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This paper investigates a futuristic spectrum sharing paradigm for heterogeneous wireless networks with imperfect channels. In the heterogeneous networks, multiple wireless networks adopt different medium access control (MAC) protocols to share a common wireless spectrum and each network is unaware of the MACs of others. This paper aims to design a distributed deep reinforcement learning (DRL) based MAC protocol for a particular network, and the objective of this network is to achieve a global $\alpha$ α -fairness objective. In the conventional DRL framework, feedback/reward given to the agent is always correctly received, so that the agent can optimize its strategy based on the received reward. In our wireless application where the channels are noisy, the feedback/reward (i.e., the ACK packet) may be lost due to channel noise and interference. Without correct feedback, the agent (i.e., the network user) may fail to find a good solution. Moreover, in the distributed protocol, each agent makes decisions on its own. It is a challenge to guarantee that the multiple agents will make coherent decisions and work together to achieve the same objective, particularly in the face of imperfect feedback channels. To tackle the challenge, we put forth (i) a feedback recovery mechanism to recover missing feedback information, and (ii) a two-stage action selection mechanism to aid coherent decision making to reduce transmission collisions among the agents. Extensive simulation results demonstrate the effectiveness of these two mechanisms. Last but not least, we believe that the feedback recovery mechanism and the two-stage action selection mechanism can also be used in general distributed multi-agent reinforcement learning problems in which feedback information on rewards can be corrupted. [ABSTRACT FROM AUTHOR]

This paper proposes using the uncertainty of information (UoI), measured by Shannon’s entropy, as a metric for information freshness. We consider a system in which a central monitor observes M binary Markov processes through m communication channels (m

Over-the-air computation (OAC) is a promising technique to realize fast model aggregation in the uplink of federated edge learning (FEEL). OAC, however, hinges on accurate channel-gain precoding and strict synchronization among edge devices, which are challenging in practice. As such, how to design the maximum likelihood (ML) estimator in the presence of residual channel-gain mismatch and asynchronies is an open problem. To fill this gap, this paper formulates the problem of misaligned OAC for FEEL and puts forth a whitened matched filtering and sampling scheme to obtain oversampled, but independent samples from the misaligned and overlapped signals. Given the whitened samples, a sum-product ML (SP-ML) estimator and an aligned-sample estimator are devised to estimate the arithmetic sum of the transmitted symbols. In particular, the computational complexity of our SP-ML estimator is linear in the packet length, and hence is significantly lower than the conventional ML estimator. Extensive simulations on the test accuracy versus the average received energy per symbol to noise power spectral density ratio (EsN0) yield two main results: 1) In the low EsN0 regime, the aligned-sample estimator can achieve superior test accuracy provided that the phase misalignment is not severe. In contrast, the ML estimator does not work well due to the error propagation and noise enhancement in the estimation process. 2) In the high EsN0 regime, the ML estimator attains the optimal learning performance regardless of the severity of phase misalignment. On the other hand, the aligned-sample estimator suffers from a test-accuracy loss caused by phase misalignment. [ABSTRACT FROM AUTHOR]

Channel conditions in wireless local area networks (WLANs) are variable due to mobility and interference. Autorate algorithms are commonly used in WLANs to maximize the data rate over the variable physical channel. However, the widely used automatic rate fallback (ARF) algorithm does not work properly in the presence of multiaccess collisions due to its inability to differentiate frame losses caused by link errors from those caused by collisions. This paper proposes an improved ARF algorithm that is capable of differentiating between these two types of losses with minimal modifications to the existing medium access control protocol. The performance evaluations show substantial throughput improvements over ARF and other existing algorithms. Index Terms--Autorate, distributed coordination function (DCF), loss differentiation (LD), protocol design, wireless local area networks (WLANs).

This paper investigates the hidden-node phenomenon (HN) in IEEE 802.11 wireless networks. HN occurs when nodes outside the carrier-sensing range of each other are nevertheless close enough to interfere with each other. As a result, the carriersensing mechanism may fail to prevent packet collisions. HN can cause many performance problems, including throughput degradation, unfair throughput distribution among flows, and throughput instability. The contributions of this paper are threefold. 1) This is a first attempt to identify a set of conditions--which we called Hidden-node-Free Design (HFD)--that completely remove HN in 802.11 wireless networks. 2) We derive variations of HFD for large-scale cellular WiFi networks consisting of many wireless LAN cells. These HFDs are not only HN-free, but they also reduce exposed nodes at the same time so that the network capacity is improved. 3) We investigate the problem of frequency-channel assignment to adjacent cells. We find that with HFD, careful assignment in which adjacent cells use different frequency channels does not improve the overall network capacity (in unit of bits per second per frequency channel). Indeed, given f frequency channels, a simple scheme with f overlaid cellular WiFi networks in which each cell uses all f frequencies yields near-optimal performance. Index Terms--Hidden-node problem (HN), IEEE 802.11, modeling, performance evaluation, protocol design.

Voice over Internet Protocol (VoIP) over a wireless local area network (WLAN) is poised to become an important Internet application. However, two major technical problems that stand in the way are: 1) low VoIP capacity in WLAN and 2) unacceptable VoIP performance in the presence of coexisting traffic from other applications. With each VoIP stream typically requiring less than 10 kb/s, an 802.11b WLAN operated at 11 Mb/s could in principle support more than 500 VoIP sessions. In actuality, no more than a few sessions can be supported due to various protocol overheads (for GSM 6.10, it is about 12). This paper proposes and investigates a scheme that can improve the VoIP capacity by close to 100% without changing the standard 802.11 CSMA/CA protocol. In addition, we show that VoIP delay and loss performance in WLAN can be compromised severely in the presence of coexisting transmission-control protocol (TCP) traffic, even when the number of VoIP sessions is limited to half its potential capacity. A touted advantage of VoIP over traditional telephony is that it enables the creation of novel applications that integrate voice with data. The inability of VoIP and TCP traffic to coexist harmoniously over the WLAN poses a severe challenge to this vision. Fortunately, the problem can be largely solved by simple solutions that require only changes to the medium-access control (MAC) protocol at the access point. Specifically, in our proposed solutions, the MAC protocol at the wireless end stations does not need to be modified, making the solutions more readily deployable over the existing network infrastructure. Index Terms--Capacity, IEEE 802.11, quality of service (QoS), voice over Internet Protocol (VoIP), wireless local area network (WLAN).

This paper studies the minimum-age scheduling problem in a wireless sensor network where an access point (AP) monitors the state of an object via a set of sensors. The freshness of the sensed state, measured by the age-of-information (AoI), varies at different sensors and is not directly observable to the AP. The AP has to decide which sensor to query/sample in order to get the most updated state information of the object (i.e., the state information with the minimum AoI). In this paper, we formulate the minimum-age scheduling problem as a multi-armed bandit problem with partially observable arms and explore the greedy policy to minimize the expected AoI sampled over an infinite horizon. To analyze the performance of the greedy policy, we 1) put forth a relaxed greedy policy that decouples the sampling processes of the arms, 2) formulate the sampling process of each arm as a partially observable Markov decision process (POMDP), and 3) derive the average sampled AoI under the relaxed greedy policy as a sum of the average AoI sampled from individual arms. Numerical and simulation results validate that the relaxed greedy policy is an excellent approximation to the greedy policy in terms of the expected AoI sampled over an infinite horizon. [ABSTRACT FROM AUTHOR]

Software-defined Internet-of-Things networking (SDIoT) greatly simplifies the network monitoring in large-scale IoT networks by per-flow sampling, wherein the controller keeps track of all the active flows in the network and samples the IoT devices on each flow path to collect real-time flow statistics. There is a tradeoff between the controller’s sampling preference and the balancing of loads among devices. On the one hand, the controller may prefer to sample some of the IoT devices on the flow path because they yield more accurate flow statistics. On the other hand, it is desirable to sample the devices uniformly so that their energy consumptions and lifespan are balanced. This paper formulates the flow sampling problem in large-scale SDIoT networks by means of a Markov decision process and devises policies that strike a good balance between these two goals. Three classes of policies are investigated: the optimal policy, the state-independent policies, and the index policies (including the Whittle index and a second-order index policies). The second-order index policy is the most desired policy among all: 1) in terms of performance, it is on an equal footing with the Whittle index policy, and outperforms the state-independent policies by much; 2) in terms of complexity, it is much simpler than the optimal policy, and is comparable to state-independent policies and the Whittle index policy; 3) in terms of realizability, it requires no prior information on the network dynamics, hence is much easier to implement in practice. [ABSTRACT FROM AUTHOR]

This paper investigates a new class of carrier-sense multiple access (CSMA) protocols that employ deep reinforcement learning (DRL) techniques, referred to as carrier-sense deep-reinforcement learning multiple access (CS-DLMA). The goal of CS-DLMA is to enable efficient and equitable spectrum sharing among a group of co-located heterogeneous wireless networks. Existing CSMA protocols, such as the medium access control (MAC) protocol of WiFi, are designed for a homogeneous network in which all nodes adopt the same protocol. Such protocols suffer from severe performance degradation in a heterogeneous environment where there are nodes adopting other MAC protocols. CS-DLMA aims to circumvent this problem by making use of DRL. In particular, this paper adopts α-fairness as the general objective of CS-DLMA. With α-fairness, CS-DLMA can achieve a range of different objectives (e.g., maximizing sum throughput, achieving proportional fairness, or achieving max-min fairness) when coexisting with other MACs by changing the value of α. A salient feature of CS-DLMA is that it can achieve these objectives without knowing the coexisting MACs through a learning process based on DRL. The underpinning DRL technique in CS-DLMA is deep Q-network (DQN). However, the conventional DQN algorithms are not suitable for CS-DLMA due to their uniform time-step assumption. In CSMA protocols, time steps are non-uniform in that the time duration required for carrier sensing is smaller than the duration of data transmission. This paper introduces a non-uniform time-step formulation of DQN to address this issue. Our simulation results show that CS-DLMA can achieve the general α-fairness objective when coexisting with TDMA, ALOHA, and WiFi protocols by adjusting its own transmission strategy. Interestingly, we also find that CS-DLMA is more Pareto efficient than other CSMA protocols, e.g., p-persistent CSMA, when coexisting with WiFi. Although this paper focuses on the use of our non-uniform time-step DQN formulation in wireless networking, we believe this new DQN formulation can also find use in other domains. [ABSTRACT FROM AUTHOR]

This article studies information freshness in information update systems with nonorthogonal multiple access (NOMA). Information freshness is characterized by age of information (AoI), defined as the time elapsed since the generation of the last successfully received update. Conventional orthogonal multiple access (OMA) systems, say time-division multiple access (TDMA) systems, lead to high average AoI when a large number of users take turns to transmit their latest samples to a common receiver over a wireless medium. In contrast to OMA, NOMA allows multiple users to transmit simultaneously. Although NOMA could lead to higher packet error rates (PER) due to the wireless interference among users, we show that higher PERs do not always lead to a higher average AoI. Specifically, our experiments on software-defined radio indicate that NOMA with conventional multiuser decoding (MUD) techniques leads to higher PERs but lower average AoI than OMA does in the high SNR regime. Furthermore, to improve the AoI performance in the medium SNR regime, we combine MUD with physical-layer network coding (PNC), a technique that turns wireless interference into useful network-coding information. PNC works well even when the SNRs of different NOMA users do not differ much. This article is the first attempt to apply PNC to information update systems. Experiments show that the combined use of PNC and MUD reduces the average AoI significantly in a practical network setting. Overall, PNC-enabled NOMA is a promising solution to information update systems. [ABSTRACT FROM AUTHOR]

This study applies reinforcement learning (RL) from the AI machine learning field to derive an optimal Bitcoin‐like blockchain mining strategy. A salient feature of the RL learning framework is that an optimal (or near‐optimal) strategy can be obtained without knowing the details of the blockchain network model. Previously, the most profitable mining strategy was believed to be honest mining encoded in the default blockchain protocol. It was shown later that it is possible to gain more mining rewards by deviating from honest mining. In particular, the mining problem can be formulated as a Markov Decision Process (MDP) which can be solved to give the optimal mining strategy. However, solving the mining MDP requires knowing the values of various parameters that characterize the blockchain network model. In real blockchain networks, these parameter values are not easy to obtain and may change over time. This hinders the use of the MDP model‐based solution. In this study, we employ RL to dynamically learn a mining strategy with performance approaching that of the optimal mining strategy. Since the mining MDP problem has a nonlinear objective function (rather than linear functions of standard MDP problems), we design a new multidimensional RL algorithm to solve the problem. Experimental results indicate that, without knowing the parameter values of the mining MDP model, our multidimensional RL mining algorithm can still achieve optimal performance over time‐varying blockchain networks. [ABSTRACT FROM AUTHOR]

This article puts forth a new channel coding paradigm for multi-source information streams with Age of Information (AoI) requirements. The recently introduced AoI metric characterizes the freshness of information, defined as the time elapsed since the generation of the last successfully received update. We study a setup in which a large number of sensors want to send update information to a common monitor with the help of aggregators. Specifically, an aggregator collects update packets from sensors and forwards them to the monitor. Conventional block codes (such as LDPC codes) that encode and decode each update packet separately do not perform well in such an information aggregation and update scenario. When update packets suffer from packet loss, we show that block codes lead to high instantaneous AoI because a sensor waits for a long time for the next update opportunity. This article investigates stream-based codes to tackle this problem. A distinguishing feature of stream-based codes is the joint encoding of update packets from different sensors, and a series of coded packets are sent continuously like a stream. Different update packets are then jointly decoded using multiple coded packets from the stream. A key challenge with AoI requirements is the joint design of error corrections of old packets and fast decodings of new packets. We design a practical encoding-decoding scheme and a sliding decoding window mechanism to control the decoding complexity. We evaluate two AoI metrics, average AoI and bounded AoI. In particular, bounded AoI corresponds to an AoI threshold that the instantaneous AoI is below a large percentage of the time. Experimental results on software-defined radio show that stream-based codes significantly outperform block codes in both average AoI and bounded AoI under varying channel conditions. Overall, stream-based codes provide a viable channel coding solution to multi-source information streams with timely update requirements. [ABSTRACT FROM AUTHOR]

Life-critical warning message, abbreviated as warning message, is a special event-driven message that carries emergency information in Vehicle-to-Everything (V2X). Three important characteristics that distinguish warning messages from ordinary vehicular messages are sporadicity, crowding, and ultra-time-criticality. Specifically, warning messages come only once in a while in a sporadic manner; however, when they come, they tend to come as a crowd and they need to be delivered in short order. This paper puts forth a medium-access control (MAC) protocol for warning messages. The overall MAC protocol operates by means of interrupt-and-access. To circumvent potential inefficiency arising from message sporadicity, we adopt an override network architecture whereby warning messages are delivered on the spectrum of the ordinary vehicular messages. A vehicle with a warning message first sends an interrupt signal to pre-empt the transmission of ordinary messages, so that the warning message can use the wireless spectrum originally allocated to ordinary messages. In this way, no exclusive spectrum resources need to be pre-allocated to the sporadic warning messages. Following the interrupt, for transmissions of ultra-time-critical crowd messages, we employ advanced channel access techniques to ensure reliable message delivery within an ultra-short time in the order of 10 ms. [ABSTRACT FROM AUTHOR]

This paper puts forth a class of new transceiver designs for interleaved frequency division multiple access (IFDMA) systems. These transceivers are significantly less complex than conventional IFDMA transceivers. The simple new designs are founded on a key observation that multiplexing and demultiplexing of IFDMA data streams of different sizes are coincident with the IFFTs and FFTs of different sizes embedded within the Cooley-Tukey recursive FFT decomposition scheme. For flexible resource allocation, this paper puts forth a new IFDMA resource allocation framework called Multi-IFDMA, in which a user can be allocated multiple IFDMA streams. Our new transceivers are unified designs in that they can be used in conventional IFDMA as well as multi-IFDMA systems. Two other well-known multiple-access schemes are localized FDMA (LFDMA) and orthogonal FDMA (OFDMA). In terms of flexibility in resource allocation, Multi-IFDMA, LFDMA, and OFDMA are on an equal footing. With our new transceiver designs, however, IFDMA has the following advantages (besides other known advantages not due to our new transceiver designs): 1) IFDMA/Multi-IFDMA transceivers are significantly less complex than LFDMA transceivers; in addition, IFDMA/Multi-IFDMA has better Peak-to-Average Power Ratio (PAPR) than LFDMA; 2) IFDMA/Multi-IFDMA transceivers and OFDMA transceivers are comparable in complexity; but IFDMA/Multi-IFDMA has significantly better PAPR than OFDMA. [ABSTRACT FROM AUTHOR]

Interleaved Frequency Division Multiple Access (IFDMA) and Orthogonal FDMA (OFDMA) belong to a class of signal modulation and multiple-access techniques in which information of multiple users are multiplexed and carried on subcarriers within a shared spectrum. Compared with OFDMA, IFDMA has lower Peak-to-Average Power Ratio (PAPR). However, IFDMA poses two rigid constraints on subcarrier allocation: 1) the subcarriers occupied by a user must be evenly-spaced among the available subcarriers. 2) the number of subcarriers used by a user must be a divisor of the total number of subcarriers. Unless these constraints can be overcome, IFDMA may remain impractical despite its excellent PAPR. This paper investigates how to overcome these constraints to allow flexible and fine-grained subcarrier allocation in IFDMA. Specifically, we put forth $i$) a bit-reversal subcarrier allocation scheme whereby the problem of allocating evenly-spaced subcarriers is transformed to a more intuitive problem of filling contiguous bins; $ii$) a multi-stream IFDMA scheme whereby a user can have an arbitrary number of subcarriers. For the synchronous scenario in which user requests arrive in a synchronous manner, we show that IFDMA can achieve the same level of flexibility and granularity as OFDMA in subcarrier allocation. For the asynchronous scenario in which user requests arrive and depart asynchronously, we show that the blocking probability of IFDMA is only slightly worse than that of OFDMA: specifically, the gap between the blocking probabilities of IFDMA and OFDMA is only 2.56% at a moderate offered load. [ABSTRACT FROM AUTHOR]

Significant sampling is an adaptive monitoring technique proposed for highly dynamic networks with centralized network management and control systems. The essential spirit of significant sampling is to collect and disseminate network state information when it is of significant value to the optimal operation of the network, and in particular when it helps identify the shortest routes. Discovering the optimal sampling policy that specifies the optimal sampling frequency is referred to as the significant sampling problem. Modeling the problem as a Markov Decision process, this paper puts forth a deep reinforcement learning (DRL) approach to tackle the significant sampling problem. This approach is more flexible and general than prior approaches as it can accommodate a diverse set of network environments. Experimental results show that, 1) by following the objectives set in the prior work, our DRL approach can achieve performance comparable to their analytically derived policy $\phi '$ – unlike the prior approach, our approach is model-free and unaware of the underlying traffic model; 2) by appropriately modifying the objective functions, we obtain a new policy which addresses the never-sample problem of policy $\phi '$ , consequently reducing the overall cost; 3) our DRL approach works well under different stochastic variations of the network environment – it can provide good solutions under complex network environments where analytically tractable solutions are not feasible. [ABSTRACT FROM AUTHOR]

This paper derives the maximum achievable sum-rate and presents the optimal encoding/decoding framework for rate-diverse wireless network coding (RD-WNC) over broadband channels consisting of multiple parallel subchannels. RD-WNC applies to a communication scenario in which a base station wants to deliver two different messages with different rates to two users. The base station combines the two separate messages into one network-coded message and broadcasts the network-coded message to both users. Each user then extracts its desired message from the network-coded message by subtracting from it the other message, which we assume to be side information available to the user. Deriving the maximum achievable sum-rate for RD-WNC is challenging when the channel consists of multiple parallel subchannels with different channel coefficients (e.g., the subcarrier channels of OFDM systems), since apart from the rate allocation between the two users, optimal power allocation among multiple subchannels needs to be identified. The first contribution of this paper is a new “mountain-leveling” power allocation algorithm to achieve the maximum sum-rate. With the resulting power allocation, we can then achieve the corresponding optimal sum-rate by having a separate encoding/decoding mechanism for each subchannels, but doing so is cumbersome and complex when the number of subchannels is large. The second contribution of this paper is a practical encoding/decoding framework using only one encoder-decoder mechanism for all subchannels without sacrificing sum-rate optimality. We provide numerical results to corroborate our theoretical findings and to demonstrate the benefits of our encoding/decoding framework. [ABSTRACT FROM AUTHOR]

This paper explores the application of physical-layer network coding (PNC) for short-packet transmissions. PNC can potentially reduce the communication delay in relay-assisted wireless networks and can thus be instrumental in realizing short-packet communication systems with stringent delay requirements. In this work, first, we first derive an achievability bound for channel-coded short-packet PNC systems. Based on the random-coding error-exponent, the bound serves as a benchmark for short-packet PNC operating with traditional preamble-aided channel estimation and XOR channel decoding. Second, we design a blind channel estimation algorithm and a code-aided channel estimation algorithm for short-packet PNC systems. Both outperform the traditional preamble-aided channel estimation for PNC systems operating with mismatched channel-state-information. As a case study, we compare the three algorithms for packets of 128 symbols over a two-way relay channel. The results show that the blind algorithm outperforms the code-aided algorithm and preamble-aided algorithm by almost 0.2 and 1.5 dB respectively. Furthermore, the blind algorithm achieves the target packet error rate of 10−4 within 0.5 dB of the random coding bound of an imaginary system in which perfect channel-state-information is available at the relay at no cost (i.e., channel estimation is not required in the imaginary system). The bound and the algorithms give us a fundamental framework for applying PNC to short-packet transmissions. [ABSTRACT FROM AUTHOR]

This paper investigates coherent detection for physical-layer network coding (PNC) with short packet transmissions in a two-way relay channel (TWRC). PNC turns superimposed EM waves into network-coded messages to improve throughput in a relay system. To achieve this, accurate channel information at the relay is a necessity. Much prior work applies preambles to estimate the channel. For long packets, the preamble overhead is low because of the large data payload. For short packets, that is not the case. To avoid excessive overhead, we consider a set-up in which short packets do not have preambles. A key challenge is how the relay can estimate the channel and detect the network-coded messages jointly based on the received signals from the two end users. We design a coherent detector that makes use of a belief propagation (BP) algorithm to do so. For concreteness, we focus on binary frequency-shift-keying (FSK) modulation. We show how the BP algorithm can be simplified and made practical with Gaussian-mixture passing. In addition, we demonstrate that prior knowledge on the channel distribution is not needed with our framework. Benchmarked against the detector with prior knowledge of the channel distribution, numerical results show that our detector can have nearly the same performance without such prior knowledge. [ABSTRACT FROM AUTHOR]

This paper studies the transceiver design of the Gaussian two-pair two-way relay channel (TWRC), where two pairs of users exchange information through a common relay in a pairwise manner. Our main contribution is to show that the capacity of the Gaussian two-pair TWRC is achievable to within $\frac{1}{ 2}$ bit for arbitrary channel conditions. For the outer bound, we derive a genie-aided bound of the Gaussian two-pair TWRC, which is tighter than the cut-set bound. For the inner bound, we develop a hybrid coding scheme involving Gaussian random coding, nested lattice coding, superposition coding, and network-coded decoding. We further present a message-reassembling strategy to decouple the coding design for the user-to-relay and relay-to-user links, so as to provide flexibility to fully exploit the channel randomness. We show that judicious power allocation at the users and at the relay is necessary to approach the channel capacity under various channel conditions. [ABSTRACT FROM AUTHOR]

In this paper, we study the multiuser detection (MUD) problem for a grant-free massive-device multiple access (MaDMA) system, where a large number of single-antenna user devices transmit sporadic data to a multi-antenna base station (BS). Specifically, we put forth two MUD schemes, termed random sparsity learning multiuser detection (RSL-MUD) and structured sparsity learning multiuser detection (SSL-MUD) for the time-slotted and non-time-slotted grant-free MaDMA systems, respectively. In RSL-MUD, active users generate and transmit data packets with random sparsity. In SSL-MUD, we introduce a sliding-window-based detection framework, and the user signals in each observation window naturally exhibit structured sparsity. We show that by exploiting the sparsity embedded in the user signals, we can recover the user activity state, the channel, and the user data in a single phase, without using pilot signals for channel estimation and/or active user identification. To this end, we develop a message-passing-based statistical inference framework for the BS to blindly detect the user data without any prior knowledge of the identities and the channel state information (CSI) of active users. The simulation results show that our RSL-MUD and SSL-MUD schemes significantly outperform their counterpart schemes in both reducing the transmission overhead and improving the error behavior of the system. [ABSTRACT FROM AUTHOR]

This paper investigates a deep reinforcement learning (DRL)-based MAC protocol for heterogeneous wireless networking, referred to as a Deep-reinforcement Learning Multiple Access (DLMA). Specifically, we consider the scenario of a number of networks operating different MAC protocols trying to access the time slots of a common wireless medium. A key challenge in our problem formulation is that we assume our DLMA network does not know the operating principles of the MACs of the other networks—i.e., DLMA does not know how the other MACs make decisions on when to transmit and when not to. The goal of DLMA is to be able to learn an optimal channel access strategy to achieve a certain pre-specified global objective. Possible objectives include maximizing the sum throughput and maximizing $\alpha $ -fairness among all networks. The underpinning learning process of DLMA is based on DRL. With proper definitions of the state space, action space, and rewards in DRL, we show that DLMA can easily maximize the sum throughput by judiciously selecting certain time slots to transmit. Maximizing general $\alpha $ -fairness, however, is beyond the means of the conventional reinforcement learning (RL) framework. We put forth a new multi-dimensional RL framework that enables DLMA to maximize general $\alpha $ -fairness. Our extensive simulation results show that DLMA can maximize sum throughput or achieve proportional fairness (two special classes of $\alpha $ -fairness) when coexisting with TDMA and ALOHA MAC protocols without knowing they are TDMA or ALOHA. Importantly, we show the merit of incorporating the use of neural networks into the RL framework (i.e., why DRL and not just traditional RL): specifically, the use of DRL allows DLMA (i) to learn the optimal strategy with much faster speed and (ii) to be more robust in that it can still learn a near-optimal strategy even when the parameters in the RL framework are not optimally set. [ABSTRACT FROM AUTHOR]

A block-fading (BF) channel, also known as a slow-fading channel, is a type of simple and practical channel model that can characterize the primary feature of a number of wireless-communication applications with low to moderate mobility. Although BF channels have received significant research attention in the past 20 years, designing low-complexity, outage-limit-approaching error-correction codes (ECCs) is still a challenging issue. For this reason, a novel family of protograph low-density paritycheck (LDPC) codes, called rootprotograph (RP) LDPC codes, has been conceived recently. The RP codes not only can realize linearcomplexity encoding and highspeed decoding with the help of the quasi-cyclic (QC) structure but can also achieve near-outage-limit performance in different BF scenarios. In this article, we briefly review the design guidelines of such protograph codes with the aim of inspiring further research activities in this area. [ABSTRACT FROM AUTHOR]

In this paper, we propose a generalized channel decoding scheme for nonbinary physical-layer network coding (CD-NC) in two-way relay channels (TWRCs), where two source nodes A and B exchange their nonbinary symbols via a relay. The two sources use the same nonbinary low-density parity-check (LDPC) channel code over the integer ring $\mathbb {Z}_M$ and $M$ -pulse-amplitude modulation, respectively. The existing channel decoding schemes for nonbinary network coding suffer severe rate loss compared with the cut-set bound of TWRC, especially in the low-to-medium signal-to-noise ratio regime. The proposed CD-NC can decrease the rate loss. Our contributions are as follows: 1) We develop a generalized nonbinary sum product algorithm (G-SPA) for CD-NC according to the principle of virtual encoding of the superimposed symbols. Simulation results show that our CD-NC can achieve significant performance gains over the conventional nonbinary network coding for both additive white Gaussian noise and fading channels; and 2) We exploit two-dimensional fast-Fourier-transform-based belief propagation (2-D-FFT-BP) and extended min-sum (EMS) decoding algorithms to reduce the decoding complexity of G-SPA. Simulation results show that the 2-D-FFT-BP has the same performance as G-SPA, while EMS can greatly reduce the decoding complexity of G-SPA at the cost of slight performance degradation. [ABSTRACT FROM AUTHOR]

This paper studies the beamforming design for the multiple-input multiple-output (MIMO) multiway relay channel with clustered full data exchange. We formulate the linear signal alignment in the model under a rank-constrained rank-minimization (RCRM) framework. Our contribution is twofold. We decompose the RCRM problem into independent rank-minimization subproblems and put forth an iterative algorithm for the beamforming design. For symmetric antenna setups, our approach advances the state of the art by expanding the achievable degree of freedom (DoF) region, and for asymmetric antenna setups, our approach can be directly applied while prior approaches are not readily applicable. [ABSTRACT FROM AUTHOR]

This paper investigates the noncoherent detection in a two-way relay channel operated with physical-layer network coding (PNC), assuming FSK modulation and short-packet transmissions. For noncoherent detection, the detector has access to the magnitude but not the phase of the received signal. For conventional communication in which a receiver receives the signal from a transmitter only, the phase does not affect the magnitude, and hence the performance of the noncoherent detector is independent of the phase. PNC, on the other hand, is a multiuser system in which a receiver receives signals from multiple transmitters simultaneously. The relative phase of the signals from different transmitters affects the received signal magnitude through constructive-destructive interference. In particular, for good performance, the noncoherent detector of a multiuser system such as PNC must take into account the influence of the relative phase on the signal magnitude. Building on this observation, this paper delves into the fundamentals of PNC noncoherent detector design. To avoid excessive overhead, we assume a set-up in which the short packets in the PNC system do not have preambles. We show how the relative phase can be deduced directly from the magnitudes of the received data symbols, and that the knowledge of the relative phase thus deduced can in turn be used to enhance performance of noncoherent detection. Our overall detector design consists of two components: 1) a channel gains estimator that estimates channel gains without preambles; and 2) a detector that builds on top of the estimated channel gains to jointly estimate relative phase and detect data using a belief propagation algorithm. Numerical results show that our detector performs nearly as well as a “fictitious” optimal detector that has perfect knowledge of the channel gains and relative phase. Although this paper focuses on PNC with FSK modulation, we believe that the insight of this paper applies generally to noncoherent detection in other multiuser systems with other modulations. Specifically, our insight is that the relative phase of overlapped signals affects the signal magnitude in multiuser systems, but fortunately the relative phase can be deduced from the magnitudes and this knowledge can be used to improve the detection performance. [ABSTRACT FROM AUTHOR]

This paper presents the first network-coded multiple access (NCMA) downlink system on unmanned aerial vehicle (UAV). The use of UAV as a mobile aerial base station has received much attention in the 5G community in the context of highly mobile and flexible-configurable communication systems. As UAVs are limited by their flight time in the air, achieving high spectral and power efficiency while they are inflight is of great importance. Non-orthogonal multiple access (NOMA) is a promising technique to increase the spectral and power efficiency. Conventional NOMA downlink that makes use of superposition coding in combination with successive interference cancellation (SIC) decoding does not work well in scenarios where the channel conditions of different downlink users are not readily available at the transmitter side. This is the case, for example, in the UAV scenario in which the UAV transmitter moves quickly, causing the channel conditions to vary in a very dynamic manner. This paper investigates a new NOMA downlink architecture, referred to asnetwork-coded multiple access. In the absence of channel information, an NCMA transmitter allocates equal power to the superposed signals of different downlink users. A key challenge is how to achieve high NOMA throughput under such equal power allocation. Toward this end, NCMA makes joint use of physical-layer network coding (PNC) and multiuser decoding (MUD) together with a new superposition coding scheme, referred to asNCMA-based superposition coding. In NCMA-based superposition coding, equal powers are allocated to the signals of different users, but a relative phase offset between the signals is introduced to optimize PNC and MUD decodings. To demonstrate the feasibility and advantage of the NCMA downlink, we implemented our designs on a software-defined radio and UAV. Our experimental results show that NCMA is robust against varying channel conditions. Moreover, the throughput of NCMA can outperform the state-of-the-art SIC-based superposition coding system and the time-division multiple access system by 50% and 80%, respectively, demonstrating that NCMA is a practical solution to boost throughput in UAV NOMA. [ABSTRACT FROM AUTHOR]

Physical-layer network coding (PNC) is a communications paradigm that exploits overlapped transmissions to boost the throughput of wireless relay networks. A high point of PNC research was a theoretical proof that PNC that makes use of nested lattice codes could approach the information-theoretic capacity of a two-way relay network (TWRN), where two end nodes communicate via a relay node. The capacity cannot be achieved by conventional methods of time-division or straightforward network coding. Many practical challenges, however, remain to be addressed before the full potential of lattice-coded PNC can be realized. Two major challenges are: (1) for good performance in lattice-coded PNC, channels of simultaneously transmitting nodes must be aligned; (2) for lattice-coded PNC to be practical, the complexity of lattice encoding at the transmitters and lattice decoding at the receiver must be reduced. We address these challenges and implement a first lattice-coded PNC system on a software-defined radio (SDR) platform. Specifically, we design and implement a low-overhead channel precoding system that accurately aligns the channels of distributed nodes. In our implementation, the nodes use low-cost temperature-compensated oscillators (TCXO) only—a consequent challenge is that the channel alignment must be done more frequently and more accurately compared with the use of expensive oscillators. The low overhead and accurate channel alignment are achieved by (1) a channel precoding system implemented over FPGA to realize fast feedback of channel state information; (2) a highly-accurate carrier frequency offset (CFO) estimation method; and (3) a partial-feedback channel estimation method that significantly reduces the amount of feedback information from the receiver to the transmitters for channel precoding at the transmitters. To reduce lattice encoding and decoding complexities, we adapt the low-density lattice code (LDLC) for use in PNC systems. Experiments show that our implemented lattice-coded PNC achieves better bit error rate performance compared with time-division and straightforward network coding systems. It also has good throughput performance in mobile non-LoS scenarios. [ABSTRACT FROM AUTHOR]

This paper presents the Distributed Cooperative Access Points (DCAP) system that can simultaneously serve multiple clients using cooperative beamforming to increase the capacity of WiFi-type wireless networks. The distributed APs are connected by Ethernet and driven by independent low-cost local oscillators. To facilitate cooperative beamforming, we address three major challenges: the phase synchronization, the channel state information (CSI) measurement, and the user selection. Specifically, we develop 1) a cooperative tracking scheme to track signal phase drifts at symbol level without adding extra hardware complexity; 2) an incremental CSI estimation mechanism that removes the per-frame CSI measurement overhead of previous approaches; and 3) a simple random user selection algorithm that scales the network capacity linearly and delivers over $70$ percent performance compared to the optimal but complex greedy algorithm. We implement DCAP on the Sora software radio platform and evaluate it in a wireless network with nine nodes. Experimental results show that the cooperative beamforming is feasible in practice, and our cooperative phase tracking can ensure strict phase alignment ( $\leq$ 0.03 radian) among APs during the entire beamforming period (1.2 ms). Otherwise, without tracking, phases may drift by 0.3 radian over merely 600 $\mu$ s, causing that the symbol SNR decreases as large as 20 dB. [ABSTRACT FROM AUTHOR]

This paper presents the first network-coded multiple access (NCMA) system prototype operated on high-order modulations up to 16-QAM. NCMA jointly exploits physical-layer network coding (PNC) and multiuser decoding (MUD) to boost throughput of multipacket reception systems. Direct generalization of the existing NCMA decoding algorithm, originally designed for BPSK, to high-order modulations, will lead to huge performance degradation. The throughput degradation is caused by the relative phase offset between received signals from different nodes. To circumvent the phase offset problem, this paper investigates an NCMA system with multiple receive antennas at the access point, referred to as MIMO-NCMA. We put forth a low-complexity symbol-level NCMA decoder that, together with MIMO, can substantially alleviate the performance degradation induced by relative phase offset. To demonstrate the feasibility and advantage of MIMO-NCMA for high-order modulations, we implemented our designs on software-defined radio. Our experimental results show that the throughput of QPSK MIMO-NCMA is double that of both BPSK NCMA and QPSK MUD at SNR = 10 dB. For higher signal-to-noise ratios at which 16-QAM can be supported, the throughput of MIMO-NCMA can be as high as 3.5 times that of BPSK NCMA. Overall, this paper provides an implementable framework for high-order modulated NCMA. [ABSTRACT FROM PUBLISHER]

This paper studies protograph low-density parity-check coded cooperation (CC) schemes for two-hop multi-relay systems with $L$ relays over Nakagami- $m$ quasi-static fading (QSF) channels. We propose two CC schemes, namely schemes I and II, with different maximum code rates to satisfy different transmission requirements. We further design a family of distributed rate-compatible root-protograph (RCRP) codes to achieve full diversity in CC-based multi-relay QSF channels. In particular, our RCRP codes with $L+1$ sub-codewords can realize full diversity in scheme I, and our RCRP codes with two sub-codewords can achieve full diversity in scheme II with a maximum-ratio combiner. In addition, we estimate the asymptotic word error rate and bit error rate of our RCRP codes using a generalized protograph extrinsic information transfer algorithm, which is able to characterize the error performance of finite-length codewords accurately. Analysis and simulation show that our RCRP codes can achieve outage-limit-approaching performance in both multi-relay CC architectures. This makes the RCRP coding framework extremely attractive for multi-relay cooperative communication applications with slow-varying fading. [ABSTRACT FROM PUBLISHER]

This paper is a theoretical-plus-experimental investigation of practical 5G strategies for power-balanced non-orthogonal multiple access (NOMA). By allowing multiple users to share the same time and frequency, NOMA can scale up the number of served users and increase spectral efficiency compared with existing OMA. Conventional NOMA schemes with successive interference cancellation (SIC) do not work well when users with comparable received powers transmit together. To allow power-balanced NOMA (more exactly, near power-balanced NOMA), this paper investigates a new NOMA architecture, named network-coded multiple access (NCMA). A distinguishing feature of NCMA is the joint use of physical-layer network coding (PNC) and multiuser decoding to boost NOMA throughputs. We first show that a simple NCMA architecture in which all users use the same modulation, referred to as rate-homogeneous NCMA, can achieve substantial throughput improvement over SIC-based NOMA under near power-balanced scenarios. Then, we put forth a new NCMA architecture, referred to as rate-diverse NCMA, in which different users may adopt different modulations commensurate with their relative SNRs. A challenge for rate-diverse NCMA is the design of a channel-coded PNC system. This paper is the first attempt to design channel-coded rate-diverse PNC. Experimental results on our software-defined radio prototype show that the throughput of rate-diverse NCMA can outperform the state-of-the-art rate-homogeneous NCMA by 80%. Overall, rate-diverse NCMA is a practical solution for near power-balanced NOMA. [ABSTRACT FROM PUBLISHER]

In asynchronous physical-layer network coding (APNC) systems, the symbols from multiple transmitters to a common receiver may be misaligned. Knowledge of the amount of symbol misalignment, hence its estimation, is important to PNC decoding. This paper addresses the problems of symbol-misalignment estimation and optimal PNC decoding given the misalignment estimate, assuming the APNC system uses the root-raised-cosine pulse to carry signals (RRC-APNC). Our contributions are as follows. First, we put forth an optimal symbol-misalignment estimator that makes use of double baud-rate samples. Second, we devise optimal RRC-APNC decoders in the presence of non-exact symbol-misalignment estimates. In particular, we show how to whiten the colored noise in the double baud-rate samples to simplify the design of optimal decoders. Third, we investigate the decoding performance of various estimation-and-decoding schemes for RRC-APNC. Extensive simulations show that: 1) our double baud-rate estimator yields substantially more accurate symbol-misalignment estimates than the baud-rate estimator does; the mean square error gains are up to 8 dB and 2) an overall estimation-and-decoding scheme in which both estimation and decoding are based on double baud-rate samples yields much better performance than other schemes. Compared with a scheme in which both estimation and decoding are based on baud-rate samples, the double baud-rate sampling scheme yields 4.5 dB gains on symbol error rate performance in an additive white Gaussian noise channel, and 2 dB gains on packet error rate performance in a Rayleigh fading channel. [ABSTRACT FROM PUBLISHER]

In this paper, we study the degrees of freedom (DoF) of the symmetric multi-relay multiple-input multiple-output Y channel, where three user nodes, each with $M$ antennas, communicate via K$ geographically separated relay nodes, each with N$ antennas. For this model, we establish a general DoF achievability framework based on linear precoding and post-processing methods. The framework poses a nonlinear problem with respect to user precoders, user post-processors, and relay precoders. To solve this problem, we adopt an uplink–downlink asymmetric strategy, where the user precoders are designed for signal alignment and the user post-processors are used for interference neutralization. With the user precoder and post-processor designs fixed as such, the original problem then reduces to a problem of relay precoder design. To address the solvability of the system, we propose a general method for solving matrix equations. Together with the techniques of antenna disablement and symbol extension, an achievable DoF of the considered model is derived for an arbitrary setup of (K, M, N)$ . We show that for K\geq 2 . We also show that the uplink-downlink asymmetric design proposed in this paper considerably outperforms the conventional approach based on uplink-downlink symmetry. [ABSTRACT FROM PUBLISHER]

This paper presents the results of a comprehensive investigation of complex linear physical-layer network coding (PNC) in two-way relay channels. In this system, two nodes A and B communicate with each other via a relay R. Nodes A and B send complex symbols, wA and wB , simultaneously to relay R. Based on the simultaneously received signals, relay R computes a linear combination of the symbols, wN=\alpha wA+\beta wB , as a network-coded symbol and then broadcasts wN to nodes A and B. Node A then obtains wB from wN and its self-information wA by wB=\beta ^{-1}(wN-\alpha wA) . Node B obtains wB in a similar way. A critical question at relay R is as follows: “given channel gain ratio \eta = hA/hB , where hA and hB are the complex channel gains from nodes A and B to relay R, respectively, what is the optimal coefficients (\alpha ,\beta ) that minimizes the symbol error rate (SER) of \alpha ,\beta ,w_{A}, w_{B} , and wN are the elements of a finite field of Gaussian integers, that is, the field of \mathbb {Z}[i]/q , where q is a Gaussian prime. Previous vector formulation, in which \alpha and \beta were represented by 2\times 2 matrices, corresponds to a subcase of our Gaussian-integer formulation, where q is real prime only. Extension to the Gaussian prime q , where q . We uncover the structure of the Voronoi regions that allows us to compute a minimum-distance metric that characterizes the SER of (\alpha \mathrm{ opt},\beta \mathrm{ opt}) . Overall, the contributions in 1) and 2) yield a toolset for a comprehensive understanding of complex linear PNC in \mathbb {Z}[i]/q . We believe investigation of linear PNC beyond \mathbb Z[i]/q can follow the same approach. [ABSTRACT FROM AUTHOR]

This paper presents the first reliable physical-layer network coding (PNC) system that supports real TCP/IP applications for the two-way relay network (TWRN). Theoretically, PNC could boost the throughput of TWRN by a factor of 2 compared with traditional scheduling (TS) in the high signal-to-noise (SNR) regime. Although there have been many theoretical studies on PNC performance, there have been relatively few experimental and implementation efforts. Our earlier PNC prototype, built in 2012, was an offline system that processed signals offline. For a system that supports real applications, signals must be processed online in real-time. Our real-time reliable PNC prototype, referred to as RPNC, solves a number of key challenges to enable the support of real TCP/IP applications. The enabling components include: 1) a time-slotted system that achieves $\mu s$ -level synchronization for the PNC system; 2) reduction of PNC signal processing complexity to meet real-time constraints; 3) an ARQ design tailored for PNC to ensure reliable packet delivery; and 4) an interface to the application layer. We took on the challenge to implement all of the above with general-purpose processors in PC through an SDR platform rather than ASIC or FPGA. With all of these components, we have successfully demonstrated image exchange with TCP and two-party video conferencing with UDP over RPNC. Experimental results show that the achieved throughput approaches the PHY-layer data rate at high SNR, demonstrating the high efficiency of the RPNC system. [ABSTRACT FROM AUTHOR]

This paper proposes an encoding/decoding framework for achieving the optimal channel capacities of the two-user broadcast channel where each user (receiver) has the message targeted for the other user (receiver) as side information. Since the link qualities of the channels from the base station to the two users are different, their respective single-user non-broadcast channel capacities are also different. A goal is to simultaneously achieve/approach the single-user non-broadcast channel capacities of the two users with a single broadcast transmission by applying network coding. This is referred to as the rate-diverse wireless network coding problem. For this problem, this paper presents a capacity-achieving framework based on linear-structured nested lattice codes. The significance of the proposed framework, besides its theoretical optimality, is that it suggests a general design principle for linear rate-diverse wireless network coding going beyond the use of lattice codes. We refer to this design principle as the principle of virtual single-user channels. Guided by this design principle, we propose two implementations of our encoding/decoding framework using practical linear codes amenable to decoding with affordable complexities: the first implementation is based on Low Density Lattice Codes (LDLC) and the second implementation is based on Bit-interleaved Coded Modulation (BICM). These two implementations demonstrate the validity and performance advantage of our framework. [ABSTRACT FROM PUBLISHER]

Physical-layer network coding (PNC) channel decoding at the relay is of key importance for good performance in PNC systems. However, PNC channel decoders can have prohibitive computation complexity. Low complexity non-iterative PNC channel decoders are desired in practice. For such PNC decoders, decoding performance may degrade significantly when there is a relative phase offset between the simultaneous signals of multiple nodes received at the relay, particularly when bit-likelihood-based decoding is adopted. In this paper, we thoroughly investigate and numerically quantify the impact of relative phase offset on decoding performance. To maintain good decoding performance under relative phase offset, we introduce and experimentally evaluate symbol-likelihood-based decoding (in contrast to bit-likelihood-based decoding) for PNC systems. Our experimental results show that symbol-likelihood-based decoding improves the packet throughput over bit-likelihood-based decoding by 100% to 400% at SNR of 15 dBs. Moreover, we study the computational complexity under both these decoding methods. We find that a reduced-complexity decoder with symbol-likelihood-based decoding provides the best performance-complexity tradeoff for practical PNC systems. [ABSTRACT FROM PUBLISHER]

Symbol misalignment is inevitable in asynchronous physical-layer network coding (PNC) systems. It is paramount that such symbol misalignment is taken into account in PNC decoding for good performance. Thus, accurate estimation of symbol misalignment is crucial. This paper argues that, when Nyquist pulses (i.e., intersymbol-interference (ISI)-free pulses) are adopted, signal samples only need to be collected at baud rate for optimal symbol misalignment estimation. Based on this principle, we propose a highly accurate symbol misalignment estimation method with low complexity. Our method makes use of the constant amplitude zero autocorrelation sequence (Zadoff–Chu sequence (ZC sequence)). We derive a maximum-likelihood (ML) estimator for symbol misalignment based on the cross-correlation result of the ZC sequence. Unlike previous methods that employ oversampling, our estimation method requires only baud-rate sampling, thus having much lower complexity. Extensive simulations show that our method can accurately estimate both integral and fractional symbol misalignments using sinc pulse and raised-cosine (RC) pulse. The root-mean-square error (RMSE) of the estimation is below 10−2 (in unit of symbol duration) when the SNR is above 15, 18, and 21 dB for 127-, 63-, and 31-bit-length ZC sequences, respectively. Furthermore, our method, being an ML estimation method, has no error floor in the high-SNR regime, whereas the prior methods exhibit an error floor. [ABSTRACT FROM PUBLISHER]

The increasingly dense deployments of wireless CSMA networks arising from applications of Internet-of-things call for an improvement to mitigate the interference among simultaneous transmitting wireless devices. For cost efficiency and backward compatibility with legacy transceiver hardware, a simple approach to address interference is by appropriately configuring the carrier sensing thresholds in wireless CSMA protocols, particularly in dense wireless networks. Most prior studies of the configuration of carrier sensing thresholds are based on a simplified conflict graph model, whereas this paper considers a realistic signal-to-interference-and-noise ratio model. We provide a comprehensive study for two effective wireless CSMA protocols: Cumulative-interference-Power Carrier Sensing and Incremental-interference-Power Carrier Sensing, in two aspects: (1) static approach that sets a universal carrier sensing threshold to ensure interference-safe transmissions regardless of network topology, and (2) adaptive approach that adjusts the carrier sensing thresholds dynamically based on the feedback of nearby transmissions. We also provide simulation studies to evaluate the starvation ratio, fairness, and goodput of our approaches. [ABSTRACT FROM PUBLISHER]

This paper presents several soft decision iterative decoding schemes for physical-layer network coding (PNC) operated with coded modulation (CM) and bit-interleaved coded modulation (BICM). With respect to PNC operated with CM, we consider network coding-based channel decoding (NC-CD) and multi-user complete decoding (MUD-NC) for PNC decoding at the relay. Their BICM counterparts are XOR-based channel decoding (XOR-CD) and MUD-XOR, respectively. First, we show that, when the decoding is non-iterative, there is a gap between the BICM capacities of both XOR-CD and MUD-XOR under Gray mapping and the capacities of their CM counterparts, NC-CD, and MUD-NC. This is in contrast to the conventional point-to-point communication system, for which the BICM capacity with Gray mapping is known to be very close to the CM capacity, without the need for iterative decoding. Second, we investigate the error performance of iteratively decoded BICM XOR-CD and MUD-XOR. Extrinsic information transfer chart analysis and simulation results indicate that for these Gray-mapped BICM PNC systems, iterative decoding can achieve considerable gains over non-iterative decoding. Again, this is in contrast to the Gray-mapped BICM point-to-point communication system, for which iterative decoding provides little gain over non-iterative decoding. We further show that Gray mapping gives rise to best PNC rate for MUD-XOR and XOR-CD systems among several bits-to-symbol mappings under study. Overall, our results indicate that BICM PNC systems exhibit different decoding behavior from conventional BICM point-to-point systems. This paper serves as a first foray into the investigation of this issue. [ABSTRACT FROM PUBLISHER]

This paper investigates a channel-coded multiuser system operated with orthogonal frequency-division multiplexing (OFDM) and interleaved-division multiple access (IDMA). In general, there are many variations to multiuser systems. Our choice of the combination of OFDM and IDMA is motivated by its ability to achieve multiuser diversity gain in frequency-selective multiple-access channels. To realize OFDM-IDMA, however, two challenges must be addressed. The first challenge is the estimation of multiple channel parameters, particularly how to contain the estimation errors of the channel parameters of the multiple users, considering that the overall estimation errors may increase with the number of users because the estimations of their channel parameters are intertwined with each other. The second challenge is that the transmitters of the multiple users may be driven by different radio-frequency oscillators. The associated frequency asynchrony may cause multiple carrier frequency offsets (CFOs) at the receiver. Compared with a single-user receiver where the single CFO can be compensated away at the receiver prior to channel decoding, a particular difficulty for a multiuser receiver is that it is not possible to compensate away all the multiple CFOs simultaneously. To tackle the two challenges, we put forth an integrated framework to solve the problems of multiuser channel-parameter estimation, CFO compensation, and channel decoding jointly and iteratively. The framework employs the space-alternating generalized expectation-maximization (SAGE) algorithm to decompose the multiuser problem into multiple single-user problems and the expectation-conditional maximization (ECM) algorithm to tackle each of the single-user subproblems. Iterative executions of SAGE and ECM in the framework allow the two aforementioned challenges to be tackled in an optimal manner. Simulation results indicate that, compared with other approaches, our approach can achieve significant bit-error-rate (BER) and mean-square-error (MSE) improvements. [ABSTRACT FROM PUBLISHER]

The growing demand for high-speed data, quality of service (QoS) assurance, and energy efficiency has triggered the evolution of fourth-generation (4G) Long-Term Evolution-Advanced (LTE-A) networks to fifth generation (5G) and beyond. Interference is still a major performance bottleneck. This paper studies the application of physical-layer network coding (PNC), which is a technique that exploits interference, in heterogeneous cellular networks. In particular, we propose a rate-maximizing relay selection algorithm for a single cell with multiple relays assuming the decode-and-forward (DF) strategy. With nodes transmitting at different powers, the proposed algorithm adapts the resource allocation according to the differing link rates, and we prove theoretically that the optimization problem is log-concave. The proposed technique is shown to perform significantly better than the widely studied selection-cooperation technique. We then undertake an experimental study—on a software radio platform—of the decoding performance of PNC with unbalanced signal-to-noise ratios (SNRs) in the multiple-access transmissions. This problem is inherent in cellular networks, and it is shown that, with channel coding and decoders based on multiuser detection and successive interference cancellation, the performance is better with power imbalance. This paper paves the way for further research on multicell PNC, resource allocation, and the implementation of PNC with higher order modulations and advanced coding techniques. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the optimization of non-uniform linear antenna arrays (NULAs) for millimeter-wave (mmWave) line-of-sight (LoS) multiple-input multiple-output (MIMO) channels. Our focus is on the maximization of the system effective multiplexing gain (EMG), by optimizing the individual antenna positions in the transmit/receive NULAs. Here, the EMG is defined as the number of signal streams that are practically supported by the channel at a finite signal-to-noise ratio. We first derive analytical expressions for the asymptotic channel eigenvalues with arbitrarily deployed NULAs when, asymptotically, the end-to-end distance is sufficiently large compared with the aperture sizes of the transmit/receive NULAs. Based on the derived expressions, we prove that the asymptotically optimal NULA deployment that maximizes the achievable EMG should follow the groupwise Fekete-point distribution. Specifically, the antennas should be physically grouped into $K$ separate ULAs with the minimum feasible antenna spacing within each ULA, where $K$ is the target EMG to be achieved; in addition, the centers of these $K$ ULAs follow the Fekete-point distribution. We numerically verify the asymptotic optimality of such an NULA deployment and extend it to a groupwise projected arch-type NULA deployment, which provides a more practical option for mmWave LoS MIMO systems with realistic non-asymptotic configurations. Numerical examples are provided to demonstrate a significant capacity gain of the optimized NULAs over traditional ULAs. [ABSTRACT FROM PUBLISHER]