Physical layer signal processing algorithms in the wireless domain are seeing increased use of machine learning algorithms, especially Bayesian methods. This work presents the hardware architecture and implementation of a vector processor for one such application, Bayesian channel estimation (CE) (BCE). The BCE vector processor supports a generic instruction set with a supplement of specialized instructions to realize Bayesian algorithms in the signal processing context. The vector processor is designed to work as an accelerator in a system-on-chip (SoC) with an AHB/AXI bus interface or as stand-alone unit. The vector processor achieves more than$4\times $improvement in performance when compared with a traditional CE algorithm running on a commercial vector processor. To the best of authors knowledge, this is a first known hardware implementation of a variational Bayesian inference algorithm for a wireless communication application.

In this paper, we study path selection and power allocation problems for relay-aided systems with multiple source, relay, and destination nodes. To take fairness among different links into account, we aim at maximizing the minimum source-relay-destination link rate performance. We consider the scenario that the source node can be paired with any destination node. For this scenario, we decouple the original path selection and power allocation problem as two graph-based matching problems and develop algorithms to solve the formulated matching problems with and without sum power constraint, respectively. Numerical results show that our proposed algorithms outperform random matching algorithms in all scenarios and the corresponding performance gains increase with the number of nodes.

In this paper, we study cooperative precoder design in two-tier networks, consisting of a macro-cell (MC) and several small-cells (SCs). By exploiting multiuser Vandermonde-subspace frequency division multiplexing (VFDM) transmission, an MC downlink can co-exist with cognitive SCs. In this paper, we first propose a cooperative cross-tier precoder (CTP) among the transmitters in the SCs to increase the transmitted dimension. The cooperative CTP allows us to use more efficient intra-tier precoder (ITP) in SCs to handle intracell interference and improve the throughput of the cognitive system. And then, three ITPs, a block-diagonal zero-forcing (BD-ZF) ITP, a capacity-achieving (CA) ITP, and a generalized MMSE channel inversion (GMI) ITP, are developed. Complexities of all CTPs and ITPs are discussed and compared. The overhead of channel state information (CSI) exchange is analyzed. Numerical results are presented to demonstrate the throughput improvement of the proposed schemes and to discover the impact of the imperfect CSI. From the complexity comparison and the numerical results, the GMI ITP offers a good tradeoff between complexity and throughput.

Massive multiple-input multiple-output (MIMO) systems have attracted extensive attention recently due to their potentials to provide high system capacity. To obtain the benefits of massive MIMO systems, channel state information (CSI) at the transmitter is essential. The high overhead of traditional channel estimation and feedback scheme for downlink massive MIMO systems makes frequency division duplexing (FDD) impractical. Compressive sensing (CS) is a potential way to alleviate the problem. In this paper, we mainly focus on the CS- based feedback design. To guarantee the performance of the CS-based algorithms, a proper basis to reveal the sparsity of the channel is important. Here, we use the statistical information of the angle-of-departure (AoD) of physical channel paths for the basis design. A l0-norm based basis optimization problem is first formulated. Then, the problem is relaxed by a weighted l1-norm and is solved by an iterative algorithm. The mean-square-error (MSE) performance of the CS-based feedback scheme based on our proposed basis is better than the traditional discrete Fourier transform (DFT) basis.

Energy-efficient orthogonal frequency division multiple access (OFDMA) aims to relieve the booming energy consumption in wireless communications networks and has recently received lots of attention. Meanwhile, two-way relay has been extensively studied and proves to be more spectral-efficient than direct transmission and one-way relay in many scenarios. In this paper, we study energy-efficient resource allocation for OFDMA-based two-way relay, maximizing the aggregated energy efficiency (EE) utility while provisioning proportional fairness in EE among different terminal pairs. The energy-efficient joint power and subchannel allocation and active subchannel selection problem is mixed-integer combinatorial and further demonstrates to be nonconvex. To approach the performance limit, we first find an upper-bound solution relying on continuous relaxation and the Lagrange dual method. To reduce the computational complexity, we separate power allocation and subchannel assignment. To this regard, we exploit the hidden concavity and the pseudoconcavity in the subproblems for any fixed subchannel assignment and propose an EE-oriented sequential subchannel assignment policy. Besides, we discover the sufficient condition for early termination of the sequential subchannel assignment without losing the EE optimality. Simulation results demonstrate that the proposed energy-efficient OFDMA-based two-way relay can achieve much larger EE utility while provisioning proportional fairness in EE among different terminal pairs compared to its spectral-efficient counterpart.

In this paper, we develop robust precoding for secondary users (SUs) with quality-of-service (QoS) guarantee based on imperfect channel state information (CSI) to minimize the maximum sum interference power to the primary user (PU). Optimal robust precoder is derived by transforming the original problem into a semidefinite programming (SDP) form. We then propose two suboptimal robust precoding design approaches to simplify the optimization process and to get more structural solutions. Numerical results show the proposed robust precoding methods can provide lower maximal interference powers to the PU compared to the non-robust ones and the performance gaps increase with the channel error region.

In this paper, we investigate robust resource allocation for cognitive radio networks. First, a resource allocation scheme based on stable matching is developed, which takes the preferences of both secondary users and primary users into account. To improve its robustness, we then discuss an ϵ-stable resource allocation scheme. With the help of the properties of ϵ-stable resource allocation, three edge-cutting algorithms are proposed. Numerical results show that the modified algorithms are robust to the channel state information variation.

Energy-efficient design in orthogonal frequency division multiple access (OFDMA) is becoming increasingly important, considering the booming energy consumption in wireless networks and the extensive deployment of OFDMA-based wireless infrastructures. On the other hand, wireless power transfer brings another promising approach for saving energy and prolonging lifetime in wireless networks by letting users scavenge energy from the received radio-frequency (RF) signals all over the wireless environments, including the desired information beams and undesired interference beams. In this paper, we study the downlink and uplink energy efficiency (EE) tradeoff in time-division duplexing (TDD) OFDMA with one access point (AP) and multiple users, where the users are allowed to split the received signals for decoding information and harvesting energy in the downlink, respectively. The established optimization problem turns to be an integer-mixed nonconvex program. Through relaxation and transformation, we develop a near-optimal resource allocation strategy that approaches the Pareto optimal tradeoff performance. Numerical results show that there may exist a tradeoff between the downlink and uplink EE and the AP can help improve the uplink EE by operating the downlink at the suboptimal EE regime.

Cognitive radio (CR) and energy-efficient design have emerged as two promising techniques to achieve high spectrum efficiency (SE) and energy efficiency (EE), respectively. In this paper, we study energy-efficient opportunistic spectrum access strategies for an orthogonal frequency division multiplexing (OFDM)-based CR network with multiple secondary users (SUs). Both worst EE and average EE are considered and optimized for different emphases and application scenarios. Since the original optimization issues belong to nonconvex integer combinatorial fractional program and are essentially NP-hard for an optimal solution, we use continuous and convex relaxation to modify the problems for somewhat better mathematical tractability. For the relaxed worst-EE-based spectrum access problem, we first demonstrate the joint quasiconcavity of EE on subchannel and power allocation matrices and then develop a framework to find the optimal solution based on efficient root finding and convex optimization. We also develop a low-complexity alternative for suboptimal solution. The relaxed average-EE-based spectrum access problem is still NP-hard and may have many local optima. We first transform the problem into an equivalent form and introduce a general concave envelope based branch-and-bound (B&B) approach to find the global optimal solution. We then exploit the underlying properties of the energy-efficient transmission to speed up the convergence of the B&B approach. Besides, we develop a low-complexity heuristic approach to find a suboptimal solution. Simulation results show that the energy-efficient spectrum access strategies significantly boost EE compared with the conventional spectral-efficient spectrum access ones while the low-complexity suboptimal approaches can well balance the performance and complexity.

In this paper, we investigate optimal resource allocation for device-to-device (D2D) communication underlaying cellular network in fading channels. We consider a scenario that the instantaneous channel power gain of interference links from regular cellular users (CUs) to D2D users are unknown at base station (BS) since obtaining the channel-state-information (CSI) in this case is difficult and requires high overhead. We assume that BS provides guaranteed quality-of-service (QoS) in terms of signal-to-interference-plus-noise-ratio (SINR) for CUs and outage probability for D2D pairs, respectively. Based on the assumptions, we first propose a probabilistic access control for D2D pairs to satisfy all the QoS requirements and power constraints. We then derive joint power and channel allocation to maximize the overall throughput of the CUs and admissible D2D pairs. Through simulation, we show the effectiveness of the proposed probabilistic strategy and there exists an optimal threshold of the targeted outage probability with respect to D2D access rate and overall network throughput.

In this paper, we investigate resource allocation for underlay cognitive radio (CR) networks, where the CR users coexist but do not cause unacceptable interference with licensed users. We focus on the energy efficiency (EE) performance of the system, where both throughput and energy consumption will be considered. We first formulate a sum-EE maximization problem and solve it by the Hungarian algorithm, called sum- EE-based scheme. Then, we take into account the preferences of CR users and licensed users and motivate a resource allocation scheme based on stable matching, called preference-based scheme. Numerical results demonstrate that the preference-based scheme has up to 50% performance gain on interference over the sum-EE-based scheme, while the former scheme has around 5% performance loss on EE performance compared to the latter one.

Cognitive radio (CR) technology has been developed to solve the spectrum-underutilization problem. In CR networks, the CR users have opportunities to access the licensed spectrum bands assigned to the primary users (PUs). Since the PUs have priorities to use the bands, the CR users are not allowed to generate unacceptable interference to them. In this paper, we investigate power allocation schemes for CR networks with both direct and relay-aided transmissions. We formulate an overall rate optimization problem with interference constraints to the PU and peak power constraints at each node and obtain solutions by theoretical analysis. Numerical results are provided to show the impact of the relay node and the PU locations on power allocation.

In device-to-device (D2D) communications underlaying uplink (UP) cellular networks, the channel state information (CSI) of interference links between regular cellular users (CUs) and D2D receivers is necessary to provide guaranteed quality-of-service (QoS) to D2D users. However, getting the CSI is very difficult and requires high overhead. In this paper, we propose a selected-K maximum distance ratio (MDR) feedback scheme (KMDR) to reduce feedback overhead, in which each D2D receivers only needs to feedback CSI of K CUs with the largest MDR metric. Simulation results show that up to 80% feedback can be reduced at D2D receivers by KMDR while still providing a near optimal performance. We also study the effect of side information at the D2D receivers. It is shown that it is possible to further reduce the feedback information when full side information is known at the D2D receivers.

Cognitive radio (CR) and energy-efficient design have emerged as two promising technologies to achieve high spectrum efficiency (SE) and energy efficiency (EE), respectively. In this paper, we study energy-efficient opportunistic spectrum access strategies for multiple secondary users (SUs) in an orthogonal frequency division multiplexing (OFDM)-based CR network. The worst EE is considered and optimized to achieve the max-min fairness. Since the original optimization issue belongs to nonconvex integer combinatorial fractional program and are essentially NP-hard for optimal solution, we use continuous and convex relaxation to modify the problem for somewhat better mathematical tractability. For the modified problem, we first develop a framework to find the optimal solution based on efficient root finding and convex optimization. We also develop a low-complexity alternative for suboptimal solution to further reduce complexity. Simulation results show that the energy-efficient spectrum access strategies significantly boost EE compared with the conventional spectral-efficient spectrum access ones. And the low-complexity suboptimal approaches can well balance the performance and complexity.

In this paper, we study interference-free uplink transmission for two-cell multiple-antenna cognitive radio (CR) networks. We consider a scenario that the primary user (PU) transmission is based on orthogonal frequency-division multiplexing (OFDM) and the nullspace generated by the cyclic-prefix (CP) can be used for CR transmission using Vandermonde-subspace frequency division multiplexing (VFDM). Besides interference to the PU system, we will take intra- and inter-cell interference within CR networks into consideration. A signal alignment scheme will be developed to protect the PU transmission while aligning inter-cell interference into a lower dimensional-subspace. When the interference signals are perfectly aligned, the number of interference-free symbols that can be transmitted by the whole CR network will increase with the number of CR users. Numerical results show significant performance gain can be obtained by using the proposed scheme.

In cellular networks, proximity users may communicate directly without going through the base station, which is called Device-to-device (D2D) communications and it can improve spectral efficiency. However, D2D communications may generate interference to the existing cellular networks if not designed properly. In this paper, we study a resource allocation problem to maximize the overall network throughput while guaranteeing the quality-of-service (QoS) requirements for both D2D users and regular cellular users (CUs). A three-step scheme is proposed. It first performs admission control and then allocates powers for each admissible D2D pair and its potential CU partners. Next, a maximum weight bipartite matching based scheme is developed to select a suitable CU partner for each admissible D2D pair to maximize the overall network throughput. Numerical results show that the proposed scheme can significantly improve the performance of the hybrid system in terms of D2D access rate and the overall network throughput. The performance of D2D communications depends on D2D user locations, cell radius, the numbers of active CUs and D2D pairs, and the maximum power constraint for the D2D pairs.

Cognitive radio (CR) technology has been developed to solve the spectrum-underutilization problem. In CR networks, the CR users have opportunities to access the licensed spectrum bands assigned to the primary users (PUs). Since the PUs have priorities to use the bands, the CR users are not allowed to generate unacceptable interference to them. In this paper, we investigate power allocation schemes for CR networks with both direct and relay-aided transmissions. We first formulate an overall rate optimization problem with interference constraints to the PU and peak power constraints at each node and obtain solutions by theoretical analysis. To take the fairness among CR users into consideration, we further investigate the overall rate optimization problem with an additional sum power constraint and achieve fairness between two CR users by adjusting the sum power threshold. Numerical results are provided to show the impact of the relay node and the PU locations on power allocation solutions.

Cognitive radio (CR) can successfully deal with the growing demand and the scarcity of the wireless spectrum. To increase the spectrum usage, CR technology allows secondary users to access licensed spectrum bands. Since licensed users have priorities to use the bands, the secondary users need to continuously monitor the activities of the licensed users to avoid interference and collisions. In this paper, we design sequential detection to maximize the throughput of secondary users for a given detection probability of the licensed users. We further investigate the impact of different system parameters on the performance. Numerical results are given to verify the theoretical analysis and demonstrate the performance.

We consider a base-station broadcasting a set of order-insensitive packets to a user population over packet-erasure channels. To improve efficiency we propose a relay-aided transmission scheme using instantaneously-decodable binary network coding. Our proposed scheme ensures that a coded packet can be immediately decoded at the user side without delay. Moreover, only binary operations are required in the encoding and decoding processes, which decrease the computational complex. We further analyze the performance of the resulting broadcast scheme, and show that significant improvements in transmission efficiency are obtained as compared to previously proposed ARQ and network-coding- based schemes.

We investigate efficient scheduling algorithms for a relay-aided broadcasting system using random network codes, where our objective is to maximize the transmission efficiency. The broadcast from a base-station (BS) is divided into an information phase and a redundancy phase, where the half-duplex relay assists in the redundancy phase. Time-division transmission is used over packet-erasure channels, where the erasure probabilities of the BS-to-relay and relay-to-user links are lower than the BS-to-user links. Following the information phase, each user provides feedback on the status of received packets to the BS and the relay, which in turn both generate redundancy packets for the redundancy phase. To improve efficiency, we formulate a scheduling problem for the transmissions of redundancy packets from the BS and the relay. We consider two scenarios; namely instantaneous feedback after each redundancy packet, and feedback after multiple redundancy packets. In the first case the schedule is determined using a greedy algorithm, while in the second case the schedule is determined using dynamic programming. To determine the performance with instantaneous feedback, we develop an analytic approach based on a Markov chain. Numerical results show that the transmission efficiency of the dynamic programming algorithm is close to the performance of the greedy algorithm, but requires significantly less feedback.

We investigate the design and use of systematic binary deterministic rateless (BDR) codes for information transmission over block-erasure broadcast channels. BDR codes are designed to obtain a level of maximal distance separable (MDS) properties, making these codes ideal for the considered broadcast scenario. For a certain number of encoded redundancy blocks, we derive an expression for the probability that the MDS properties are maintained. Moreover, if limited feedback is available, we extend the BDR coding protocol to further improve the system performance. Numerical results show that for a finite number of source blocks and as the number of users grows the proposed systematic BDR codes performs significantly better than LT codes. The proposed schemes with feedback have better performance than traditional ARQ schemes.

We investigate a relay-aided multi-cell broadcasting system using random network codes, where the focus is on devising efficient scheduling algorithms between relay and base stations. Two scheduling algorithms are proposed based on different feedback strategies; namely, a one-step scheduling algorithm with instantaneous feedback for each redundancy packet; and a multi-step scheduling algorithm with feedback only after multiple redundancy packets. For the latter case, dynamic programming is applied to determine optimal scheduling. Numerical results show that the transmission efficiency of the multi-step algorithm approaches that of the one-step algorithm, but requires significantly less feedback. They both significantly outperform corresponding ARQ and random scheduling approaches.

It has been shown in the literature that network coding can improve the transmission efficiency of wireless broadcasting as compared to traditional ARQ schemes. In this paper, we propose an improved network coding scheme that can asymptotically achieve the theoretical lower bound on transmission overhead for a sufficiently large number of information blocks. The proposed scheme makes use of an index allocation algorithm that distributes information blocks that have been erased during transmission into a minimum number of encoding sets, where each set represents the erased blocks to be jointly network encoded and retransmitted. Numerical results show that the proposed scheme enables higher transmission efficiencies than traditional ARQ, and previously proposed networks coding schemes for wireless broadcasting.