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In the wideband regime, the performance of many of the popular modulation schemes such as code division multiple access and orthogonal frequency division multiplexing falls quickly without channel state information. Obtaining the amount of channel information required for these techniques to work is costly and difficult, which suggests the need for schemes which can perform well without channel state information. In this work, we present one such scheme, called wideband time frequency coding, which achieves rates on the order of the additive white Gaussian noise capacity without requiring any channel state information. Wideband time frequency coding combines impulsive frequency shift keying with pulse position modulation, which allows for information to be encoded in both the transmitted frequency and the transmission time period. On the detection side, we propose a non-coherent decoder based on a square-law detector, akin to the optimal decoder for frequency shift keying based signals. The impacts of various parameters on the symbol error probability and capacity of wideband time frequency coding are investigated, and the results show that it is robust to shadowing and highly fading channels. When compared to other modulation schemes such as code division multiple access, orthogonal frequency division multiplexing, pulse position modulation, and impulsive frequency shift keying without channel state information, wideband time frequency coding achieves higher rates in the wideband regime, and performs comparably in smaller bandwidths.

Future Internet of Things (IoTs) networks will consist of a Hybrid Access Point (HAP) that powers devices via Radio Frequency (RF) signals. These devices harvest RF-energy and are tasked with monitoring target(s) such as vehicles at key locations. In order to maximize their expected targets monitoring time, this paper considers the problem of optimizing the HAP's transmit power, and the active time of devices. We formulate a Stochastic Program (SP) and solve it using the Sample Average Approximation method. We also present a sequential Monte-Carlo based reinforcement learning method or SMC-L to learn sensor activation time. Our results show that (i) the confidence interval upper bound of the solution derived by the SP is affected by the number of samples that represent targets appearance time, (ii) increasing the sensing range of devices affects the total expected target monitoring time, (iii) the learning speed of SMC-L is affected by its exploration rate, and (iv) SMC-L does not utilize the energy of devices fully under poor channel conditions.

© 1967-2012 IEEE. Relays have been deployed to provide connectivity to heavily blocked and isolated radio environments, but suffer from severe data rate losses due to the half-duplex nature of relays. This paper exploits non-orthogonal multiple access (NOMA) to enhance the sum-rate of two heavily blocked users to and from a base station (BS) via two-way relays (TWRs). By carefully arranging the NOMA transmissions, only two time slots are needed to complete a round of two-way transmissions between the BS and the two users. Unlike existing works where users associated with the same relay form a NOMA pair, the two users are connected to two different relays in the proposed scheme, due to the blockage. We analyze the sum-rate of the proposed NOMA-based TWR scheme with two popular relaying modes, namely, amplify-and-forward (AF) and decode-and-forward (DF) relaying, and derive closed-form expressions for the sum-rate upper bounds of the scheme. We also analyze the asymptotic sum-rate of the NOMA-based TWR schemes in high signal-to-noise ratio (SNR) regions. Simulations verify the analysis, and show that the proposed schemes outperform existing TDMA-based alternatives and a NOMA-based benchmark scheme. It is shown that the sum-rate of the schemes is independent of the transmit power of the BS in the high SNR regions, given the transmit powers of the relays and users.

In this paper, we investigate a hybrid multicast/unicast scheme for a multiple-input single-output cache-aided non-orthogonal multiple access (NOMA) vehicular scenario in the face of rapidly fluctuating vehicular wireless channels. Considering a more practical situation, imperfect channel state information is taking into account. In this paper, we formulate an optimization problem to maximize the unicast sum rate under the constraints of the peak power, the peak backhaul, the minimum unicast rate, and the maximum multicast outage probability. To solve the formulated non-convex problem, a lower bound relaxation method is proposed, which enables a division of the original problem into two convex sub-problems. Computer simulations show that the proposed caching-aided NOMA is superior to the orthogonal multiple access counterpart.

This paper studies the physical layer security for a cooperative non-orthogonal multiple access system with energy harvesting (EH) and full-duplex (FD) relaying. In particular, the base station transmits a superposed message to a dedicated EH-FD relay according to NOMA principle. Then, the relay forwards the decoded message to users. At the same time, the message transmitted by the relay can be wiretapped by an passive eavesdropper. The lower bound on the ergodic secrecy rates and the approximated secrecy outage probabilities of users are analyzed with closed-form solutions considering imperfect SIC. Finally, simulation results demonstrate the correctness of theoretical analysis, reflect the influence of the key parameters on the system performance and verify the better secrecy performance of the proposed scheme than its counterpart.

Vehicle to Vehicle (V2V) communication phase is an integral part of collaborative message dissemination in vehicular ad-hoc networks (VANETs). In this work, we apply index coding techniques to reduce the number of transmissions required for data exchange. The index coding problem has a sender, which tries to meet the demands of several receivers in a minimum number of transmissions. All these receivers have some prior knowledge of the messages, known as the side-information. In this work, we consider a particular case of the index coding problem, where multiple nodes want to share information among them. Under this set up, lower bound on the number of transmissions is established when the cardinality of side-information is the same. An optimal solution to achieve the bound in a special case of VANET scenario is presented. For this special case, we consider the link between the nodes to be error-prone, and in this setting, we construct optimal linear error correcting index codes.

Non-orthogonal multiple access (NOMA) aided unmanned aerial vehicle (UAV) is becoming a promising technique for future wireless networks. However, its security remains a great challenge due to the line-of-sight in UAV communications and high transmit power for weak users in NOMA. Thus, in this paper, we propose a power allocation (PA) scheme for NOMA-UAV networks with circular trajectory, to maximize the sum rate of common users while guaranteeing the security for a specific user. To achieve this, we consider three cases based on the distance from the UAV to the secure user. Specifically, the lowest transmit power is assigned to the secure user in each time slot to guarantee its security, with the remaining power allocated to common users to maximize their sum rate. Due to the non-convexity of the problem, we analyze its monotonicity and derive the closed-form solutions for these three cases. To further improve the transmission rate of the secure user, we also derive the upper bound for its decoding threshold, and analyze the linear relationship between the secure decoding threshold and the sum rate of common users. Simulation results are demonstrated to evaluate the effectiveness of the proposed secure PA scheme in NOMA-UAV networks.

In this paper, we study the secure transmission in multiple access wiretap channels when the instantaneous channel state information (CSI) of the wiretap channels is unknown by the legitimate users. We propose a two-phase jamming-assisted secure transmission scheme based on the randomness and independence of wireless channels. In this scheme, we appropriately design artificial noise in two transmission phases and make sure that the eavesdropper's channel is degraded by the artificial noise but the legitimate user's channel is free of the artificial noise. We analyze the capacity of the legitimate channel after using successive interference cancellation (SIC), and demonstrate that the eavesdropping capacity has an upper bound. Furthermore, we prove that a positive secrecy rate can be achieved and the achievable secrecy rate can be maximized by allocating appropriate power to the private information and the artificial noise.

Vehicular communications, with their promise to provide drivers and passengers with a wide range of applications, are attracting significant attention from both research and industry. In this paper, we study the performance of full duplex amplify and forward (AF) relaying-based vehicle-to-vehicle (V2V) cooperative wireless communications over Nakagami- $m$ fading channels. In such systems, in practical scenarios, the communication link inevitably suffers from co-channel interference, residual self-interference, and blockage from other vehicles on the road. In this context, we consider independent and not necessarily identically distributed (i.n.i.d) Nakagami- $m$ fading channels and derive novel exact and asymptotic outage probabilities of the exact equivalent and approximated signal-to-interference-plus-noise ratio (SINR), respectively. Building on this, the end-to-end exact and asymptotic outage probabilities are expressed in terms of the blockage probability and then used to evaluate the throughput of the proposed system. In addition, a lower bound to the symbol error rate of the considered system is also derived. Monte-Carlo simulation results are provided to demonstrate the accuracy of the proposed analytical expressions. The results demonstrate the significant impact of the considered interference and blockage on the system performance. Precisely, it is shown that the system performance is degraded when the average height of the obstacles is increased. This highlights the importance of taking into account these phenomena in the performance evaluation in order to assess the practical limit of V2V cooperative wireless communications.

5G wireless communication technologies aim at simultaneously achieving energy efficiency and spectral efficiency. 5G also demands high communication reliability. In this context, fine-grained temporal characterization of wireless channel can be used to enhance both. To this end, we propose a novel context-aware characterization of the temporally-varying wireless channel. Our characterization of temporal variation of the channel is based on the method of finite mixture of Gaussian distributions. However, unlike the classical Gaussian mixture model, the proposed characterization does not use an iterative algorithm for its parameter estimation; it depends on the current channel state and its statistics. Based on this characterization we estimate the quantity of data that can be transferred over the channel in a time interval without knowing the actual channel state in that duration. We propose an application context dependent upper bound on the time interval over which this estimation can be made. Our numerical results demonstrate that the present channel state plays a crucial role. When the proposed characterization is used in the context of channel adaptive communication, energy efficiency obtained is as high as 3.15 times over its nearest approach. A nontrivial trade-off between energy efficiency and precision of the proposed characterization is also investigated.

In this treatise, we conceive a hardware impairment aware receiver design of a large multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system, taking into account the error statistics of parameter estimations. The hardware impairment includes the quantization noise, phase noise etc. There are two key contributions in this work. Firstly, consideration of error statistics of residual parameters in the receiver design improves receiver performance. In this work, we propose a linear equalizer based data detector at the receiver for a large MIMO-OFDM system and the parameters of interest are the channel impulse response (CIR) and carrier frequency offset (CFO) along with the hardware impairments awareness both at transmitter and receiver. The design criterion chosen for designing the detector equalizer is based on the linear minimum mean square error (LMMSE) one and it will consider the statistics of estimation error and hardware impairments. However, in any practical communication system, the availability of statistics for these parameter errors is inconceivable. Hence, we propose further to consider the Bayesian Crammer Rao lower bound (BCRLB) as the variance of the error with the key assumption of zero mean. Numerical simulation demonstrates that, consideration of such statistics attains an SNR gain of $2$ dB with respect to a fixed symbol error ratio (SER). It also shows that the SER performance with BCRLB matches very closely with the case, when exact error variance is used. For the second contribution, an extensive theoretical analysis has been performed to evaluate the MSE improvement of data detection with the LMMSE criterion with parameter estimation error statistics consideration in the large MIMO-OFDM system. This theoretical analysis of improved MSE has been conceived with the usage of large dimension random matrix theory and a closed-form expression has been obtained. Numerical simulation demonstrates that the theoretical analysis for MSE improvement matches closely with the simulated one.

Single-input multiple-output non-orthogonal multiple access (SIMO-NOMA) is a promising technology for the uplink (UL) of the Internet of things (IoT). A rate splitting scheme to guarantee max-min fairness (MMF) of SIMO-NOMA is studied. First, the achievable data rate of the minimum mean square error successive interference cancellation (MMSE-SIC) detection is formulated. An upper bound for the minimum data rate of UL users is derived, and it is proved that the upper bound can be achieved by rate splitting with MMSE-SIC detection. Then, an exhaustive-search rate splitting algorithm to maximize the minimum data rate is designed. The existence of a lower bound for the minimum data rate with two-layer SIMO-NOMA is proved. A low-complexity two-layer rate splitting algorithm is designed with suitable detection order to exceed the lower bound. Numerical results verify that rate splitting SIMO-NOMA has higher minimum data rate and lower transmission latency than single-input multiple-output orthogonal multiple access (SIMO-OMA) and un-layered SIMO-NOMA.

Large intelligent surface (LIS)-assisted wireless communications have drawn attention worldwide. With the use of low-cost LIS on building walls, signals can be reflected by the LIS and sent out along desired directions by controlling its phases, thereby providing supplementary links for wireless communication systems. In this paper, we evaluate the performance of an LIS-assisted large-scale antenna system by formulating a tight upper bound of the ergodic spectral efficiency and investigate the effect of the phase shifts on the ergodic spectral efficiency in different propagation scenarios. In particular, we propose an optimal phase shift design based on the upper bound of the ergodic spectral efficiency and statistical channel state information. Furthermore, we derive the requirement on the quantization bits of the LIS to promise an acceptable spectral efficiency degradation. Numerical results show that using the proposed phase shift design can achieve the maximum ergodic spectral efficiency, and a 2-bit quantizer is sufficient to ensure spectral efficiency degradation of no more than 1 bit/s/Hz.

Caching at base station is a promising technology to reduce the backhaul loads in future wireless networks. To exploit the content diversity gain and multiple antenna transmission, in this paper, we consider the analysis and optimization of random caching in multi-antenna small-cell networks with limited backhaul. We analyze the performance of the success probability, which is defined as the probability that the requested file is not only obtained from the cache or the backhaul at the small base station (SBS), but also successfully delivered via the wireless link from the SBS to the user. Using tools from stochastic geometry, we first derive an exact expression and an upper bound of the success probability. Based on the analytical results, we then consider network throughput maximization by optimizing the design parameters in the cache-enabled multi-antenna small-cell networks. The optimization problem is a complicated mixed-integer optimization problem. After analyzing the optimal properties, we obtain a local optimal solution with low complexity. To further simplify the optimization, we solve an asymptotic optimization problem in the high user density region, using the upper bound as the objective function. Numerical simulations show that our proposed scheme improves the performance by up to $14\%$ when the Zipf parameter is 0.4 and the antenna number is 8.

We propose a transmission scheme that exploits the spatial degrees of freedom from large antenna arrays to maximize the downlink sum-rate of user equipment (UE) in a heterogeneous network. Our scheme incorporates the design of 1) a new precoder for the millimeter wave (mmWave) wireless backhaul; and 2) an enhanced small cell range expansion method for data link transmission. Aside from the advantage of lower implementation and computational complexity, the wireless backhaul precoder can simultaneously support multiple small access points with multi-streams. Under the constraint of wireless backhaul capacity, a downlink cooperation precoding algorithm is proposed to suppress the interference caused by small cell range expansion. Simulation results show that the proposed precoder in backhaul link can use a small number of radio frequency (RF) chains to achieve the sum-rate performance that approaches the one under traditional block diagonalization precoder, which requires the number of the RF chains to be comparable with that of antenna elements. Moreover, we give a lower bound for the overall UE sum-rate of the downlink cooperation precoding algorithm.

This paper proposes a new location-based multicasting technique, for dual phase amplify-and-forward (AF) large networks, aiming to improve the security in the presence of non-colluding passive eavesdroppers. These eavesdroppers could also be part of this cooperative network as relays. In order to reduce the impact of these eavesdroppers on the network security, we propose a new transmission strategy where, for the first hop of each transmission time, while the destination is jamming, the source randomly chooses a different subset $K$ of the total $T$ relays, to transmit its message toward the destination. For practical implementation, sectoral transmission can be achieved with analog beamforming at the source's side. In the second hop, using the distributed beamforming technique, the $K$ AF relays retransmit the received signal to the destination. We analytically demonstrated that the proposed technique decreases the probability of choosing the same sector that has certain eavesdroppers again, for each transmission time, to $K/T$ . Moreover, we also show that the secrecy capacity scaling of our technique is still the same as for broadcasting. Hereafter, the lower and upper bounds of the secrecy outage probability are calculated, and it is shown that the security performance is remarkably enhanced, compared to conventional multicasting technique.

This paper uses an information-theoretic lens to view the error bound of wireless local area network (WLAN) localization, which is recognized as one of the superior candidate localization techniques in the GPS-denied environment. Interestingly, we analogize the process of WLAN localization into one of information propagation in a parallel Gaussian noisy channel, and then derive the corresponding localization error bound from the channel capacity of the analogical information propagation system. Experimental results show that compared with the widely-known Cramer-Rao Lower Bound (CRLB), the proposed approach performs better in localization error bound estimation under most of the cases of different access point deployment and reference point calibration.

Relay assisted communication helps to extend the transmission coverage area. In more realistic scenarios, the available relays are untrusted, which try to decode the confidential signals during their main relaying function. Thus, these untrusted relays are regarded as potential eavesdroppers, and the confidential signals are vulnerable. A positive secrecy rate can be achieved at the physical layer without any cryptography, in particular, the mutual interference achieved from two-way relaying network can be used to secure the confidential signals against eavesdroppers attack. In this paper, the relay selection of two-way untrusted relays network is presented, and a directed beamforming is aligned to zero force the transmission at all other non-selected relays. Particularly, we propose a selection scheme based on max–min criterion, which requires an exhaustive search. Then, the lower bound (LB) of the secrecy rate is introduced to minimize the complexity of the selection criterion. The closed forms of the secure outage probability (SOP) for the max–min LB scheme is formulated and compared with other schemes. The simulations results validate the proposed analytical forms, and the performance of max–min LB scheme appears to be quite close to that of the max–min scheme with limited complexity.

This paper investigates unmanned aerial vehicle (UAV) enabled secure communication systems where a mobile UAV wishes to send confidential messages to multiple ground users. To improve the security performance, a cooperative UAV is additionally considered that transmits the jamming signal. In this system, we maximize the minimum secrecy rate among the ground users by jointly optimizing the trajectory and the transmit power of the UAVs as well as the user scheduling. To efficiently solve this nonconvex problem, we adopt block successive upper bound minimization techniques, which address a sequence of approximated convex problems for each block of variables. Numerical results verify that the proposed algorithm outperforms baseline methods.

In this paper, a two-hop nonregenerative multiple-input multiple-output relay system is investigated, where the relay node relies on harvesting the radio frequency energy transferred from the source node to forward information from source to destination. We consider the time switching (TS) protocol between wireless information and energy transfer. In particular, we propose a more general energy consumption constraint at the source node during the information and energy transfer, which includes the constant power constraints used in existing works as special cases. We study the joint optimization of the source precoding matrices, the relay amplifying matrix, and the TS factor to maximize the source–destination mutual information (MI). The optimal structure of the source and relay matrices is derived, which reduces the original transceiver optimization problem to a simpler power allocation problem. We propose a primal decomposition based algorithm and an upper bound based approach to efficiently solve the power allocation problem. The first algorithm achieves the global optimum, whereas the latter one has a lower computational complexity. Numerical simulations show that both proposed algorithms yield much higher system MI and better rate-energy tradeoff than existing approaches.

Beamforming with antenna arrays has been considered as an enabling technology in future wireless communication systems. To conduct beamforming, one has to know the angle-of-departure (AoD) or angle-of-arrival (AoA). For data detection, the receiver also has to know channel response. In this paper, we propose a new joint AoD, AoA, and channel estimation scheme for pilot-assisted multiple-input-multiple-output–orthogonal-frequency-division-multiplexing (MIMO-OFDM) systems. First, a compressive-sensing technique is employed to estimate the channel impulse response, exploiting the sparsity property of wireless channels. Then, AoA and AoD are jointly estimated for each detected path by the maximum likelihood method. The Cramer–Rao lower bound (CRLB) is also derived and a transmit beamforming scheme is proposed accordingly. In the scenario of available prior information, a maximum a posteriori estimation is proposed. The Bayesian CRLB (BCRLB) for the problem is also derived and a transmit beamforming scheme is further proposed. It turns out that only two training OFDM symbols are required for the estimation. Simulation results show that the proposed methods can approach the CRLB/BCRLB in both scenarios and achieve the same spectral efficiency as that obtained with the ideal channel in millimeter-wave communications.

This paper considers the artificial noise (AN) scheme, proposed by Goel and Negi for securing wireless communications over multiple-input multiple-output multiantenna eavesdropper (MIMOME) channels. In this paper, we develop a lower bound on the ergodic secrecy rate of the AN scheme and derive an analytic expression of the bound without assuming any asymptotes for system parameters. Thus, the derived bound explicitly elucidates how the system parameters exert influence on the secrecy rate in various cases to some of which the previous work cannot be applied. It will also be shown that the bound is tight over wide combinations of system parameters, and a normalized difference between the bound and secrecy rate vanishes with a growing number of antennas at the transmitter. The derived bound is analytically so amenable that it enables one to not only extend the results of the previous work but also explore untouched aspects of the AN scheme. The bound answers an intriguing question: When is the AN signal beneficial? Besides, it makes possible to study a new important system parameter called the minimum required transmit power for a certain target secrecy rate, which has never been touched before.

In this study, we develop a physical-layer network coding based throughput-optimal transmission scheduling policy, which we call network coding aware maximum differential backlog (NCMDB), and demonstrate its throughput optimality. As the proposed policy requires an NP-hard network-wide centralized algorithm to determine the optimal transmission schedule, we propose a distributed NCMDB based on link prioritization (D-NCMDB) as a lower bound approach. We further propose a bidirectionality-aware route establishment scheme for the best application of the NCMDB and D-NCMDB policies. Simulations confirm that compared to the signal-to-interference-based interference avoidance transmission approach and random maximal physical-layer network coding approach, the proposed D-NCMDB scheme offers $48\%$ and $27\%$ increased throughput, $78\%$ and $63\%$ reduced access delay, and $80\%$ and $20\%$ enlarged stability, respectively. The proposed distributed transmission scheduling scheme can be employed in future communication networks in which both the number of nodes and the amount of interference increase exponentially.

Due to robustness against multipath effect, frequency domain CSI (Channel State Information) of OFDM (Orthogonal Frequency Division Multiplexing) systems is supposed to provide good ranging measurement for indoor localization. Since lower bound of positioning error for CSI-based localization has been rarely reported, this paper proposes a Cramer–Rao lower bound (CRLB) for CSI-based localization. This CRLB is derived based on indoor wireless propagation model integrating both shadowing effect and multipath effect. The main factors that can influence the CRLB are analyzed. Through simulations and analysis, the gap between CSI-based localization error and the proposed CRLB is presented, and the influence by three main factors is thoroughly investigated.

Channel matrix sparsification is considered as a promising approach to reduce the progressing complexity in large-scale cloud radio access networks based on ideal channel condition assumption. In this paper, the research of channel sparsification is extend to practical scenarios in which the perfect channel state information (CSI) is not available. First, a tractable lower bound of signal-to-interference-plus-noise ratio (SINR) fidelity, which is defined as a ratio of SINRs with and without channel sparsification, is derived to evaluate the impact of channel estimation error. Based on the theoretical results, a Dinkelbach-based algorithm is proposed to achieve the global optimal performance of channel matrix sparsification based on the criterion of distance. Finally, all these results are extended to a more challenging scenario with pilot contamination. Finally, simulation results are shown to evaluate the performance of channel matrix sparsification with imperfect CSIs and verify our analytical results.

In wireless communications, massive multiple-input multiple-output (MIMO) systems in which the base station (BS) is equipped with a large number of antennas can provide significant spectral and energy efficiency by means of simple signal processing. Therefore, massive MIMO is an attractive technology for next-generation wireless communication systems and for green communications. However, phase noise (PN) introduced by the impairment of oscillators can cause a severe performance loss in wireless communication systems. Solving the PN problem for massive MIMO systems in both the downlink and uplink is challenging as it is a multivariate joint PN estimation and data detection problem. The optimal solution is difficult to derive and may lead to high complexity. In this paper, we propose a zero-forcing (ZF) based precoding scheme with PN suppression to solve the PN problem for massive MIMO downlink systems. By using the ideal PN free output signal-to-noise ratio (SNR) of the ZF precoding as a performance upper bound, analytical and numerical results show that when the required SNR is satisfied, the output SNR of the proposed scheme can approach the upper bound, except for very large PN variances. Moreover, the complexity of the proposed scheme is low on both the BS and the user sides, and the BS does not need any feedback information from the users.

In this paper, we investigate the individual channel estimation over the three-node one-way relay network, where all the nodes are equipped with multiple antennas. We first examine one simple but representative scenario, where the relay node is equipped with a single antenna. An iterative linear minimum mean-square-error (LMMSE) method, which has fast convergence speed, is proposed to estimate the individual channels. We derive the closed-form least square channel estimator through the matrix unitary diagonalization to provide one good initialization point for the iterative LMMSE estimator. To evaluate the performance of the proposed algorithm, we present two performance lower bounds: Bayesian Cramer lower bound and linear estimation lower bound (LELB). Then, the training block and the relay amplification factor are optimized through minimizing the LELB. After that, our studies are extended to the general case, where all the nodes are equipped with multiple antennas. Finally, numerical results are provided to corroborate our proposed studies.

The nested array, implemented by nonuniform antenna placement, is an effective approach to achieve $O(N^2)$ degrees of freedom (DOF) with an antenna array of $N$ antennas. Such DOF refers to the number of directions of incoming signals that can be resolved. With the increased number of DOF, an important application of nested array is to nullify interference signals from multiple directions. In this paper, we apply nested array in a massive multiple input multiple output (MIMO) heterogeneous network (HetNet) for interference management. With a nested array, a base station (BS) can nullify a certain number of interference signals based on their directions. Then, a key design issue is how to select the set of interference sources to be nullified at each BS. As the DOF of each BS is used to resolve both desired signals and interference, the number of interference signals that can be nullified depends on the number of users served by the BS. Thus, user association is another factor that impacts the system performance and should be jointly considered with interference nulling. We formulate the joint interference nulling scheduling and user association problem as an integer programming problem, aiming to maximize the sum rate of all users subject to BS DOF constraints. We first investigate the case of interference nulling with a given user association, and propose a scheme to solve a relaxed problem as well as derive a performance upper bound. Then, we propose a distributed joint interference nulling and user association scheme based on a poly matching between users and BSs. Simulation results show that the proposed schemes effectively improve the sum rate and achieves a near optimal performance.

This paper provides a comprehensive scaling law-based performance analysis for multicell multiuser massive multiple-input-multiple-output (MIMO) downlink systems. Imperfect channel state information (CSI), pilot contamination, and channel spatial correlation are all considered. First, a sum-rate lower bound is derived by exploiting the asymptotically deterministic property of the received signal power, while keeping the random nature of other components in the signal-to-interference-plus-noise-ratio (SINR) intact. Via a general scaling model on important network parameters, including the number of users, the channel training energy and the data transmission power, with respect to the number of base station antennas, the asymptotic scaling law of the effective SINR is obtained, which reveals quantitatively the tradeoff of the network parameters. More importantly, pilot contamination and pilot contamination elimination (PCE) are considered in the analytical framework. In addition, the applicability of the derived asymptotic scaling law in practical systems with large but finite antenna numbers are discussed. Finally, sufficient conditions on the parameter scalings for the SINR to be asymptotically deterministic in the sense of mean square convergence are provided, which covers existing results on such analysis as special cases and shows the effect of PCE explicitly.

As the conventional spatial modulation (SM) system lacks an efficient downlink multiple access scheme, this paper proposes a novel downlink nonorthogonal multiple access aided multiple-input multiple-output SM system with finite alphabet inputs. Mutual information (MI) is used to characterize the system's achievable spectral efficiency (SE). Because the MI lacks a closed-form formulation, in this paper we propose a computational-efficient lower bound to provide an approximation to the simulated MI. Besides, preprocessing is also designed at the receiver to mitigate the detrimental effect of channel correlation, which leads to striking a higher SE performance. Simulation results not only confirm the accuracy of our SE analysis, but also substantiate the superior SE performance achieved by the proposed preprocessing.

While device-to-device (D2D) communication between two proximate cellular terminals improves spectral efficiency, cooperative spectrum sharing (CSS) among D2D users (DUs) and cellular users (CUs) provides more gains. In this paper, we thus propose a new CSS scheme, where a pair of DUs (D $_1$ and D $_2$ ) acts as half-duplex two-path successive relays (TPSRs) between two CUs (C $_1$ and C $_2$ ) while maintaining cellular full-duplex gains. The channel between D $_1$ and D $_2$ , referred to as TPSR interrelay interference channel, is exploited to provide D2D access with an ideal throughput of 1 symbol per time slot (sym/TS). To eliminate the interference between C $_1$ –C $_2$ and D $_1$ –D $_2$ links, we propose a hybrid complex field network coding (HCFNC) scheme for two cases: First, both of D $_1$ and D $_2$ correctly decode cellular data; and second, either D $_1$ or D $_2$ correctly decodes cellular data. For each case, we derive the closed-form CU diversity-multiplexing tradeoff (DMT) and data rate, symbol error probability for the HCFNC strategy, and the D2D throughput. Our performance analysis and simulation results show that: first, the proposed scheme achieves the 3 $\times$ 1 multiple-input single-output DMT; second, the CU achievable rate approaches the full-duplex upper bound; and third, the DU average throughput approaches 1 sym/TS at high signal-to-interference-plus-noise ratio, demonstrating that DUs gain full access opportunity to the cellular spectrum.

In this paper, we study a space-shift keying (SSK) based dual-hop relaying system. We assume that the relay node (RN) possesses an information buffer and propose an adaptive transmission scheme in which the system adaptively chooses the source–relay or the relay–destination links to transmit information in each time slot. We obtain an exact closed-form expression of the average bit-error probability for the system in which the source node has two transmit antennas. For the system with arbitrary number of transmit antennas, an upper bound is provided, and it is shown that the proposed scheme can achieve a diversity order of two by using the asymptotic analysis. The delay of the information packets at the RN is investigated, and two link adaptation schemes are proposed to reduce the average packet delay. Numerical results validate our analysis and show that the proposed scheme can significantly improve the system performance, as compared to the conventional relaying scheme.

In this paper, a video framework with stochastic quality of service (QoS) is proposed for cognitive heterogeneous networks based on internetwork cooperation. The video packet scheduling is subject to constraints in the available energy at each call for secondary mobile terminal (MT), the time varying channel state information (CSI) at different interfaces, the total interference power, the target call duration, and the video characteristics. The objective function maximizes the minimum lower bound of video quality. In order to solve the above video packet scheduling problem with stochastic QoS guarantee, a video packet scheduling scheme based on forward-auction theory is proposed. Then, the cumulative distribution function (CDF) for video quality is analyzed. Finally, the power allocation scheme to maximize the minimization lower bound of video quality among different secondary MTs is presented. Simulation results demonstrate that the proposed video packet scheduling scheme with stochastic QoS requirement improves the video quality for secondary MT significantly.

In interference channels, channel state information (CSI) can be exploited to reduce the interference signal dimensions, and thus, achieve the optimal capacity scaling, i.e., degrees of freedom, promised by the interference alignment technique. However, imperfect CSI, due to channel estimation error, imperfect CSI feedback and time selectivity of the channel, lead to a performance loss. In this paper, we propose a novel limited feedback algorithm for single-input single-output interference alignment in time-variant channels. The feedback algorithm encodes the channel evolution in a small number of subspace coefficients, which allow for reduced-rank channel prediction to compensate for the channel estimation error due to time selectivity of the fading process and feedback delay. An upper bound for the rate loss caused by feedback quantization and channel prediction is derived. Based on this bound, we develop a dimension switching algorithm for the reduced-rank predictor to find the best tradeoff between quantization and prediction error. Besides, we characterize the scaling of the required number of feedback bits in order to decouple the rate loss due to channel quantization from the transmit power. Simulation results show that a rate gain over the traditional nonpredictive feedback strategy can be secured and a $60\%$ higher rate is achieved at 20-dB signal-to-noise ratio with moderatesxst mobility.

We consider a dual-hop amplify-and-forward (AF) relay-aided multicarrier system between a source and destination, in which there is an external eavesdropper which taps the signals in both hops. We consider individual power constraints at source and relay. The relay pairs the carriers in the second hop and enhances the system sum rate through multicarrier diversity. The sum rate maximization problem belongs to the class of mixed-integer nonlinear program (MINLP) due to the variables corresponding to subcarrier (SC) pairing being binary and its joint optimization with source and relay powers. We propose a solution based on alternate maximization and show that it converges to a local maximum. We also derive a computationally less complex and optimally computable solution based on transforming the problem to a single power variable which, ultimately, is a lower bound to sum rate. We analyze both the solutions when either the source power or relay power, or both, are very high. In the process, we also derive asymptotically achievable upper bound to the sum rate. We also show that either full relay power or full source power, or both, are used at the optimal point. Our numerical results demonstrate an improved sum rate over a scenario of no pairing at relay.

Cooperative communication assisted by relays is regarded as an effective means of achieving spatial diversity. The achievable diversity gain relies on the channel-state information (CSI) that can be acquired at the destination because spatial diversity combining unavoidably requires the disintegrated channel information. To reduce signaling overhead, quantization and compression are applied prior to the delivery of the source-to-relay (SR) CSI to the destination. This paper studies an amplify-and-forward relay network while considering the impact of the quantization of the SR CSI. Several quantization methods relying on the statistics of the SR CSI are investigated. The probability density functions of upper and lower bounds on the signal-to-noise ratio (SNR) and the corresponding symbol-error rate (SER) achieved at the destination are derived analytically. Monte Carlo simulations are performed to compare the simulation results with the analytical results of the SER bounds and to verify the effectiveness of the proposed channel quantizer. A combined method of determining the quantization interval based on equal-probability partitioning and determining the representative quanta based on the minimum-mean-square-error criterion is proposed and is verified to have the lowest quantization error among the noniterative quantization methods compared here. It is shown via the simulations that when using an iterative procedure based on the Lloyd–Max procedure, three bits per dimension are sufficient to achieve negligible SNR degradation.

In wireless communications, massive multiple-input multiple-output (MIMO) uplink systems in which the base station is equipped with a large number of antennas can provide significant spectral and energy efficiency by means of simple signal processing. Therefore, massive MIMO uplink systems are an attractive technology for next-generation wireless systems and for green communications. However, phase noise (PN) introduced by the impairment of oscillators can cause a severe performance loss in wireless communication systems. Solving the PN problem for massive MIMO uplink systems is challenging as it is a multivariate joint PN estimation and data detection problem. The optimal solution is difficult to derive and may lead to high complexity. In this paper, we propose a low-complexity PN suppression scheme based on the zero-forcing detector to solve this problem. By using the ideal PN free output signal-to-noise ratio (SNR) as a performance upper bound, simulation results show that when the required SNR is satisfied, the output SNR of the proposed scheme can approach the upper bound, except for very large PN variances. Moreover, the complexity of the proposed scheme is low because the proposed scheme can update the current PN estimation and data detection by the previous ones via a simple iterative process.

This paper investigates the impact of primary transmissions on the performance of a dual-hop bidirectional cognitive radio system. The secondary users (SUs) communicate with each other in an underlay mode with the assistance of amplify-and-forward (AF) relays in the presence of primary users (PUs). Depending on whether there exists interference at the secondary transceivers, originating from the primary transmissions, or there is a possibility of employing the best relay selection technique at the secondary side, two operating cases are considered for the secondary network (SN). For both cases, tight lower bounds on the outage probability (OP) and the average symbol error probability (SEP) are derived in closed form. Moreover, a novel and general closed-form expression for the bit error rate (BER) of $M$ -ary square quadrature amplitude modulation (QAM) for both single- and multiple-relay systems, under additive white Gaussian noise (AWGN) when Gray coded bit mapping is employed, is presented. In addition, we provide an upper bound and closed-form approximate expression for the ergodic capacity (EC). Numerical results provide important insights into the impact of system parameters on performance; for instance, it is demonstrated that, in both cases, under consideration of power constraints on the secondary nodes as dictated by the underlay mode of operation, the SN always achieves the full diversity order similar to the noncognitive counterpart.

To address the asynchronous interference issue in a generalized scenario with multiple primary and multiple secondary receivers, in this paper, we propose an innovative cooperative beamforming technique. The cooperative beamforming design is formulated as an optimization problem that maximizes the weighted sum transmission rate of secondary destinations while maintaining the asynchronous interference at the primary receivers below their target thresholds. In particular, we obtain the optimum beamforming directions and allocate power values among such beamforming directions. In light of the intractability of the power allocation problem, an approximation is used to convert the nonconvex and nonlinear optimization problem into a convex and linear one. Due to the multiple interference constraints corresponding to multiple primary receivers, the power allocation scheme is still complex. Therefore, we also propose a low-complexity power allocation algorithm. The proposed beamforming technique is extended for the case when cooperating CR nodes (CCRNs) have statistical channel knowledge of the primary receivers by developing an upper bound on the probability of introducing asynchronous interference at a given primary receiver beyond a given threshold value. We also propose two CCRN selection strategies for cooperative beamforming.

We propose an extended recursive Cramer–Rao lower bound (ER-CRLB) method as a fundamental tool to analyze the performance of wireless indoor localization systems. The ER-CRLB fully models the complicated indoor environment, e.g., the sequential position state propagation, the target-anchor geometry effect, the non-line-of-sight (NLOS) identification, and the related prior information. First, we use an abstract function to represent the entire wireless localization system model. Then, the unknown vector of the ER-CRLB consists of two parts: The first part is the estimated vector, and the second part is the auxiliary vector that helps improve the estimation accuracy. Accordingly, the Fisher information matrix (FIM) of the ER-CRLB is divided into two parts, namely, the state matrix and the auxiliary matrix. Based on this idea, ER-CRLB can be a practical fundamental limit to denote the system which fuses multiple information in the complicated environment, e.g., recursive Bayesian estimation based on the hidden Markov model, the map matching method and NLOS identification and mitigation methods. When only a small set of unknown vectors is estimated in the system, the ER-CRLB is equivalent to the other CRLBs of the wireless indoor localization system as well. However, the ER-CRLB is more adaptable than other CRLBs when considering more unknown important factors. We employ the ER-CRLB to analyze the time-of-arrival (TOA) range-based indoor localization system. The influence of the hybrid line-of-sight (LOS)/NLOS channels, the building layout information, and the relative height differences between the target and anchors are analyzed. It is demonstrated that the ER-CRLB exploits all the available information for the indoor localization systems and serves as a fundamental limit of the unbiased estimation accuracy.

In this paper, we consider a general centralized feedback model for interference alignment (IA) algorithms of which precoders and decoders are designed with quantized channel state information (CSI) at a central unit and then fed back to corresponding transmitters and receivers via finite-rate links. The significant difference of this model from existing models is that all the realizations of channels, precoders, and decoders are fed back by finite rates. The signal-to-interference-plus-noise ratio (SINR) characteristic of this model is analyzed, which helps derive an upper bound of average throughput loss relative to perfect IA. The upper bound is shown to be tight by simulation, particularly in the high-feedback regime. Based on this result, the scaling laws of feedback bits for channels, precoding, and decoding vectors to obtain full degree of freedom are derived, which are backward compatible to the existing results for quantized CSI only or quantized precoder only. Simulation results show that the system is interference limited at a high signal-to-noise ratio (SNR) if the three scaling laws are not simultaneously satisfied, and the throughput performance is dominantly limited by the worst feedback links among the channels, precoders, and decoders.

In this paper, we investigate power-allocation and relay-selection algorithms for amplify-and-forward (AF) cooperative relay systems under imperfect channel estimation. Based on maximizing a tight capacity lower bound, a closed-form formula for optimal power allocation under a given relay is first derived and then used to establish an optimal relay-selection algorithm. By exploring the asymptotic behavior of the capacity lower bound, a suboptimal but reduced-complexity relay-selection algorithm is further presented. Simulation results demonstrate that significant capacity gains can be achieved by using the proposed optimal and suboptimal algorithms.

This paper presents a simple and effective solution for the peak-to-average power ratio (PAPR) problem in orthogonal frequency-division multiplexing (OFDM) systems. Since the OFDM signal has a high PAPR, it generates inevitable nonlinear distortions at the output of a high-power amplifier. A new technique of PAPR reduction called phase realignment (PR) and its modified version called modified PR (MPR) are proposed, which require neither side information nor do they impose any modification of the receiver. Furthermore, four possible variations of the MPR constellation structure are proposed and evaluated in terms of PAPR reduction and bit-error-rate (BER) performance. The proposed PR and MPR provide considerable reductions in PAPR. MPR1 gives a comparatively high PAPR reduction, followed by MPR3, PR, MPR2, and MPR4 in descending order. The PAPR reductions of MPR3 and MPR2 tend to converge to that of the PR as the modulation order increases, whereas MPR4 gives a low PAPR reduction. These techniques are designed to allow a certain level of BER degradation, which is defined by a transmit error vector magnitude (EVM) limited to a specified threshold, to reduce the PAPR and the average power (equally important in terms of power efficiency). The proposed techniques produce the reduced out-of-band radiation compared with the original OFDM signal and have moderate computational complexity. The EVM threshold is imposed to design optimum amplitude and phase margins for the PR and MPRs. Furthermore, we present the analytic upper bound BER expressions of the proposed techniques.

The performance of throughput, delay, and their tradeoff in mobile ad hoc networks (MANETs) has been investigated on different assumptions. Nevertheless, few papers consider the supportive infrastructure in MANETs. With the help of supportive infrastructure, i.e., cellular networks, the performance of throughput and delay in MANETs can be improved. The impact of supportive infrastructure on the throughput–delay tradeoff in MANETs is an open research point. In this paper, we are interested in the optimal throughput–delay tradeoff in MANETs with supportive infrastructure. We investigate the supportive infrastructure that only provides transmission pipes between distant nodes. We study the optimal throughput–delay tradeoff in MANETs on different assumptions such as different densities of nodes and base stations (BSs), discrete/continuous mobility models with various velocities, and the fast/slow mobility assumption. We obtain the asymptotically optimal throughput–delay tradeoff and propose transmission policies based on random linear coding (RLC) to achieve the optimal throughput–delay tradeoff asymptotically. From the obtained optimal throughput–delay tradeoff, we find that supportive infrastructure reduces the delay bound from velocity, which is a lower bound of delay caused by nodes' velocity. In particular, if supportive infrastructure can cover the entire network, then the delay bound from velocity vanishes when per-node throughput does not exceed a threshold. Moreover, we find that there also exists a delay bound from the transmission range of the instant transmission. In addition, we observe that, for the optimal throughput–delay tradeoff in the large throughput region, the case in the continuous mobility model outperforms the case in the discrete mobility model, and the case on the slow mobility assumption outperforms the case on the fast mobility assumption.

This paper presents a thorough performance analysis of dual-hop cognitive amplify-and-forward (AF) relaying networks under spectrum-sharing mechanism over independent nonidentically distributed (i.n.i.d.) $\eta-\mu$ fading channels. In order to guarantee the quality of service (QoS) of primary networks, both the maximum tolerable peak interference power $\mathcal{Q}$ at the primary users (PUs) and the maximum allowable transmit power $\mathcal{P}$ at the secondary users (SUs) are considered to constrain transmit power at the cognitive transmitters. For integer-valued fading parameters, a closed-form lower bound for the outage probability (OP) of the considered networks is obtained. Moreover, assuming arbitrary-valued fading parameters, the lower bound in integral form for the OP is derived. In order to obtain further insights into the OP performance, asymptotic expressions for the OP at high SNRs are derived, from which the diversity/coding gains and the diversity–multiplexing gain tradeoff (DMT) of the secondary network can be readily deduced. It is shown that the diversity gain and the DMT are solely determined by the fading parameters of the secondary network, whereas the primary network only affects the coding gain. The derived results include several others available in previously published studies as special cases, such as those for Nakagami- $m$ fading channels. In addition, performance evaluation results have been obtained by Monte Carlo computer simulations, which have verified the accuracy of the theoretical analysis.

In this paper, we study completely irrepressible (CI) sequences. For a slot-asynchronous communication system supporting $K$ users with such sequences, a key feature is that each user is guaranteed to be able to send out at least one contention-free packet in one common sequence period. This is a desirable property since it provides a bounded delay guarantee for medium access control (MAC) layer contention, in contrast to random access schemes. Generalizing previous studies on CI sequences, we investigate systems endowed with multiple-packet reception (MPR) capability $\gamma$ , $\mbox{2} \leq \gamma . That is, a packet transmission is successful if and only if the total number of transmissions in the channel at any point in time during its transmission is less than or equal to $\gamma$ . We investigate the minimum period $L$ of CI sequences for MPR as $L$ is a fundamental factor that affects the worst-case delay. The main result is that $L$ is asymptotically upper bounded by $\mbox{2}K^2/(\gamma-\mbox{2})$ when $\gamma \geq \mbox{3}$ . For $\gamma = \mbox{2}$ , the corresponding bound is $\mbox{2}K^2$ . In contrast, the bound for the single-packet reception system $(\gamma=\mbox{1})$ is $\mbox{4}K^2$ . Simulation results verify our analysis and present comparative studies between CI sequences and random access in an application of group-based detection in a wireless sensor network.

Previous works on multicast capacity mainly focus on deriving asymptotic order results in large-scale wireless networks. While they can explore the general scaling laws of throughput capacity, it is also of great interest for practical concern to predict the exact achievable throughput in networks with an arbitrary finite number of nodes. In this paper, we investigate the nonasymptotic throughput and delay of multihop wireless networks for multicast applications wherein for each source node, $k$ nodes are randomly selected as receivers. It is challenging for the exact performance analysis since multicast transmission has a dynamic nature due to the following factors: 1) random distribution of nodes; 2) bursty traffic arrivals; and 3) different timescales for transient analysis. To tackle the problem, we propose an explicit analytical model and develop a multicast routing scheme, which accounts for these aspects. With our proposed model, we derive lower bound (LB) and upper bound (UB) on nonasymptotic multicast throughput and delay using stochastic network calculus. We show that the performance results hold for all timescales and network sizes and are strongly correlated to data burstiness and the number of receivers. While we investigate from a nonasymptotic point of view, our results can also cover the asymptotic scaling laws. Simulations are conducted to further verify the accuracy of the analytical bounds.

In this paper, we establish an analytical framework to derive a closed form for a close approximation of the optimal angle of transform in fractional-Fourier-transform orthogonal-frequency-division-multiplexing (FrFT-OFDM) systems. In this framework, we derive a tight upper bound for the intercarrier interference (ICI) power and use this bound instead of the exact ICI power to obtain the near-optimal angle of transform. We also analyze the sensitivity of the FrFT-OFDM systems to the angle of transform. In other words, we derive the increase in the ICI power when we exploit a nonoptimal angle of transform due to channel estimation errors. We show that once the error in the channel state information (CSI) is reasonable, the ICI power increase is tolerable. For instance, for a two-path channel, if the error in estimating the slope of the channel spread function in the delay–Doppler plane is less than 20%, the ICI power increase is smaller than 0.1 dB.

Despite increasing interest in distributed antenna systems (DASs), how to guarantee resource-based fairness in multiuser DASs remains largely unexplored. In this paper, we propose a novel cumulative distribution function (CDF)-based scheduling (CS) with flexible beam transmissions (CSFB) to guarantee resource-based fairness in DASs. With CSFB, to increase the probability that each remote antenna unit (RAU) can contribute to the throughput of users located near it, the probabilities of each user being selected for different RAUs and the number of beams for transmission are jointly adapted to the geographical distribution of RAUs. Thus, CSFB can efficiently exploit multiuser diversity realized by independent fading channels, as well as spatial multiplexing by effectively utilizing the distributed RAUs. To realize CSFB in practice, a one-bit-feedback scheme, i.e., CSFB-OB, is further proposed to reduce feedback overhead for user selection. We prove that both CSFB and CSFB-OB can satisfy resource-based fairness and derive the upper bound for the feedback overhead with CSFB-OB. Simulation results show that CSFB and CSFB-OB with a suitable feedback threshold achieve a better throughput performance than CS with all-beam and single-beam transmissions and the existing fair scheduling schemes.

Dense wireless local area networks (WLANs) have emerged as a promising design paradigm recently. While partially overlapped channels (POCs) have proved to be able to improve network capacity significantly in the dense network, they are usually considered the fixed number of access points (APs) in traditional research works. The network capacity can be scaled by providing additional APs in a given area since the expected distance from users to the associated APs becomes shorter. On the other hand, the additional increment of network capacity by means of deploying more APs is limited. It can be accredited to the substantial interference among the high number of deployed APs assigned with POCs. Furthermore, the impact of the POC model makes the design more complex. To cope with these challenges in this work, we study the problem of interaction between density of APs and POC assignment with parameter tuning and propose the approach called DAPA, in which we derive that the optimal density of APs lies in the feasible region consisting of the lower bound and upper bound density. Thus, the solution can be obtained by searching for the feasible region by means of the proposed POC assignment. Through analysis and numerical results in DAPA, we provide recommendations on the optimal dimension of high density and POC assignment, with consideration of the network configuration.