should be carefully tuned to produce balanced subnetworks.

Clustered cell-free networking has been considered as an effective scheme to trade off between the low complexity of current cellular networks and the superior performance of fully cooperative networks. With clustered cell-free networking, the wireless network is decomposed into a number of disjoint parallel operating subnetworks with joint processing adopted inside each subnetwork independently for intra-subnetwork interference mitigation. Different from the existing works that aim to maximize the number of subnetworks without considering the limited processing capability of base-stations (BSs), this paper investigates the clustered cell-free networking problem with the objective of maximizing the sum ergodic capacity while imposing a limit on the number of user equipments (UEs) in each subnetwork to constrain the joint processing complexity. By successfully transforming the combinatorial NP-hard clustered cell-free networking problem into an integer convex programming problem, the problem is solved by the branch-and-bound method. To further reduce the computational complexity, a bisection clustered cell-free networking ($\text {B}\text {C}^{2}\text {F}$-Net) algorithm is proposed to decompose the network hierarchically. Simulation results show that compared to the branch-and-bound based scheme, the proposed$\text {B}\text {C}^{2}\text {F}$-Net algorithm significantly reduces the computational complexity yet achieves nearly the same network decomposition result. Moreover, our$\text {B}\text {C}^{2}\text {F}$-Net algorithm achieves near-optimal performance and outperforms the state-of-the-art benchmarks with up to 25% capacity gain.

Slotted Aloha has been widely adopted in various communication systems, while its stability performance has long been observed as being sensitive to the setting of transmission probabilities. For stability analysis of Aloha networks with multiple transmitter-receiver (T-R) pairs, the focus of previous studies has been placed on characterization of the maximum input rate of T-R pairs, below which the network can be stabilized under any given topology. With a fixed and identical transmission probability for all T-R pairs, nevertheless, network stability is often found to be unachievable, as part of transmitters would become unstable regardless of how small the traffic input rate is. As we will demonstrate in this paper, to stabilize the whole network, transmission probabilities of T-R pairs should be properly adjusted according to their traffic input rates and locations. Specifically, by establishing the fixed-point equations of the steady-state probabilities of successful transmissions of Head-of-Line (HOL) packets, the service rates of transmitters' queues can be obtained, based on which the operating region of transmission probabilities for achieving stability can further be characterized. Simulation results corroborate that all the T-R pairs can be stabilized by choosing transmission probabilities from the region, which highlights the importance of proper transmission control based on the traffic input rates and network topology for Aloha networks.

With massive access of Machine-to-Machine (M2M) communications, the delay performance of random access networks has become a major concern, which crucially depends on the access parameter setting and sensing capability of nodes. To facilitate the increasing demand for low-latency services, it is of vital importance to study how to optimize the delay performance by tuning the access parameters, and understand which type of random access, sensing-free Aloha or sensing-based Carrier Sense Multiple Access (CSMA), performs better in terms of the optimal delay performance. In this paper, the minimum mean queueing delay of data packets and the corresponding optimal transmission probability of each node are derived for Aloha and CSMA, based on which the upper bound of sensing time for CSMA to outperform Aloha in terms of the minimum mean queueing delay is characterized. The analysis is applied to the 2-step Random Access-based Small Data Transmission (RA-SDT) scheme in the Fifth-Generation (5G) system, and offers important insights for the optimal access design of M2M communications.

Slotted Aloha provides a simple solution for facilitating the massive access of machine-type-devices (MTDs). To prolong the battery lifetime of MTDs, on-demand sleep mechanisms are usually adopted, with which the lifetime and delay performance of MTDs crucially depends on the access and sleep parameters, and may significantly deteriorate if they are not properly selected. In this article, an analytical framework is proposed for on-demand-sleep-based slotted Aloha to optimize the lifetime and delay performance. Specifically, by establishing a novel node-centric model, explicit expressions of the expected lifetime of nodes and the mean queueing delay of data packets are derived, based on which the transmission probability of each node for maximizing the expected lifetime and minimizing the mean queueing delay is obtained. The tradeoff between the optimal lifetime and delay performance is also characterized. The practical insights of the analysis are further demonstrated by conducting a case study on the 2-step random-access-based small data transmission (RA-SDT) scheme with mobile initiated connection only (MICO)-based on-demand sleep mechanism.

Delay and information transmission rate are two fundamental quantities in communication systems. The information-theoretic capacity provides the ultimate limit of the transmission rate for reliable communications, yet with the effect of delay either completely ignored or partially considered. How to incorporate delay constraints into capacity characterization is one of the long-standing fundamental problems in wireless communications, which has gained a renewed sense of urgency with the increasingly demanding requirements for high data rate and low latency. The challenge lies in establishing an analytical framework that can integrate the accurate characterization of delay with the information-theoretic capacity. In this paper, by modeling the information flow as a queue of codewords, we demonstrate that the service process is the key that associates both the information output rate and the access/queueing delay performance. For a given mean-access-delay constraint, the maximum information output rate is characterized in various conditions in fading channels. The analysis shows that the mean-access-delay constraint has a crucial impact on the maximum information output rate when it is below a certain threshold. To achieve the maximum information output rate, the information encoding rate needs to be carefully tuned according to the delay constraint, which diminishes as the constraint becomes more strict. Discussions on how to incorporate feedback overhead and other delay constraints including the mean queueing delay and access delay deadline into the rate analysis are also presented.

According to whether a connection to the receiver is established before data transmission, random access schemes can be divided into two categories: connection-based and connection-free. For cellular networks, its connection-based access mechanism has been heavily criticized for being ill-suited to Machine-to-Machine (M2M) communications. With the existing studies overwhelmingly focusing on the physical-layer transmission and detection design of various connection-free access schemes, less attention has been paid to the fundamental understanding of when connection establishment could be beneficial. The challenge originates from the lack of proper modeling of connection-based random access. In this paper, a scalable model that incorporates each node’s queueing behavior is proposed for connection-based slotted Aloha, based on which both the data throughput and the mean queueing delay of data packets are characterized, optimized, and compared with those for connection-free slotted Aloha. Conditions for beneficial connection establishment in terms of the optimal throughput and queueing delay performance are discussed and applied to cellular networks with the newly introduced Random Access-based Small Data Transmission (RA-SDT) schemes. The analysis offers important insights for the optimal access design of M2M communications.

As one of the most representative random-access schemes, slotted Aloha has been adopted in various wireless communication networks. A multi-cell Aloha network may easily become unstable as the inter-cell interference grows if the traffic input rates and transmission probabilities of nodes are not properly regulated. Yet how to stabilize a multi-cell Aloha network has remained largely unknown. To address the above open issue, an analytical framework is proposed in this paper for multi-cell Aloha networks to characterize the stability region of traffic input rates and operating region of transmission probabilities of nodes for achieving network stability. Specifically, the inter-cell interference level is captured by the overlapping ratio of each cell, and shown to be a key factor that determines the stability performance. For a two-cell Aloha network, the stability region of input rates and complete operating regions of transmission probabilities are obtained as functions of the overlapping ratios of cells. For the general$M$-cell case, a transmission control algorithm is further proposed to stabilize the network only based on the local information exchange between neighboring cells, with effectiveness demonstrated through simulations.

By moving to millimeter wave (mmWave) frequencies, base stations (BSs) will be densely deployed to provide seamless coverage in sixth generation (6G) mobile communication systems, which, unfortunately, leads to severe cell-edge problem. In addition, with massive multiple-input-multiple-output (MIMO) antenna arrays employed at BSs, the beamspace channel is sparse for each user, and thus there is no need to serve all the users in a cell by all the beams therein jointly. Therefore, it is of paramount importance to develop a flexible clustered cell-free networking scheme that can decompose the whole network into a number of weakly interfered small subnetworks operating independently and in parallel. Given a per-user rate constraint for service quality guarantee, this paper aims to maximize the number of decomposed subnetworks so as to reduce the signaling overhead and system complexity as much as possible. By formulating it as a bipartite graph partitioning problem, a rate-constrained network decomposition (RC-NetDecomp) algorithm is proposed, which can smoothly tune the network structure from the current cellular network with simple beam allocation to a fully cooperative network by increasing the required per-user rate. Simulation results demonstrate that the proposed RC-NetDecomp algorithm outperforms existing baselines in terms of average per-user rate, fairness among users and energy efficiency.

Base-stations (BSs) will be densely deployed to provide seamless coverage in sixth generation (6G) mobile communication systems, which, unfortunately, leads to severe cell-edge problem. A flexible clustered cell-free networking scheme to replace the cellular network is studied in this paper, which decomposes the whole network into a number of subnetworks operating independently. In order to reduce signaling overhead and system complexity as far as possible, we aim to maximize the number of decomposed subnetworks with a per-user rate constraint for service quality guarantee. In addition, subnetworks with BSs only are allowed to enable BS sleep mode operation. A rate-constrained network decomposition (RC-NetDecomp) algorithm is proposed, which can smoothly tune the network structure from the current cellular network to the fully cooperative network by varying the required per-user rate. Simulation results demonstrate that it outperforms the existing baselines in terms of both average per-user rate and fairness among users.

As one of the two fundamental types of multiple access, random access has been widely adopted in various communication networks, and expected to play an increasingly central role owing to the rising popularity of Machine-to-Machine (M2M) communications. Despite decades of successful applications, the theory of random access has long been underdeveloped, with key fundamental issues unresolved. Among them, stability of random-access networks is the most long-standing one that has received continuous attention for almost half a century. The challenge lies in establishing an analytical framework where the coupled service processes of nodes’ queues can be characterized. In this paper, by extending our previously proposed analytical framework from the symmetric scenario to the general one, we tackle three open questions: 1) How to characterize the coupled service rates of nodes? 2) How to determine the stability region of input rates, only within which the network can be stabilized? 3) For given input rates within the stability region, how to tune the transmission probabilities of nodes to stabilize the network? We demonstrate that the key to characterizing the coupled service rates lies in properly establishing and solving the fixed-point equations of steady-state probabilities of successful transmission of Head-of-Line (HOL) packets of nodes. For the stability region of input rates, which closely depends on the definition of stability, two types of stability, i.e., queue-stability and throughput-stability, are considered, and both stability regions are shown to be determined by the sufficient and necessary condition of the existence of positive real roots of the fixed-point equations. To characterize the operating regions of transmission probabilities, constraints need to be further developed to ensure that the network operates at the specific steady-state point. The analysis shows that to stabilize the network, the transmission probabilities of nodes can be tuned only based on their long-term traffic input rates. Although the main results are illustrated based on Aloha with Constant Backoff, discussions on how to incorporate a general backoff function and other features of random access are also presented.

With the emerging unlicensed spectrum sharing and Machine-to-Machine communications, the coexistence performance of multiple devices with different access schemes operating at the same unlicensed bands has received significant research interests. As the two most representative random-access schemes, Aloha and Carrier Sense Multiple Access (CSMA) both found wide applications in unlicensed bands. Yet most of the studies have focused on the coexistence of CSMA-based networks, leaving the coexistence of Aloha-based and CSMA-based networks largely unexplored. The challenge originates from the lack of a coexistence model of slotted Aloha and CSMA. In this paper, the throughput performance of coexisting Aloha and CSMA networks is characterized and optimized by extending a unified analytical framework proposed for random-access networks. The analysis shows that different from the single-network case where the maximum throughput of CSMA is much higher thanks to carrier sensing, when Aloha and CSMA coexist, Aloha would significantly outperform CSMA if each network optimizes its own throughput performance without cooperation, leading to poor throughput performance for CSMA and severe unfairness. To achieve fair coexistence, inter-network cooperation is crucial. The optimal transmission probabilities of Aloha and CSMA for maximizing the total network throughput under a given throughput ratio are further derived, and applied to coexisting LTE Unlicensed and WiFi networks to optimize their coexistence performance.

This paper focuses on the outage probability of access delay of Machine-to-Machine (M2M) communications in cellular networks, which is an important performance indicator for Ultra-Reliable and Low-Latency Communication (URLLC). Specifically, by deriving the outage probability for given maximum allowable access delay as a function of system parameters, the outage probability is minimized by optimally tuning the Access Class Barring (ACB) factor. For given outage probability bound, the admission control and resource allocation for the random access channel are further discussed, where the maximum number of Machine-Type Devices (MTDs) that can be admitted with given number of preambles and the minimum number of preambles that should be allocated with given network size are obtained. Compared to the standard setting where the ACB factor is fixed, significant gains in outage probability are demonstrated by optimally tuning the ACB factor according to the number of MTDs and the traffic input rate of each MTD. It is also shown that for given required outage probability bound, the optimal tuning of ACB factor enables much more MTDs to be admitted for given preamble resource, and requires much fewer preambles for given network size.

Slotted Aloha provides a simple way for accommodating the massive access of Machine-to-Machine (M2M) communications. Yet, the delay performance of slotted Aloha has long been observed to significantly deteriorate as the network size grows. It is therefore important to study how to optimize the delay performance of slotted Aloha in a large-scale network. This paper focuses on the optimization of access delay of a buffered slotted Aloha network, where n nodes transmit to a common receiver in fading channels. Specifically, by deriving the closed-form expressions of the network steady-state points in both unsaturated and saturated conditions, the first and second moments of access delay of each packet are obtained as explicit functions of system parameters, and minimized by optimizing the transmission probability of each node. The analysis shows that to achieve the minimum mean access delay, the transmission probability of each node should be reduced as the network size increases, leading to a diminishing node data rate unless the information encoding rate is jointly optimized. The minimum mean access delay for a given data rate requirement is further characterized, and effects of key parameters such as the minimum required data rate for each node, the mean received signal-to-noise ratio of each packet and the number of nodes on the rate-constrained minimum mean access delay are discussed. The practical insights of the analysis are also demonstrated by taking the example of an LTE-M system with smart grid applications.

Although millimeter wave (mmWave) communications can well support high-data-rate transmissions, the inherent shortcomings, e.g., high path loss and sensitivity to blockage, may cause severe outage problems if the network is not configured properly. This paper aims to minimize the long-term outage probability of an mmWave communication network by optimizing the base station (BS) deployment and user association. For the BS deployment problem, existing works usually assumed that the positions of users are fixed and formulated it as a deterministic optimization problem. With the time-varying nature of positions of user equipments (UEs) taken into account, we establish a stochastic optimization framework for BS deployment optimization. The objective is to maximize the average number of physically accessible BSs of each UE under an inaccessible probability constraint, and a cooperative stochastic approximation (CSA)-based algorithm is developed to effectively search the optimal positions of BSs. For user association, our focus is to properly associate UEs with BSs to minimize the outage probability with balanced workloads among BSs. Combined with the proposed user association scheme, the proposed BS deployment scheme can significantly improve the network outage probability in the long term, especially when the aggregation degree of UEs is large.

Although many efforts have been devoted to the access point (AP) placement problem in IEEE 802.11 networks, most of them assumed that the positions of users are fixed and formulated it as a deterministic optimization problem. In this paper, with the time-varying nature of users' positions taken into account, we establish a stochastic optimization framework to jointly optimize the positions and coverage radius of APs. The aim is to maximize the average network throughput under an outage probability constraint. With both the objective and constraint functions in the form of expectation, a novel algorithm is developed based on the recently proposed cooperative stochastic approximation (CSA). Simulation results show that the proposed algorithm can significantly improve the average network throughput, especially when the outage constraint becomes loose or the aggregation degree of users increases.

The deployment of Long Term Evolution (LTE) networks in unlicensed spectrum is a promising solution to overcome the scarcity of licensed spectrum. Yet it has been widely observed that severe unfairness and performance degradation would occur when LTE coexists with WiFi, the incumbent user of unlicensed bands, without proper adjustment. Fair and efficient coexistence of these two networks thus becomes crucial. It, nevertheless, remains largely unknown how to optimize the total throughput of the LTE and WiFi networks under fairness constraints. To address the above open issue, this paper considers that a WiFi network coexists with an LTE network using the Category 3 or Category 4 Listen-Before-Talk (LBT) mechanism, and aims to characterize the maximum total throughput of the LTE and WiFi networks under two fairness constraints including throughput fairness and 3GPP fairness. The analysis shows that the maximum total throughput is independent of which LBT mechanism the LTE network adopts, and can be improved as the mean successful transmission time of the LTE network increases. Explicit expressions of the optimal initial backoff window sizes to achieve the maximum total throughput under both throughput fairness and 3GPP fairness are also derived, which shed important light on the practical network design. It is found that the initial backoff window size of the LTE network should be enlarged as the mean successful transmission time of the LTE network increases, indicating that for fair and efficient coexistence with a WiFi network, the LTE network needs to access the unlicensed channel infrequently with large packets. To facilitate implementation in practice, distributed schemes are further proposed, with which WiFi and LTE can optimally adjust the backoff window sizes based on their own observation and estimation without the need of coordination between these two networks.

A structured random matrix ensemble that maintains constant modulus entries and unit-norm columns, often called a random phase-rotated (RPR) matrix, is considered in this letter. We analyze the coherence statistics of RPR measurement matrices and apply them to acquire probabilistic performance guarantees of orthogonal matching pursuit (OMP) for support detection (SD). It is revealed via numerical simulations that the SD performance guarantee provides a tight characterization, especially when the signal is sparse.

Aimed at providing high data rate in wireless local area networks (WLANs), the IEEE 802.11ac standard has been developed with key enhancements, including increasing the transmission rate and enlarging the packet payload length. The improvement in the sum rate performance, nevertheless, could become marginal or even disappear when nodes of legacy 802.11a/n standards coexist. It is, therefore, of paramount importance to study how to optimize the network sum rate of a multi-standard WLAN. In this paper, a multi-group model is proposed to analyze the data rate performance of a multi-standard WLAN where nodes with different standards have distinct transmission rates and packet payload lengths. It is shown that the packet payload length is a key system parameter that has a crucial impact on both the network sum rate and the ratio of node data rates. The enhancement proposed in the latest 802.11ac standard on enlarging the packet payload length can improve the data rate performance of its own nodes, but it may lead to the starvation of the legacy 802.11a/n nodes, and even impair the sum rate performance. To maximize the network sum rate with given target ratios of node data rates, the optimal packet payload lengths with or without joint tuning of the initial backoff window sizes are further obtained, which shed important light on the optimal network design of WLANs.

A fundamental difference between the two most representative random-access schemes, Aloha and carrier sense multiple access (CSMA), is sensing. There has been a common belief that the access efficiency can always be improved by the use of carrier sensing, which is indeed based on an implicit assumption that the packet length is much larger than the sensing time. For machine-to-machine (M2M) communications featured with short packets, the benefit of sensing may not overweigh the cost any more. It is therefore of paramount importance to identify the conditions for CSMA to outperform Aloha. In this paper, the sum rate performance of CSMA networks with two representative receiver structures, i.e., the collision model and the capture model, is characterized and optimized, based on which a comparative study of the optimal sum rate performance between Aloha and CSMA is conducted to establish criteria for beneficial sensing. The analysis shows that the maximum sum rates of CSMA with both receiver structures logarithmically increase with the mean received SNR$\rho $at the high SNR region, and the rate gain of the capture model over the collision model is significant only when$\rho $is small. The critical threshold for the ratio of the sensing time to the packet length for beneficial sensing is characterized under various scenarios, and found to be close to zero at the low SNR region when the capture model is adopted, indicating that the packet length needs to be extremely large for CSMA to outperform Aloha in that case. The analysis sheds important light on the access design of M2M communications, and suggests that Aloha could be a more favorable option when short packets are sent by a massive amount of low-power machine-type devices.

Different from the conventional packet-based random access schemes where each data packet needs to contend for channel access, with connection-based random access, a connection is first established before data packet transmission. Despite the consensus that there exists a critical threshold of the data packet transmission time, only above which establishing a connection is beneficial, characterization of such a threshold has received little attention. In this paper, a comparative study will be presented on the optimal throughput performance of the packet-based random access and the connection-based random access to characterize criteria for beneficial connection establishment. Based on a unified channel-centric model, explicit expressions of the maximum effective throughput are obtained for both packet-based and connection-based Aloha and Carrier Sense Multiple Access (CSMA). The analysis shows that whether connection establishment is beneficial crucially depends on the sensing capability of nodes. The threshold of data packet transmission time with Aloha is found to be much lower than that with CSMA, indicating that the throughput gain brought by connection establishment is more significant when sensing is absent. The analysis sheds important light on the access design of machine-to-machine (M2M) communications.

This is a sequel of our previous work [20] on access throughput optimization of Machine-to-Machine (M2M) communications in Long Term Evolution (LTE) networks. By incorporating a finite data transmission rate, this paper aims to characterize the effect of data transmission on the optimal access performance of Machine-Type Devices (MTDs). Specifically, both the maximum access throughput and the corresponding optimal Access Class Barring (ACB) factor are obtained as explicit functions of the data transmission rate, which show that even with the ACB factor optimally tuned, the access throughput may deteriorate as the number of MTDs increases, and even drop to zero if the data transmission rate is too small. To boost the data transmission rate, more resources should be allocated to data transmission, which, however, leads to fewer chances for access. In light of the tradeoff between the data transmission rate and the access frequency, the time slot length is further optimized for maximizing the normalized maximum access throughput. Simulation results corroborate that by properly choosing the time slot length, substantial gains can be achieved over the default setting in various scenarios.

This is a sequel of our previous work on characterization of maximum sum rate of slotted Aloha networks. By extending the analysis to incorporate the capacity-achieving receiver structure, successive interference cancellation (SIC), this paper aims to identify the rate loss due to random access. Specifically, two representative SIC receivers are considered, i.e., ordered SIC, where packets are decoded in a descending order of their received power, and unordered SIC, where packets are decoded in a random order. The maximum sum rate and the corresponding optimal parameter setting including the transmission probability and the information encoding rate in both cases are obtained as the functions of the mean received signal-to-noise ratio (SNR). The comparison to the capture model shows that the gains are significant only with the ordered SIC at moderate values of the mean received SNR ρ. With a large ρ, the rate gap diminishes, and they all have the same high-SNR slope of e-1, which is far below that of the ergodic sum capacity of fading channels. The effect of multipacket reception (MPR) on the sum rate performance is also studied by comparing the MPR receivers including SIC and the capture model to the classical collision model.

A key challenge for enabling machine-to-machine (M2M) communications in long-term evolution (LTE) networks is the intolerably low access efficiency in the presence of massive access requests. To address this issue, a new analytical framework is proposed in this paper to optimize the random access performance of the M2M communications in LTE networks. Specifically, a novel double-queue model is established, which can both incorporate the queueing behavior of each machine-type device (MTD) and be scalable in the massive access scenarios. To evaluate the access efficiency, the network throughput is further characterized, and optimized by properly choosing the backoff parameters including the access class barring (ACB) factor and the uniform backoff (UB) window size. The analysis reveals that the maximum network throughput is solely determined by the number of preambles, and can be achieved by either tuning the ACB factor or the UB window size based on statistical information such as the traffic input rate of each MTD. Simulation results corroborate that with the optimal tuning of backoff parameters, the network throughput can remain at the highest level regardless of how many MTDs in the network, and is robust against feedback errors of the traffic input rate and burstiness of data arrivals.

The recently released IEEE 802.11ac standard has implemented enhancements including higher maximum transmission rate and larger maximum packet payload length. In wireless local area networks (WLANs) where the latest 802.11ac nodes and the legacy ones coexist, nevertheless, the effects of the enhancements on the data rate performance remain largely unknown. In this paper, we tackle this open problem. The analysis shows that with a growing transmission rate of 802.11ac nodes, the data rates of all the nodes in the network can be improved. If the packet payload length of 802.11ac nodes grows, on the other hand, the data rate of each 802.11ac node increases while the other coexisting nodes' are degraded. To avoid starvation, we further study how to adaptively tune system parameters to optimize the network sum rate under a certain service differentiation requirement among distinct groups. Explicit expressions of the maximum network sum rate and the optimal packet payload lengths are obtained, and verified by simulation results.

A key challenge for enabling Machine-to-Machine (M2M) communications in Long Term Evolution (LTE) networks is the intolerably low access efficiency in the presence of massive access requests. To address this issue, a new analytical framework is proposed in this paper to optimize the random access performance of M2M communications in LTE networks. Both the maximum network throughput and the corresponding optimal backoff parameters including the Access Class Barring (ACB) factor and the backoff window size are obtained as explicit functions of key system parameters such as the number of preambles, the number of Machine Type Devices (MTDs) and the aggregate input rate. The analysis is verified by simulations and sheds important light on practical network design for supporting massive access of M2M communications in LTE networks.

For IEEE 802.11 networks with multiple basic service sets (BSSs), most studies have focused on how to allocate different frequency sub-channels to BSSs for minimizing the co-channel interference. With the significant increase of the sub-channel bandwidth, however, it becomes increasingly important to study the network performance with universal frequency reuse. In this paper, we focus on an uplink M-BSS IEEE 802.11 network, where all the BSSs share the frequency band rather than operate at different sub-channels. By dividing the nodes in each BSS into multiple groups according to the set of access points (APs) they can be heard by, the steady-state points of M BSSs in saturated conditions are obtained as the functions of the number of nodes in each group and the initial backoff window size of nodes of each BSS. The maximum network throughput is further characterized by optimally choosing the initial backoff window sizes of all the nodes and shown to be closely dependent on the percentage of nodes that can be heard by multiple APs. The comparison with orthogonal frequency division reveals that although the maximum network throughput is degraded due to interference among BSSs, a higher network data rate can still be achieved by universal frequency reuse, which makes it a preferable option for multi-BSS IEEE 802.11 networks.

The fundamental idea of network decomposition is to break a large-scale network into smaller parts such that the subnetworks can operate in parallel, each with a much lower dimensionality. For large-scale wireless networks, the cellular structure is based on the idea of network decomposition, where the network is decomposed into multiple subnetworks, i.e., cells, according to the coverage of each base-station (BS). Such a decomposition scheme, nevertheless, leads to strong interference among subnetworks, which becomes increasingly significant as the density of BSs grows. For the next-generation cellular network, where a massive amount of BSs need to be deployed to meet the ever-increasing demand of high data rate, it is of paramount importance to develop efficient network decomposition schemes to replace the current cellular structure. How to build such a decomposition framework, unfortunately, has remained largely unknown. This paper aims to establish a network decomposition theory for large-scale wireless networks from a graph-theoretic point of view. Specifically, we start from a novel bipartite graph representation of an infrastructure-based wireless network and show that in general the optimal network decomposition can be formulated as a graph partitioning problem. For demonstration, we focus on maximizing the number of subgraphs for a given cut ratio constraint and propose a binary search based spectral relaxation (BSSR) algorithm to solve it in two loops. The performance of the proposed BSSR algorithm is further examined and compared with the current cellular structure and BS clustering in various scenarios. Significant gains are shown to be achieved by the proposed BSSR algorithm, which corroborates that the optimal network decomposition of next-generation cellular networks should be performed based on a bipartite graph, where the geographical information of BSs and users are both included.

This paper presents the sum rate analysis of a saturated M-group multi-rate carrier sense multiple access network, where nodes in different groups have distinct packet transmission rates. An explicit expression of the network sum rate is derived, based on which the maximum sum rate is obtained by optimizing the transmission probabilities of nodes. It is found that to achieve the maximum sum rate, only the group of nodes with the largest transmission rate is allowed to access the channel, which leads to severe unfairness. To ensure certain fairness, two constraints, namely, throughput fairness (TF) and data-rate fairness (DF), are proposed, with which each node acquires a target proportion of the network throughput and the network sum rate, respectively. Explicit expressions of the network maximum sum rate with TF and DF are derived, which show that by including the fairness constraints, the network maximum sum rate becomes inferior to that without fairness constraints as long as there is difference in the transmission rates of nodes. The analysis is further applied to IEEE 802.11 networks, where the optimal initial backoff window sizes of nodes to achieve the network maximum sum rates with both fairness constraints are derived.

The sum rate performance of random-access networks crucially depends on the access protocol and receiver structure. Despite extensive studies, how to characterize the maximum sum rate of the simplest version of random access, Aloha, remains an open question. In this paper, a comprehensive study of the sum rate performance of slotted Aloha networks is presented. By extending the unified analytical framework from the classical collision model to the capture model, the network steady-state point in saturated conditions is derived as a function of the signal-to-interference-plus-noise ratio (SINR) threshold, which determines a fundamental tradeoff between the information encoding rate and the network throughput. To maximize the sum rate, both the SINR threshold and backoff parameters of nodes should be properly selected. Explicit expressions of the maximum sum rate and the optimal setting are obtained, which show that similar to the sum capacity of the multiple access channel, the maximum sum rate of slotted Aloha also logarithmically increases with the mean received signal-to-noise ratio (SNR), but the high-SNR slope is only $e^{-1}$. Effects of backoff and power control on the sum rate performance of slotted Aloha networks are further discussed, which shed important light on the practical network design.

A retry limit is usually adopted in practical carrier sense multiple access (CSMA) networks, where a packet is discarded if the maximum number of retransmission attempts is reached. Despite extensive studies, the effect of retry limit on the performance optimization of CSMA networks has remained largely unknown. This paper focuses on a CSMA network with a finite retry limit M , and aims to address the following open issues. First, for a given retry limit M , how should the backoff parameters be adaptively tuned to achieve the optimal network performance? Second, how does the optimal network performance vary with M ? Specifically, in this paper, the explicit expressions of the network steady-state points, the network throughput, and moments of access delay of successfully transmitted packets are all obtained as the functions of the retry limit M , based on which the optimal network performance is further characterized. It is revealed that a CSMA network with a finite retry limit M may have three steady-state points, and the retry limit M has distinct effects on the throughput and delay performance at these steady-state points. The maximum network throughput is found to be independent of M . Yet to achieve the maximum network throughput, the aggregate input rate and the initial transmission probability of each node should be set according to M in unsaturated and saturated conditions, respectively. To optimize the mean access delay, on the other hand, the initial transmission probability should be carefully selected in saturated conditions. The minimum mean access delay can be greatly reduced by choosing a smaller retry limit M , which, nevertheless, leads to a significant throughput loss. The analysis sheds important light on performance optimization of practical CSMA-based networks such as IEEE 802.11 networks.

Despite substantial rate gains achieved by joint transmission from a massive amount of geographically distributed antennas, the resulting computational cost and channel measurement overhead could be unaffordable for a large-scale distributed antenna system (DAS). A scalable signal processing framework is therefore highly desirable, which could be established based on the concept of virtual cell. In a virtual-cell based DAS, each user chooses a few neighboring base-station (BS) antennas to form its virtual cell, i.e, its own serving BS antenna set. In this paper, we focus on a downlink DAS with a large number of users and BS antennas uniformly distributed in a certain area, and aim to study the effect of the virtual cell size on the average user rate. Specifically, by assuming that maximum ratio transmission (MRT) is adopted in each user's virtual cell, the achievable ergodic rate of each user is derived as an explicit function of the large-scale fading coefficients from all the users to their virtual cells, and an upper-bound of the average user rate is established, based on which a rule of thumb is developed for determining the optimal virtual cell size to maximize the average user rate. The analysis is further extended to consider multiple users grouped together and jointly served by their virtual cells using zero-forcing beamforming (ZFBF). In contrast to the no-grouping case where a small virtual cell size is preferred, it is shown that by grouping users with overlapped virtual cells, the average user rate can be significantly improved by increasing the virtual cell size, though at the cost of a higher signal processing complexity.

This paper addresses the beam allocation problem in a switched-beam based massive multiple-input-multiple-output (MIMO) system working at the millimeter wave frequency band, with the target of maximizing the sum data rate. This beam allocation problem can be formulated as a combinatorial optimization problem under two constraints that each user uses at most one beam for its data transmission and each beam serves at most one user. The brute-force search is a straightforward method to solve this optimization problem. However, for a massive MIMO system with a large number of beams N, the brute-force search results in intractable complexity O(NK), where K is the number of users. In this paper, in order to solve the beam allocation problem with affordable complexity, a suboptimal low-complexity beam allocation (LBA) algorithm is developed based on submodular optimization theory, which has been shown to be a powerful tool for solving combinatorial optimization problems. Simulation results show that our proposed LBA algorithm achieves nearly optimal sum data rate with complexity O(K log N). Furthermore, the average service ratio, i.e., the ratio of the number of users being served to the total number of users, is theoretically analyzed and derived as an explicit function of the ratio N/K.

Due to the rapid growth of real-time applications in wireless local area networks (WLANs), quality-of-service (QoS) guarantee becomes one of the key issues for IEEE 802.11e enhanced distributed channel access (EDCA) networks. In contrast to most existing studies which only focus on providing delay guarantee to real-time flows, in this paper we study the open question of how to adaptively tune system parameters to maximize the aggregate throughput of non-real-time flows with a certain mean access delay constraint on real-time flows for saturated IEEE 802.11e EDCA networks. Explicit expressions of the maximum aggregate throughput of non-real-time flows and the optimal initial backoff window sizes are obtained, and verified by simulation results. The analysis shows that for a given mean access delay constraint, the maximum aggregate throughput of non-real-time flows declines as the number of real-time nodes grows. It drops to zero when the number of realtime nodes exceeds a critical threshold, indicating that the delay requirement cannot be satisfied. An admission control scheme is further proposed, where the maximum number of real-time nodes that can be enrolled is derived as a linearly increasing function of the mean access delay constraint.

Binary Exponential Backoff (BEB) is a key component of the IEEE 802.11 DCF protocol. It has been shown that BEB can achieve the theoretical limit of throughput as long as the initial backoff window size is properly selected. It, however, suffers from significant delay degradation when the network becomes saturated. It is thus of special interest for us to further design backoff schemes for IEEE 802.11 DCF networks that can achieve comparable throughput as BEB, but provide better delay performance. This paper presents a systematic study on the effect of backoff schemes on throughput and delay performance of saturated IEEE 802.11 DCF networks. In particular, a backoff scheme is defined as a sequence of backoff window sizes {Wi}. The analysis shows that a saturated IEEE 802.11 DCF network has a single steady-state operating point as long as {Wi} is a monotonic increasing sequence. The maximum throughput is found to be independent of {Wi}, yet the growth rate of {Wi} determines a fundamental tradeoff between throughput and delay performance. For illustration, Polynomial Backoff is proposed, and the effect of polynomial power x on the network performance is characterized. It is demonstrated that Polynomial Backoff with a larger x is more robust against the fluctuation of the network size, but in the meanwhile suffers from a larger second moment of access delay. Quadratic Backoff (QB), i.e., Polynomial Backoff with x=2, stands out to be a favorable option as it strikes a good balance between throughput and delay performance. The comparative study between QB and BEB confirms that QB well preserves the robust nature of BEB and achieves much better queueing performance than BEB.

A great deal of efforts have been made on the performance evaluation of distributed antenna systems (DASs). Most of them assume a regular base-station (BS) antenna layout where the number of BS antennas is usually small. With the growing interest in cellular systems with large antenna arrays at BSs, it becomes increasingly important to study how the BS antenna layout affects the rate performance when a vast number of BS antennas are employed. This paper presents a comparative study of the asymptotic rate performance of downlink multi-user systems with multiple BS antennas either co-located or uniformly distributed within a circular cell. Two representative linear precoding schemes, maximum ratio transmission (MRT), and zero-forcing beamforming (ZFBF), are considered, with which the effect of BS antenna layout on the rate performance is characterized. The analysis shows that as the number of BS antennas L and the number of users K grow infinitely while L/K → v, the asymptotic average user rates with the co-located antenna (CA) layout for both MRT and ZFBF are logarithmic functions of the ratio u. With the distributed antenna (DA) layout, in contrast, the scaling behavior of the average user rate closely depends on the precoding schemes. With ZFBF, for instance, the average user rate grows unboundedly as L, K → ∞ and L/K → v > 1, which indicates that substantial rate gains over the CA layout can be achieved when the number of BS antennas L is large. The gain, nevertheless, becomes marginal when MRT is adopted.

Performance of cellular networks is severely limited by intense inter-cell interference due to aggressive frequency reuse among cells. How to characterize the dependency of inter-cell interference on positions of BS antennas and users is a key question for the capacity analysis of cellular systems, which unfortunately remains elusive. In this paper, a comparative study on the uplink ergodic sum capacity of cellular systems is presented, where LcBS antennas are either co-located at the cell center or uniformly distributed within each cell. With a large number of users, the inter-cell interference density is shown to be inversely proportional to Lcif the co-located antenna (CA) layout is adopted. With the distributed antenna (DA) layout, it scales in the order of Lc-α/2, where α is the path-loss factor, and is much lower than that in the CA case when Lcis large. Substantial gains on the uplink sum capacity are achieved by the DA layout thanks to the reduction of inter-cell interference level. The analysis also reveals that the inter-cell interference density of each BS antenna is sensitive to its position. With the DA layout, BS antennas at cell boundary areas suffer from much higher inter-cell interference than those at the cell center, which may exacerbate the performance disparity of users in cellular systems. To tackle the cell-edge problem, a distributed antenna system (DAS) is further considered, where L BS antennas are distributed over a wide area, and each user chooses V ≪ L surrounding BS antennas as its virtual cell, i.e., its own serving BS antenna set. A uniform inter-cell interference density is shown to be achieved thanks to the adaptive formation of virtual cells. More importantly, by the use of virtual cell, the number of users served by each BS antenna decreases with an increasing L, implying that much of the signal processing and information exchange can be performed in a local and distributed way. The uplink ergodic sum capacity and the ergodic rate with orthogonal access of DASs with V=1 are further derived, and shown to be close to each other even with a large L. It is in sharp contrast to cellular systems where a significant tradeoff between performance and complexity has to be made when the number of BS antennas is large.

Cooperative communication is an effective technique which provides diversity gains to combat the attenuation of radio signals caused by fading and path loss. By sharing each cooperative node's antenna to form a virtual antenna array, the reliability of the wireless transmission can be significantly improved. On the other hand, cooperation among different nodes may produce a higher level of interference and degrade the overall performance of larger and dynamic networks with multiple concurrent transmissions. For practical environments, it is essential to investigate the tradeoff between cooperative diversity and this additional interference. In this paper, by analyzing and comparing the outage performance of non-cooperative and cooperative strategies, we derive a criterion which determines whether we should implement cooperation or not. Both analytical results and simulations show that a cooperative strategy is preferred for sparse networks. As the wireless network becomes more dense, the diversity gain is eventually eliminated by the excessive amount of interference, which implies that non-cooperative strategies should be used.

Enhanced distributed channel access (EDCA) is an extension of the distributed coordination function to support quality-of-service for IEEE 802.11 wireless local area networks. By assigning distinct backoff parameters to each access category (AC), differentiated throughput performance can be achieved when the network is saturated. Although it has been long observed that the network throughput with the current EDCA standard setting may significantly degrade as the network size grows, how to properly tune the backoff parameters to optimize the network throughput under a certain differentiation requirement remains largely unknown. In this paper, a new analytical model is proposed to address this open issue. Specifically, we focus on an M-AC IEEE 802.11e EDCA network where nodes in the same AC have identical backoff parameters, including the initial backoff window sizeW(g), the cutoff phaseK(g), and the arbitration interframe spaces (AIFS) numberA(g), g = 1, . . . , M. The network steady-state operating point in saturated conditions, i.e., pA, is characterized by using the steady-state probability of successful transmission of head-of-line (HOL) packets given that the channel is idle, based on which explicit expressions of node throughput and network throughput are further obtained. For given target ratios of node throughput of ACs, the optimal initial backoff window sizes and AIFS numbers to maximize the network throughput are derived and verified by simulation results. The analysis reveals that the maximum network throughput is solely determined by the holding time of HOL packets in successful transmission and collision states. To achieve the maximum network throughput, the initial backoff window size of each AC should be linearly increased with the network size. In the meantime, the increasing rate of the initial backoff window size, or the AIFS number, of each AC should be also carefully set according to the target ratios of node throughput. Although the maximum network throughput with pre-specified target ratios of node throughput of ACs can be achieved in both ways, the backoff window size differentiation could be a more preferable option as it requires fewer tuning parameters and provides better precision than the AIFS differentiation.

Despite the common belief that substantial capacity gains can be achieved by using more antennas at the base-station (BS) side in cellular networks, the effect of BS antenna topology on the capacity scaling behavior is little understood. In this paper, we present a comparative study on the ergodic capacity of a downlink single-user multiple-input-multiple-output (MIMO) system where BS antennas are either co-located at the center or grouped into uniformly distributed antenna clusters in a circular cell. By assuming that the number of BS antennas and the number of user antennas go to infinity with a fixed ratio L ≫ 1, the asymptotic analysis reveals that the average per-antenna capacities in both cases logarithmically increase with L, but in the orders of log2L and α/2 log2L, for the co-located and distributed BS antenna layouts, respectively, where α > 2 denotes the path-loss factor. The analysis is further extended to the multiuser case where a 1-tier (7-cell) MIMO cellular network with K ≫1 uniformly distributed users in each cell is considered. By assuming that the number of BS antennas and the number of user antennas go to infinity with a fixed ratio L ≫ K, an asymptotic analysis is presented on the downlink rate performance with block diagonalization (BD) adopted at each BS. It is shown that the average per-antenna rates with the co-located and distributed BS antenna layouts scale in the orders of log2L/K and log2(L-K+1)α/2/K, respectively. The rate performance of MIMO K cellular networks with small cells is also discussed, which highlights the importance of employing a large number of distributed BS antennas for the next-generation cellular networks.

Despite the common belief that substantial capacity gains can be achieved by employing a large number of antennas at the base-station (BS) side in cellular networks, the effect of BS antenna topology on the capacity scaling behavior is little understood. This paper presents a comparative study on the asymptotic capacity of a downlink single-user multiple-input-multiple-output (MIMO) system with multiple BS antennas which are either co-located or grouped into uniformly distributed clusters. The analysis reveals that when the ratio L of the number of BS antennas to the number of user antennas is large, the average per-antenna capacities in both cases logarithmically increase with L, but in the orders of log2L and α/2 log2L for the co-located and distributed antenna layouts, respectively, where α>2 denotes the path-loss factor. A higher capacity is achieved by distributing the BS antennas thanks to the reduction of the minimum access distance.

A key open question in IEEE 802.11e networks with enhanced distributed channel access (EDCA) is how to properly tune the backoff parameters to optimize the network throughput performance under a certain differentiation requirement. To tackle this problem, a new analytical model for IEEE 802.11e EDCA networks is proposed in this paper, based on which the maximum network throughput is derived as an explicit function of the holding times of head-of-line (HOL) packets in successful transmission and collision states. The optimal initial backoff window sizes to achieve the maximum network throughput under pre-specified target node-throughput ratios are also obtained, and verified by simulation results.

This paper presents an asymptotic rate analysis for downlink multi-user systems with L base-station (BS) antennas either co-located or uniformly distributed within a circular cell. A representative non-orthogonal linear precoding scheme, maximum ratio transmission (MRT), is considered, based on which the effect of BS antenna layout on the intra-cell interference is characterized. The analysis reveals that the ratio σ of the number of BS antennas L and the number of users K is a key parameter that determines the rate performance of non-orthogonal downlink multi-user systems. Ergodic rates in the colocated antenna (CA) layout and the distributed antenna (DA) layout both logarithmically grow with σ, yet a higher rate is achieved in the DA case thanks to enhanced signal-to-interference ratio.

In this paper, a unified analytical framework is established to study the stability, throughput, and delay performance of homogeneous buffered IEEE 802.11 networks with Distributed Coordination Function (DCF). Two steady-state operating points are characterized using the limiting probability of successful transmission of Head-of-Line (HOL) packets $(p)$ given that the network is in unsaturated or saturated conditions. The analysis shows that a buffered IEEE 802.11 DCF network operates at the desired stable point $(p=p_{L})$ if it is unsaturated. $(p_{L})$ does not vary with backoff parameters, and a stable throughput can be always achieved at $(p_{L})$. If the network becomes saturated, in contrast, it operates at the undesired stable point $(p=p_{A})$, and a stable throughput can be achieved at $(p_A)$ if and only if the backoff parameters are properly selected. The stable regions of the backoff factor $(q)$ and the initial backoff window size $(W)$ are derived, and illustrated in cases of the basic access mechanism and the request-to-send/clear-to-send (RTS/CTS) mechanism. It is shown that the stable regions are significantly enlarged with the RTS/CTS mechanism, indicating that networks in the RTS/CTS mode are much more robust. Nevertheless, the delay analysis further reveals that lower access delay is incurred in the basic access mode for unsaturated networks. If the network becomes saturated, the delay performance deteriorates regardless of which mode is chosen. Both the first and the second moments of access delay at $(p_A)$ are sensitive to the backoff parameters, and shown to be effectively reduced by enlarging the initial backoff window size $(W)$.

Aloha and Carrier Sense Multiple Access (CSMA) are two representative random-access protocols. Despite their simplicity in concept, the performance analysis of Aloha and CSMA networks has long been known as notoriously difficult. Numerous models and analytical approaches have been proposed in the past four decades. Yet how to integrate them into a coherent theory remains an open challenge. Toward this end, a unified analytical framework was recently proposed in , based on which a comprehensive study of throughput, delay and stability performance of Aloha networks was presented. In this paper, the framework is further extended to CSMA networks. The analysis shows that both CSMA and Aloha have the same bi-stable property, and the performance of both networks critically depends on the selection of backoff parameters. Different from Aloha, however, substantial gains can be achieved in CSMA networks by reducing the mini-slot length a and the collision-detection time x. The maximum throughput with CSMA is derived as an explicit function of a and x, and shown to be higher than that with Aloha if a1/ϵ- 1≈0.445. With a small mini-slot length a, CSMA networks are also found to be more robust than Aloha networks thanks to larger stable regions of backoff parameters. To demonstrate how to properly tune the backoff parameters to stabilize the network, the complete stable region of the initial transmission probability q0is characterized, and illustrated via the example of p-persistent CSMA with the cutoff phase K=0. The optimal values of q0to maximize the network throughput and to minimize the first and second moments of access delay are also obtained, which shed important light on practical network control and optimization.

This paper presents the throughput analysis of an M-group heterogeneous IEEE 802.11 DCF network where nodes in different groups have distinct input rates and initial backoff window sizes. An explicit expression of the network steady-state operating point is obtained based on the fixed-point equation of the limiting probability of successful transmission of Head-of-Line (HOL) packets given that the channel is idle, which is shown to be closely dependent on the backoff parameters of saturated groups and the input rates of unsaturated groups. Both the network throughput and the group throughput performance are further characterized, and the maximum network throughput is derived as an explicit function of the holding times of HOL packets in successful transmission and collision states. The analysis reveals that to achieve the maximum network throughput, the optimal set of input rates of unsaturated groups and initial backoff window sizes of saturated groups should satisfy a constraint that is determined by the group sizes of saturated groups. Given the input rates of unsaturated groups, for instance, the initial backoff window sizes of saturated groups should linearly increase with their group sizes, and those with higher increasing rates achieve lower group throughput.

In this paper, a unified analytical approach is developed to characterize the stability and delay performance of buffered Aloha networks. It is demonstrated that a buffered Aloha network can be stabilized if backoff parameters are properly selected. The stable region of backoff factor q is derived and shown to be enlarged by increasing the cutoff phase K. With Geometric Retransmission (K{=}1), for instance, the stable region rapidly diminishes as the number of nodes n increases, implying that a slight change of network size may lead to instability if the backoff factor q is not updated accordingly. In contrast, a buffered Aloha network with Exponential Backoff (K{=}∞) is much more robust as the stable region becomes insensitive to the number of nodes n. The improvement on stability performance is, nevertheless, achieved at the cost of severe delay jitter. The delay analysis further reveals that the second moment of access delay of Head-of-Line (HOL) packets rapidly grows with the cutoff phase K when the network is saturated. In spite of an improved stable region, an excessively large K will lead to significant degradation of queueing performance.

Extensive studies have been conducted on the rate analysis of downlink multiuser multiple-input-multiple-output (MIMO) systems. Most of them, nevertheless, focused on the sum rate of all the users where the effect of large-scale fading is either ignored or simplified. For interference-limited MIMO cellular networks, users in various locations may achieve drastically different rates due to distinct large-scale fading and interference levels. It is, therefore, of great practical interest to characterize the rate performance of each single user under various power allocation strategies and precoding schemes. This paper presents an asymptotic per-user rate analysis for a 1-tier (i.e., 7-cell) downlink MIMO cellular network with M base-station antennas co-located at the center of each cell and K uniformly distributed users each equipped with N antennas. Explicit expressions of the ergodic rate with two representative linear precoding schemes, singular-value-decomposition (SVD) transmission and block diagonalization (BD), are derived, based on which the effect of power allocation strategies is further evaluated. The analysis shows that the rate performance of each user is sensitive to its position even if the average received power is kept a constant. To achieve a uniform rate all over the cell, an interference-aware power allocation scheme is proposed, with which the transmit power allocated to each user is carefully adjusted according to the large-scale fading coefficients from the user to its own and neighboring base stations. The tradeoff between fairness and sum rate performance is also identified, which highlights the importance of the characterization of per-user rate.

It has been long believed that the distributed antenna system (DAS) has great potentials for capacity improvement compared to the traditional cellular system with co-located base-station antennas. Intuitively, the distributed characteristic of antennas provides a much more efficient utilization of spatial resources. It, however, also significantly complicates the channel modeling and system analysis. Despite an increasing amount of academic attention and industrial interest, how to characterize the capacity advantages of the DAS in the multiuser scenario remains largely unknown. In this paper, we present a comparative study on the uplink ergodic sum capacity with multiple base-station antennas either co-located or uniformly distributed within a given area. We demonstrate that under the same consumption of transmission power, enormous gains can be achieved by the distributed antenna layout thanks to (1) reduced minimum access distance of each user; and (2) enhanced channel fluctuations which provide a significant boost to the sum capacity when the channel state information is available at both the transmitter and the receiver sides. We further apply the analysis to a cellular system with full cooperation among base stations. The comparison verifies that the DAS yields a much higher sum capacity, and capacity gains increase with the number of base-station antennas per cell.

In ad-hoc networks, cooperative diversity is especially beneficial where the use of multiple antennas may be impractical. There has been a lot of work on improving the peer-to-peer link quality by using advanced coding or power and rate allocation between a single source node and its relays. However, how to fairly and efficiently allocate resources among multiple users and their relays is still unknown. In this paper, a multiuser cooperative protocol is proposed, where a power reward is adopted by each node to evaluate the power contributed to and by others. It will be shown that the proposed fair cooperative protocol (FAP) can significantly improve the fairness performance compared to full cooperation. It is further demonstrated that in energy-constrained cooperative ad-hoc networks, fairness can actually bring significant throughput gains. The tradeoff between fairness and throughput is analyzed and two price-aware protocols, FAP-R and FAP-S, will be further proposed to improve fairness. Simulation results will validate our analysis and show that compared to the direct transmission (i.e., without cooperation) and the full cooperation, our proposed FAP, FAP-R and FAP-S can achieve much better fairness performance along with substantial throughput gains.

The fading characteristics and broadcast nature of wireless channels are usually not fully considered in the design of routing protocols for wireless networks. In this paper, we combine routing and cooperative diversity, with the consideration of a realistic channel model. We focus on a multihop network with multiple relays at each hop, and three routing strategies are designed to achieve the full diversity gain provided by cooperation among the relays. In particular, an optimal routing strategy is proposed to minimize the end-to-end outage, which requires the channel information of all the links and serves as a performance bound. An ad-hoc routing strategy is then proposed based on a hop-by-hop relay selection, which can be easily implemented in a distributed way. As expected, ad-hoc routing performs worse than optimal routing, especially with a large number of hops. To achieve a good complexity-performance tradeoff, an N-hop routing strategy is further proposed, where a joint optimization is performed every N hops. Simulation results are provided which verify the outage analyses of the proposed routing strategies.

In this paper, we investigate two selective relaying schemes in cooperative OFDM systems. Selective OFDMA relaying, where the relay selection is performed in a per-subcarrier manner, and selective OFDM relaying, where one best relay among the L potential relays is selected to relay the entire OFDM block, are compared in a two-hop random network. The outage performance of equal bit allocation (EBA), where each subchannel has the same number of bits, and bit loading (BL), where bits are adaptively allocated to each subchannel, are analyzed and compared for these two approaches. The outage analysis clearly shows that a significant performance gain can be achieved by selective OFDMA relaying, whether EBA or BL is employed, compared with selective OFDM relaying. The performance gain remains the same for different relay locations. With EBA, the performance gain increases with an increase in L and N, the number of independent subchannels. For BL, the performance gain also increases with an increase in R, the average number of bits per subchannel, in addition to L and N. Centralized and decentralized implementation issues are also considered. For EBA, selective OFDMA relaying scheme is preferred because of its superior performance and simple decentralized implementation. For BL, selective OFDMA relaying scheme is a good choice for centralized systems and selective OFDM relaying is more suitable for decentralized systems at the expense of a loss in performance.

We propose a cross-layer design which combines truncated ARQ at the link layer and cooperative diversity at the physical layer. In this scheme, both the source node and the relay nodes utilize an orthogonal space-time block code for packet retransmission. In contrast to previous cooperative diversity protocols, here cooperative diversity is invoked only if the destination node receives an erroneous packet from the source node. In addition, the relay nodes are not fixed and are selected according to the channel conditions using CRC. It will be shown that this combination of adaptive cooperative diversity and truncated ARQ can greatly improve the system throughput compared to the conventional truncated ARQ scheme and fixed cooperative diversity protocols. We further maximize the throughput by optimizing the packet length and modulation level and will show that substantial gains can be achieved by this joint optimization. Since both the packet length and modulation level are usually discrete in practice, a computationally efficient algorithm is further proposed to obtain the discrete optimal packet length and modulation level.

Node cooperation is an emerging and powerful solution that can overcome the limitation of wireless systems as well as improve the capacity of the next generation wireless networks. By forming a virtual antenna array, node cooperation can achieve high antenna and diversity gains by using several partners to relay the transmitted signals. There has been a lot of work on improving the link performance in cooperative networks by using advanced signal processing or power allocation methods among a single source node and its relays. However, the resource allocation among multiple nodes has not received much attention yet. In this paper, we present a unified cross- layer framework for resource allocation in cooperative networks, which considers the physical and network layers jointly and can be applied for any cooperative transmission scheme. It is found that the fairness and energy constraint cannot be satisfied simultaneously if each node uses a fixed set of relays. To solve this problem, amulti-statecooperationmethodology is proposed, where the energy is allocated among the nodes state-by-state via a geometric and network decomposition approach. Given the energy allocation, the duration of each state is then optimized so as to maximize the nodes utility. Numerical results will compare the performance of cooperative networks with and without resource allocation for cooperative beamforming and selection relaying. It is shown that without resource allocation, cooperation will result in a poor lifetime of the heavily-used nodes. In contrast, the proposed framework will not only guarantee fairness, but will also provide significant throughput and diversity gain over conventional cooperation schemes.

User cooperation is a powerful solution that can significantly improve the reliability of wireless networks by using several relays to achieve diversity gains. There has been a lot of work on improving the peer-to-peer link quality of a single source-destination pair. However, how to fairly and efficiently allocate resources among multiple nodes has not received much attention yet. In this paper, we propose a novel cooperative diversity method that can achieve fair and efficient resource allocation. We shall show that fairness cannot be achieved by using fixed sets of relays in general. A multi-state cooperation method, where the relay set of each node can be changed, is then proposed to solve this problem. In this proposed approach, the energy is allocated among the nodes via a finite step iterative algorithm. In each step, the relay sets of nodes are changed so that each step will generate a cooperation state, which characterizes the cooperation relationship among the nodes. Based on the energy allocation result, the duration of each state is then optimized so as to minimize the outage probability. We shall show that the proposed method can not only guarantee fairness, but also provide significant diversity gain over conventional cooperation schemes.

There has been growing interest in the integration of multihop (or relaying) capability into conventional wireless networks. In this paper, we propose an OFDM-based selective relaying scheme, where the relay selection at each hop is performed on a per-subcarrier basis and joint selection is adopted at the last two hops. The outage analysis clearly shows that full spatial diversity gain can be achieved with this proposed selective OFDMA relaying. In contrast, no diversity gain can be obtained if the entire OFDM block chooses the same relay with the highest combined SNR. It is also demonstrated that with coding among the subcarriers, superior performance can be achieved by selective OFDMA relaying with only symbol detection at each relay. This is highly attractive as the processing complexity and decoding delay incurred are very small.

The fading characteristics and broadcast nature of wireless channels are usually not fully considered in the design of routing protocols for wireless networks. In this paper, we address the routing issue from a link layer point of view. We focus on a multihop network with multiple relays at each hop and three routing strategies are designed to achieve the full diversity gain provided by the cooperation among relays. In particular, an optimal routing strategy is proposed to minimize the end-to-end outage, which requires the channel information of all the links and serves as a performance bound. An ad-hoc routing strategy is then proposed based on a hop-by-hop relay selection, which can be easily implemented in a distributed way. The outage analysis shows that the performance gap between these two routing strategies increases with the number of hops. To achieve a good complexity-performance tradeoff, a N-hop routing strategy is further proposed, where a joint optimization is performed every N hops. Simulation results are presented which verify the analysis.

Most existing MIMO (multiput-input multiput-output) schemes optimize only either the diversity gain or the multiplexing gain. To obtain a good tradeoff between these two, the quasi-orthogonal group space-time (QoGST) architecture is proposed, wherein the transmit stream is subgrouped but encoded via an inter-group space-time block encoder, with group interference suppression at the receiver. This paper also considers another combined space-time coding and layered space-time architecture, which we refer to as group layered space-time (GLST), where space-time block coding is employed within each group. Under the assumption of Rayleigh fading and a prior perfect channel state information at the receiver, a performance analysis will demonstrate that both QoGST and GLST can achieve a good diversity-multiplexing tradeoff. QoGST is even superior to GLST. Simulation results will validate our analysis and further show that compared to the existent layered space-time block code (LSTBC) scheme, both QoGST and GLST can achieve a significant performance gain.

In ad-hoc networks, cooperative diversity is especially desired where the use of multiple antennas may be impractical due to the size of nodes. There has been a lot of work on improving the peer-to-peer link quality by using advanced coding or power and rate allocation between a single source node and its relays. However, how to efficiently and fairly allocate resources among multiple users and their relays is still unknown. In this paper, a novel multiuser cooperation protocol is proposed, where multiuser diversity scheme is adopted to schedule different source/destination pairs and each pair computes its required rate based on a power reward. Power reward is adopted by each node to evaluate the power contributed to and by others so as to guarantee fairness. It will be shown that in energy-constrained cooperative ad-hoc networks, fairness can actually bring significant throughput gains. Simulation results will validate our analysis and show that compared to direct transmission and full cooperation protocols, much higher aggregate throughput can be achieved by the proposed Fair Cooperation Protocol thanks to improved fairness.

Recent work has shown that multiple-input multiple-output (MIMO) systems with multiple antennas at both the transmitter and receiver are able to achieve great capacity improvement. In such systems, it is desirable to select a subset of the available antennas so as to reduce the number of radio frequency (RF) chains. This paper addresses the problem of antenna selection in correlated channels. We consider a narrowband communication system with M transmit and N receive antennas. We present the criterion for selecting the optimal L/sub t/ out of M transmit and L/sub r/ out of N receive antennas in terms of capacity maximization, assuming that only the long-term channel statistics, instead of the instantaneous channel-state information, are known. Simulations will be used to validate our theoretical analysis and demonstrate that the number of required RF chains can be significantly decreased using our proposed selection strategy, while achieving even better performance than the conventional MIMO system without antenna selection.

We propose a cross-layer design which combines truncated ARQ at the link layer and cooperative diversity at the physical layer. In this scheme, both the source node and the relay nodes utilize an orthogonal space-time block code for packet retransmission. In contrast to the previous cooperative diversity protocols, here cooperative diversity is invoked only if the destination node receives an erroneous packet from the source node. In addition, the relay nodes are not fixed and are selected according to the channel conditions using CRC. It is shown that this combination of adaptive cooperative diversity and truncated ARQ can greatly improve the system throughput compared to the conventional truncated ARQ scheme and fixed cooperative diversity protocols

Multiple-input multiple-output (MIMO) systems can provide two kinds of gain: diversity gain and multiplexing gain. Most existing MIMO schemes, including space-time coding and layered space-time, aim at maximizing either of them. Group transmission and detection is a good way to get a tradeoff between these two gains. In this paper, the diversity-multiplexing tradeoff functions of 3 schemes based on group transmission and detection, quasi-orthogonal group space-time (QoGST), group layered space-time (GLST) and layered space-time block codes (LSTBC), are provided and a vivid comparison among them demonstrates that QoGST achieves the best diversity-multiplexing tradeoff. GLST also gets significant gains over LSTBC. Simulation results will validate the analysis and show that QoGST can achieve at least 3 dB gain over GLST and 12 dB over LSTBC at a FER of 10/sup -3/, for instance.

Group layered space-time architecture (GLST) combines space-time block coding and layered space-time processing, where the transmit stream is partitioned into different groups, and in each group, space-time block coding is applied. In the traditional receiver of GLST, group detection is applied first to suppress the interference from other groups, and then decoding is performed for the desired group. In this letter, a novel detector is proposed in which the entire groups are decoded first, and then group detection is performed next. Theoretical analysis will demonstrate that the new detector can achieve a significant capacity gain compared with the traditional one. Simulation results will further show that the proposed detector can obtain at least 4 dB gain at a frame-error rate of 10/sup -2/, for instance.

It is well known that multiple-input multiple-output (MIMO) systems provide two types of gains: diversity gains and spatial multiplexing gains. Recently, a tradeoff function of these two gains has been derived for a point-to-point MIMO system when optimal detection is used. In this paper, we extend the previous work to a more general MIMO system, where the transmitted data is coded in groups. Group detection is applied at the receiver to retrieve the data. It consists of a zero-forcing decorrelation that separates the groups, followed by a joint detection for each of the groups. Two receiver structures are considered in this paper; namely, group zero forcing (GZF) and group successive interference cancellation (GSIC). We assess the diversity-multiplexing tradeoff function of each of these receivers over a richly scattered Rayleigh fading channel. Three rate-allocation algorithms will be considered here; namely, equal rate, group-size proportional rate, and optimal-rate allocation. An explicit expression of the system tradeoff will be derived for both receivers with these three rate allocations. The obtained results will first be optimized over all possible group partitions for a given number of groups. Next, the number of groups will be varied to further optimize the system-tradeoff performance. An overall optimum tradeoff for a general MIMO system with group detection will then be obtained. Numerical results will indicate that optimum performance can be approached with very-low-complexity schemes for a wide range of data rates. It will be also demonstrated that group detection bridges the gap between the traditional decorrelator and the optimal receiver tradeoff performances.

In this letter, the effect of maximal ratio combining (MRC)-based macrodiversity on the reverse-link and forward-link capacity in code division multiple access (CDMA)-distributed antenna systems is analyzed. The concept of virtual cell is illustrated, and the analytical outage probability expressions are derived. The present investigation shows that on the reverse link, the interference can be suppressed greatly with macrodiversity, which leads to a significant increase in capacity. However, on the forward-link, it is proven that if simulcasting is used in CDMA-distributed antenna systems, the forward-link capacity cannot increase with macrodiversity whatever power allocation scheme is adopted. Based on the analysis of the cause of capacity loss, a new transmission scheme is further presented and the optimal power allocation scheme is derived. It is shown that, in this case, the forward-link capacity increases rapidly with the number of involved distributed antennas.

Multiple-input multiple-output (MIMO) systems can provide two kinds of gain: diversity gain and multiplexing gain. Most existing MIMO schemes, including space-time coding and layered space-time, aim at maximizing either of them. Therefore, it is desirable to design a scheme to get a better tradeoff between the multiplexing gain and diversity gain. In this paper, a novel quasi-orthogonal group space-time (QOGST) architecture is proposed. In QOGST, the transmit stream is divided into several groups and all the groups are encoded via an inter-group space-time block encoder. Group interference suppression is adopted at the receiver. Performance is evaluated in terms of symmetric energy and it is shown that compared to the group layered space-time (GLST) architecture, the proposed QOGST can achieve a higher symmetric energy and a better diversity-multiplexing tradeoff. Simulation results validate our analysis and show that QOGST can achieve at least 3 dB gain over GLST at a FER of 10/sup -3/, for instance.

MIMO systems have been shown to provide significant performance gains over traditional single antennas systems that fall in two categories: diversity and multiplexing rate. A tradeoff between these gains was recently put in evidence and has been quantified with the optimal detection. We consider the evaluation of such tradeoff when group detection is applied and particularly when the group zero forcing (GZF) receiver structure is considered. To do so, we define and evaluate the outage probability per group and derive the tradeoff obtained by each of the groups. The overall system tradeoff is then given by the minimum group tradeoff performance. Optimal rate allocation is also proposed so as to maximize GZF tradeoff performance. Comparison for a given group partition with equal rate allocation shows that optimal rate allocation allows us to both maximize the diversity and the multiplexing rate of GZF. Furthermore, considering a fixed number of antennas, we find the minimum required number of groups for a given tradeoff level, as well as the optimal group partition that maximizes the system tradeoff. Numerical results demonstrate the optimality of this scheme. Significant diversity gains are put in evidence demonstrating that GZF can efficiently bridge the gap between BLAST and the optimal receiver while offering lower levels of complexity.

Multiple-input multiple-output (MIMO) systems can provide great capacity improvement but suffer from multiple expensive RF chains. Antenna selection offers a good tradeoff between complexity and performance. This paper addresses the problem of optimal receive antenna selection in correlated channels. We consider the transmission of M independent signals to a base station with N correlated antennas and present two criteria for selecting the optimal L out of N receive antennas in terms of capacity maximization or BER minimization, assuming that only the long-term channel statistics, instead of the instantaneous channel state information, are known. Simulations will validate our theoretical analysis and demonstrate that within a rather wide angular spread range, the number of required RF chains can be significantly decreased using our proposed selection strategy while achieving very close performance to the instantaneous antenna selection system and the conventional MIMO system without antenna selection.

this paper we propose a novel spectral efficient transmit precoder scheme in which channel state information is fully utilized to maximize channel capacity. It is shown that this new scheme provides striking performance that is 3.5-5 dB from the outage capacity at 10% frame error rate even in the uncoded case. Compared with the open-loop scheme V-BLAST, this new scheme can not only maintain the same high bandwidth efficiency, but also achieve much better performance thanks to more effective transmission power allocation and diversity gain. Nearly 7dB gain can be provided by this new scheme over V-BLAST in a (2, 2) system.

This paper proposes an iterative V-BLAST detection algorithm that improves the error performance over error propagation. Traditional detection algorithm cannot alleviate error propagation because the decision feedbacks from low-diversity substreams are used to decode high-diversity substreams. In our algorithm, we iteratively suppress the interference towards low-diversity substreams by using decisions from high-diversity substreams, and the system performance is highly improved over the traditional one, which is demonstrated via simulation results. Besides, existing algorithms combating error propagation operate in high complexity, while the complexity of our algorithm is proportional to the loop times, providing a tradeoff between performance and complexity.

A spatial multiplexing technique for distributed antenna systems is proposed, where the mobile co-located with multiple antennas is capable of receiving distinct sub-streams from multiple widely separated antennas. Modulation and power are adapted based on the knowledge of large-scale fading, which varies much slower than instantaneous channel state information. Three modulation and power adaptation criteria are presented, which minimize the error probability, minimize the power consumption and maximize data throughput, respectively. Simulations based on minimum error probability criterion illustrate the performance improvement compared with original V-BLAST. Based on minimum power consumption criterion and maximum data throughput criterion, numerical results also indicate that DAS is more power efficient, spectral efficient and uniform in quality of service than co-located antenna systems (CAS), which validates the previous theoretic analysis results.

Previous studies have shown that per-antenna rate and power control can greatly increase the data throughput of vertical Bell Labs layered space-time (V-BLAST), while an extra transmit antenna selection can provide additional diversity advantage. We combine the transmit antenna selection with power and rate control for each antenna. We derive a simple criterion for minimum bit-error rate (BER) or minimum total transmit power when the data throughput is constant over time. Zero-forcing and zero-forcing successive interference cancellation detections are considered. For practical implementation, we also present a fast algorithm that gives near-optimal performance with very low complexity. Simulation results show that the proposed closed-loop BLAST outperforms the open-loop V-BLAST significantly in terms of BER performance, especially when the antennas exhibit strong fading correlations.